How to calm down the markets? The effects of COVID-19 economic policy responses on financial market uncertainty

Graphical abstract

Abstract

Financial markets during the COVID-19 pandemic are characterized by a prolonged period of increased uncertainty. In this paper, we analyse how the announcements of policy interventions and responses, to buffer short-term economic impact of the pandemic and offset financial turmoil, have affected the level of realized volatility in 23 countries. Under the augmented heterogeneous autoregressive model framework, we show that the international calming effect of COVID-19 economic policy actions originates from the US macroprudential policy announcements.

1. Introduction

The COVID-19 pandemic brought an unprecedented level of extreme volatility onto financial markets (Ali et al., 2020, Baker et al., 2020) last seen more than a decade ago during the global financial crisis. The increased volatility is attributed to the information on the number of cases and fatalities globally (Zhang et al., 2020, Ashraf, 2020b, Baig et al., 2021) and in the US (Albulescu, 2021), reproductive number (Díaz et al., 2022), the non-pharmaceutical interventions (such as school and workplace closing, cancelled public events, closed public transport, public information campaigns, restrictions on internal movement, and international travel controls) (Zaremba et al., 2020, Ashraf and Goodell, 2021, Bakry et al., 2021), investor sentiment (Huynh et al., 2021) and simply fear (Lyócsa et al., 2020).

Extreme sensitivity of financial markets due to media scrutiny and fears of disastrous economic consequences and associated negative impacts on corporate profitability pushed markets into a high-volatility, low-price state (Mamaysky, 2021). To offset possible negative effects of staled economic activities and market fears, governments, central banks and supervisory authorities around the world have started an unprecedented amount of policy interventions and responses. The combined policy reaction does not only aim to calm the financial turmoil (similar to the global financial market) but also to prevent temporary disruptions from inflicting permanent damage to the economy (Alberola et al., 2020).

Since March 2020, governments have intensified their fiscal policy actions to buffer the short-term impact of the COVID-19 economic shock. The wide array of fiscal measures to support households and firms include loans and guarantees especially to SMEs, wage and employment subsidies, tax cuts and capital injections to strategic firms. In accordance with their mandates, central banks have promptly responded to the shock by decreasing policy interest rates and by providing liquidity injections to preserve financial market functioning and ensure the transmission of the monetary policy. Monetary authorities are motivated by concerns that price dislocation can cause significant damage to central players and thereby trigger financial crises (Bevilacqua et al., 2021). Prudential authorities have supported the flow of credit to firms, households and governments by relaxing banking market constraints on the use of capital buffers and liquidity. Many actions have complimented each other creating an unprecedented complex stray of actions to decrease the uncertainty.

The aim of the paper is to empirically examine how economic policy actions have affected the financial market uncertainty around the world during the onset of the COVID-19 pandemic. We analyse the impact of the announcements of approved actions in fiscal, monetary and macroprudential policies on the realized volatility in 23 countries. Realized volatility is a common proxy of asset-market-base uncertainty in the financial literature due to free availability of high-frequency high-quality data for a large number of national stock markets1 (e.g. Andersen and Bollerslev, 1998, Andersen et al., 2001, Andersen et al., 2003, Andersen et al., 2007, Corsi, 2009, Corsi and Renó, 2012, Bollerslev et al., 2018, Cascaldi-Garcia et al., 2021). To estimate the effect of government action announcements on the overall level of volatility, we use an augmented model of the standard realized volatility heterogeneous autoregressive model (RV-HAR) of Corsi (2009).

To our knowledge, the paper is the first complex study to evaluate the impact of broad government economic actions to relieve the consequences of the COVID-19 pandemic on the stock market's volatility. Previous studies have hinted on the possibilities of policy actions to offset the economic and financial impact of COVID-19 (Topcu and Gulal, 2020, Zhang et al., 2020). The limited perspective of government support announcements on market returns might be found in Ashraf (2020a). He shows that the announcements of government income support and debt/contract relief for households programs are likely to weaken the stock markets’ negative reaction to the growth in COVID-19 confirmed cases. We also address cross-country variations in market reactions to government interventions (one day before, during the day of the announcement, and one day after) basing our analysis on 28 stock market indices in 23 countries, the spillover impact of US and EU authorities’ actions on other markets and the importance of specific interventions, namely asset purchases, credit facilities, liquidity enchantments, fiscal stimulus and macroprudential policies.

2. Related literature

Our analysis broadly relates to two strands of literature: the financial implications of the COVID pandemic and the impact of economic policies on stock market risk. The pandemic as an extraordinary event brought a lot of attention of researchers in financial economics depicting the related global uncertainty (Baker et al., 2020). Miescu and Rossi (2021) find that the COVID-induced shocks and structural uncertainty shocks are highly correlated and generate qualitatively and quantitatively comparable dynamic responses of key financial and economic indicators (under the structural vector-auto-regression framework). Wang and Wang (2021) provides evidence for sharply declining market efficiency of S&P 500 Index during February–March 2020. Vera-Valdés (2021) shows that volatility measures of 21 stock indices are characterized by growing long memory parameters following the pandemic. Kizys et al. (2021) demonstrate herding behaviour in stock markets during the pandemic while also showing that the stringency of both non-pharmaceutical and financial response mitigates investor herding behaviour by way of reducing multidimensional uncertainty. For instance, Demir and Danisman (2021) show that the COVID-related reduction in bank stock prices is mitigated by several government response policies, such as income support, debt contract relief and fiscal measures.

There are significant regional differences in the magnitude and impact of the COVID-related uncertainty. Harjoto et al. (2021) emphasize the differences in market reactions to COVID-19 cases and mortality rates in developed and emerging countries. In the investigation of 47 national stock markets, Engelhardt et al. (2021) show that the stock markets’ volatility during the COVID-19 pandemic is significantly lower in high-trust countries (as measured by World Values Survey, where respondents declare confidence in government and societal trust). Szczygielski et al. (2021) demonstrate within the ARCH/GARCH framework the resilience of Asian markets, while European, North and Latin American markets experience a weakening of the uncertainty over time (the analysis covers the pandemic period till June 2020). On the other hand, Bakry et al. (2021) show that government stringency actions increase volatility in emerging and decrease it in developed markets.

Within the second strand of literature, there is a profound evidence that macroeconomic measures and announcements about such measures have an impact on financial markets. For instance, Caporin and Poli (2017) show that macroeconomic news announcements provide useful information for volatility forecasting of S&P100 firms. Fiordelisi and Galloppo (2018) document the reactions of 12 stock exchanges around the world to monetary and fiscal policy announcements. Huang (2018) suggests that news announcements are significant determinants of disagreement and uncertainty in stock and bond futures. Moreover, Collingro and Frenkel (2020) show that financial market participants respond more strongly to monetary policy after the Global Financial Crisis and that policy communication is more effective in countries that had previously faced a severe economic downturn. Thus, developed markets might be more susceptible to policy actions than emerging markets.

While most of literature focuses on the effects of scheduled macroeconomic news announcements, policy announcements during the initial phase of pandemic were not expected. The economic understanding behind the impact of unanticipated monetary policy action on stock prices is provided in Bernanke and Kuttner (2005). The event study of Heyden and Heyden (2021) evaluates the impact of first announced fiscal and monetary policy measures on the abnormal returns of US and European firms during the initial phase of the pandemic. The findings implicate that fiscal policy potentially adds to uncertainty among investors, while responses from central banks can have a reassuring character and help to calm markets. The event study of Rahman and Al Mamun (2021) on Asia Pacific financial markets indicates that government stimulus packages calmed the markets as well. Klose and Tillmann (2021) provide an aggregate empirical analysis of the responses of European financial markets (via changes in stock and bond yields) to policy announcements in the spring of 2020. Fiscal stimulus announcements are found to be leading to higher yields, while the effect is heterogeneous depending on the severity of the pandemic. Moreover, simultaneous announcements of fiscal and monetary policy actions are particularly effective. On the other hand, Wei and Han (2021) suggest that the emergence of pandemic has significantly weakened the transmission of both conventional and unconventional monetary policies to financial markets.

The later documented heterogeneity of policy responses among countries might be a result of previous policy implementations, both conventional and unconventional. In fact, in investigation of the monetary policy reaction function of central banks during the pandemic, Yilmazkuday (2021) shows that emerging markets or countries without a zero bound on their interest rates were able to reduce interest rates, whereas advanced economies or countries with a zero bound on their interest rates were not.

Recent studies also focus on the effect of Economic Policy Uncertainty on market volatility (Tiwari et al., 2019, Antonakakis et al., 2013). Pre-COVID evidence suggests that policy responses in one country (especially the US) might affect the uncertainty in other countries. For instance, Mei et al. (2018) show that economic policy uncertainty in the US helps predict the volatility of the European stock markets. (Bekaert et al., 2013, Miranda-Agrippino and Rey, 2020) document international spillovers of Fed conventional policies. In a broader study, Chen et al. (2016) find that US unconventional monetary policy has spillover effects to both advanced and emerging economies. In this regard, the COVID-19 pandemic represents an interesting case study into the economic policy - volatility relationship both nationally and cross-border. For instance, Chinese stocks showed a higher degree of volatility sensitivity to economic policy uncertainty during COVID-19 lockdown (Yang and Yang, 2021).

In the during COVID study, Bevilacqua et al. (2021) evaluate the impact of Fed policy interventions on stock market fear in the US and internationally. They found that market liquidity, foreign exchange policies and macroprudential policies have significant impact on the risk term structure derived from daily option prices. In contrast, credit to households, businesses, and the public sector had no discernible impact on market fear in the main US stock market index. Authors believe that the market seems to either have expected those policies and priced them in or regarded them as inconsequential for asset prices. The most obvious channel of international spillovers of US policies is the Fed's US dollar swap lines, which are aimed to help funding pressures for international investors borrowing in US dollars. Aizenman et al. (2021) analyses motivations for the Fed liquidity lines and spillovers of US actions by central banks in other countries. Main US trading partners, such Germany, Japan and the UK were expanding swap line agreements in on 15 and 20 March 2020, while other countries, such as Korea, joined temporary agreements later on (as late as July 29, 2020). Pettenuzzo et al. (2021) emphasize another possible channel of monetary policy effect on recovery of stock prices via individual firm suspensions of dividends and share buybacks, bond and stock issuance. Such actions normally leading to lower returns were during pandemic associated with higher stock returns.

3. Data and methodology

We perform our analysis on 28 stock indices from 23 countries around the world. Table 1 presents the extensive list of stock indices. The analysed period ranges from January 2020 to the end of July 2021. The volatility data for all indices are obtained from the Realized Volatility Library provided by Oxford-Man Institute of Quantitative Finance.2 These data are freely available and are based on high-frequency data from the Thomson Reuters DataScope Tick History database.

Table 1.

Stock indices.

Country	Code	Index	Ticker	EM
Panel A: Europe
Belgium	BE	Bell 20 Index	BFX
Switzerland	CH	Swiss Stock Market Index	SSMI
Germany	DE	DAX	GDAXI
Denmark	DK	OMX Copenhagen 20 Index	OMXC20
Spain	ES	IBEX 35 Index	IBEX
Eurozone	EU	EURO STOXX 50	STOXX50E
Finland	FI	OMX Helsinki All Share Index	OMXHPI
France	FR	CAC 40	FCHI
United Kingdom	GB	FTSE 100	FTSE
Italy	IT	FTSE MIB	FTMIB
Netherlands	NL	AEX index	AEX
Norway	NO	Oslo Exchange All-share Index	OSEAX
Sweden	SE	OMX Stockholm All Share Index	OMXSPI
Panel B: America
Brazil	BR	BVSP BOVESPA Index	BVSP	X
Canada	CA	S&P/TSX Composite index	GSPTSE
Mexico	MX	IPC Mexico	MXX	X
United States of America	US	Dow Jones Industrial Average	DJI
United States of America	US	Nasdaq 100	IXIC
United States of America	US	Russel 2000	RUT
United States of America	US	S&P 500 Index	SPX
Panel C: Asia and Australia
China	CN	Shanghai Composite Index	SSEC	X
Hong Kong	HK	HANG SENG Index	HSI
India	IN	S&P BSE Sensex	BSESN	X
India	IN	NIFTY 50	NSEI	X
Japan	JP	Nikkei 225	N225
Republic of Korea	KR	Korea Composite Stock Price Index (KOSPI)	KS11	X
Singapore	SG	Straits Times Index	STI
Australia	AU	All Ordinaries	AORD

Note: Code column contains the 2 digit codes (ISO 3166-1) for each country. EM indicates the emerging markets by X.

From available volatility metrics, we choose the five-minute realized variance (RV), which is a standard in academic literature. Our choice is also supported by findings of Liu et al. (2015), who compared almost 400 different volatility estimators and concluded that it is challenging to beat 5-min RV significantly. Realized variance is formally defined as follows:

$${\mathrm{RV}}_t^N=\sum _{i=1}^n{r}_{t,i}^2$$ (1)

where $r_{t,i}$ represents intra-day asset returns, and $N$ is a number of returns on a given day (sampling frequency). Moreover, RV is annualized (assuming 252 trading days), and, as commonly practiced, we also apply the logarithmic transformation of this measure of variance (e.g. Andersen et al., 2001, Andersen et al., 2003, Andersen et al., 2007, Corsi and Renó, 2012, Taylor, 2017). We refer to this variation measure as realized volatility or simply volatility.

Fig. 1 depicts the realized volatility across 28 analysed stock markets. It indicates that the general trends are almost similar across all markets, including the emerging markets. At the end of February 2020, the volatility started to rise and peaked in the middle of March 2020. Volatility then gradually decreases till July 2020. Nevertheless, even one year later (till July 2021) the volatility stays above the levels common before the COVID pandemic (in February 2020).

Fig. 1.

Realized volatilities of analysed stock indices. Note: Dashed lines emphasizes the period of January 2020 to July 2020.

The next part of the dataset is composed of policy actions and interventions in response to the COVID-19 pandemic. We utilize the data from the COVID-19 Financial Response Tracker collected by the Yale Program on Financial Stability (YFPS).3 The tracker follows approved actions by central banks, fiscal authorities, and other organizations aimed at restoring financial stability. We create a dummy variable for each day called Act${}_t$. The dummy has a value of 1 if there were any action announced on a given day or 0 if there were no actions approved.

Table 2 indicates how many days included at least one action announcement for each analysed country. The actions in the dataset are divided into 13 categories. Commonly, more than one action was announced each day. Therefore, the total number of days with action in the last row is not a sum of all the rows above but the total number of days with any action announcement.

Table 2.

Number of days with policy action announcement.

Country code	BE	CH	DE	DK	ES	EU	FI	FR	GB	IT	NL	NO	SE	BR	CA	MX	US	CN	HK	IN	JP	KR	SG	AU
Panel A: Period from 2020-01-01 to 2020-07-31
Account guarantees	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0
Asset purchases	5	0	4	4	4	5	4	5	1	4	4	5	7	0	9	0	1	1	0	4	1	3	0	1
Credit facilities	4	1	4	4	9	7	5	5	5	6	4	3	7	5	2	0	7	3	2	2	4	20	3	0
Credit guarantees	1	0	3	1	3	4	1	2	0	2	1	2	4	1	0	0	0	1	0	0	0	1	0	0
Emergency liquidity	3	2	3	6	3	4	3	3	6	4	3	6	12	4	7	4	5	2	1	9	4	2	1	2
Fiscal policy	6	2	4	2	2	6	2	3	3	2	3	2	1	5	1	0	1	2	1	1	0	1	0	0
Fiscal stimulus	21	1	25	21	24	32	25	23	14	27	21	23	22	16	4	3	10	17	24	8	4	6	7	6
Interest rate change	0	0	0	1	0	0	0	0	2	0	0	3	1	5	4	4	1	6	2	1	1	2	1	2
Loan guarantees	4	1	5	5	4	10	6	7	3	6	5	8	5	1	0	0	1	4	4	2	1	5	0	0
Macroprudential policy	31	5	24	22	26	31	20	28	18	25	23	20	25	20	9	6	30	8	3	13	7	26	8	13
Market liquidity	1	0	1	2	1	1	1	1	2	1	1	1	1	0	1	1	7	0	0	2	1	1	0	0
Monetary policy	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
Swap lines	1	1	1	2	1	1	1	1	1	1	1	2	2	1	1	1	2	0	0	0	1	1	1	1
Actions (total days)	43	8	41	38	46	52	39	43	33	46	40	39	41	38	21	13	41	34	27	25	15	40	15	18
Panel B: Period from 2020-08-01 to 2021-07-31
Fiscal stimulus	3	1	10	0	7	39	0	3	17	11	10	2	7	5	14	2	7	3	16	3	2	1	3	10
Macroprudential policy	0	0	1	0	0	3	0	0	2	0	1	0	3	7	4	2	1	0	1	3	1	0	4	2
Actions (total days)	3	1	11	0	7	40	0	3	17	11	11	2	10	12	16	4	7	3	17	6	3	1	6	12

Note: The table indicates the number of days with at least action divided by 13 categories. The last row shows the total number of days with any action. It is common that during the same day more than one action were announced.

Here we also divide policy announcements into two sub-periods. The main focus is on the first half of 2020, from January 20 to the end of July 2020. This period is characterized by a large number of policy actions. Moreover, as shown in Fig. 1, it is connected to the unprecedented levels of volatility due to overall uncertainty about economic consequences of the pandemic. The second selected period (August 2020–July 2021) consists of a significantly lower number of the announcements mostly related to fiscal policies.4 Moreover, after summer of 2020, most of policy action announcements were related to the renewal or prolongation of the already existing measures, thus, can largely be considered as anticipated.

To reflect our initial observations on volatility levels and the number of policy announcements we primarily focus our analysis on the first wave of the COVID-19 pandemic, accompanied by the most uncertainty and panic of March–April 2020. During this period, all policy announcements were not anticipated by the market. After this period, the situation has settled, while market participants became aware of the broad range of implemented responses. We also add two months before and after this period to include some calmer periods into the model and get a longer time series. Therefore, the main results of our study are based on the period between January and July 2020.

To fulfill our goal, we apply the heterogeneous autoregressive model (HAR) developed by Corsi (2009). The model is easy to estimate, offers a good economic interpretation, and allows the inclusion of other regressors. Moreover, it provides an outstanding performance in- and out-of-sample (e.g. Corsi, 2009, Kourtis et al., 2016, Horpestad et al., 2018). The model is specified as follows:

$${\mathrm{RV}}_{t+1}={\beta }_0+{\beta }_1{\mathrm{RV}}_t^D+{\beta }_2{\mathrm{RV}}_t^W+{\beta }_3{\mathrm{RV}}_t^M+{ϵ}_t$$ (2)

where ${\mathrm{RV}}_t^D$ is the realized variance for the previous day, ${\mathrm{RV}}_t^W$ is the average realized variance for the last five trading days (one week), and ${\mathrm{RV}}_t^M$ is the average realized variance for the past twenty-two trading days (one month). The selection of the model components should represent investors’ behaviour with different investment horizons (Müller et al., 1997).5 The model is estimated using ordinary least squares, where standard errors are obtained via heteroskedasticity- and autocorrelation-consistent estimator of Newey and West (1994).

We add policy actions announcement to the HAR model as interactive terms. All dummy variables in the model are multiplied by (${\mathrm{RV}}_t^D$) to control for the level of volatility. Otherwise, during the period of low volatility, the dummy variables could have a much higher impact that during the period of high volatility. Therefore, our model with variables representing actions is defined as follows:

$$\begin{array}{cc}{\mathrm{RV}}_{t+1}& ={\beta }_0+{\beta }_1{\mathrm{RV}}_t^D+{\beta }_2{\mathrm{RV}}_t^W+{\beta }_3{\mathrm{RV}}_t^M\mathit{+}{\beta }_{\mathit{4}}R{V}_t^D\mathit{\times }Ac{t}_{t\mathit{+}\mathit{1}}\mathit{+}{\beta }_{\mathit{5}}R{V}_t^D\mathit{\times }Ac{t}_t\mathit{+}{\beta }_{\mathit{6}}R{V}_t^D\mathit{\times }Ac{t}_{t\mathit{-}\mathit{1}}\mathit{+}{ϵ}_t\\ & \end{array}$$ (3)

For each stock index, we include interactive terms ${\mathrm{Act}}_{t+1}$, ${\mathrm{Act}}_t$, ${\mathrm{Act}}_{t-1}$ that indicates whether there was some action announced in the corresponding country. We include the Act variable with three different time indices. ${\mathrm{Act}}_{t+1}$ represents the actions performed one day after the day for which we explain volatility. From another perspective, it explains what happen with volatility one day before the action takes place. It could indicate some leakage of information about government claims before the official announcement of the specific action. ${\mathrm{Act}}_t$ stands for actions that happened during the day of announcement. It contains the information on whether the markets reacted immediately to new information. The last component is ${\mathrm{Act}}_{t-1}$, which shows the action declared one day before the day of interest. It is suitable for predictive regression and indicates how the markets reacted the next day after the action was announced.

4. Results

4.1. Preliminary data analysis

As a first step, we investigate the statistical properties of our dependent variables represented by the volatility of stock market indices (Table 3 ). The realized volatility of selected stock indices shows comparable patterns. Mean values revolve around 5.65. RV is right-skewed, and the kurtosis of the majority of indices indicates the leptokurtic distribution and the presence of fat tails. According to the autocorrelation coefficients, RV exhibits high-persistence. The synchronization of volatilities across different stock markets is also evident in Fig. 1. The preliminary data analysis is in accordance with the stylized facts about volatility.

Table 3.

Descriptive statistics.

	Obs.	Mean	SD	Min.	Med.	Max.	Skew.	Kurt.	$\rho (1)$	$\rho (10)$
Panel A: Europe
BE-BFX	140	6.02	1.19	3.52	6.05	9.14	0.11	$-$0.18	0.87	0.54
CH-SSMI	135	5.61	1.38	3.09	5.20	9.55	0.92	0.39	0.86	0.55
DE-GDAXI	137	5.81	1.23	2.69	5.72	8.97	0.18	0.02	0.88	0.55
DK-OMXC20	132	5.63	0.91	4.03	5.38	8.75	1.10	1.14	0.84	0.48
ES-IBEX	140	6.05	1.17	3.26	6.13	9.31	0.09	$-$0.03	0.87	0.53
EU-STOXX50E	141	6.06	1.30	2.81	5.99	9.37	0.28	0.21	0.85	0.49
FI-OMXHPI	135	5.53	1.02	3.54	5.44	8.26	0.56	$-$0.01	0.85	0.48
FR-FCHI	141	5.97	1.25	2.95	5.96	9.30	0.19	0.08	0.86	0.52
GB-FTSE	140	6.11	1.26	3.65	5.95	9.73	0.41	$-$0.19	0.77	0.53
IT-FTMIB	136	5.74	1.19	2.92	5.72	8.84	0.20	$-$0.02	0.88	0.54
NL-AEX	139	5.77	1.27	2.55	5.66	9.27	0.52	0.43	0.86	0.51
NO-OSEAX	133	6.00	1.36	3.18	5.82	10.81	0.64	0.37	0.74	0.40
SE-OMXSPI	137	5.44	1.06	3.51	5.26	8.55	0.79	0.35	0.88	0.50
Panel B: America
BR-BVSP	136	5.97	1.28	3.66	5.76	9.23	0.64	$-$0.18	0.86	0.57
CA-GSPTSE	137	5.01	1.59	1.91	5.01	9.14	0.16	$-$0.54	0.89	0.66
MX-MXX	138	5.40	0.88	3.31	5.38	7.99	0.09	$-$0.43	0.64	0.36
US-DJI	136	5.81	1.45	2.80	5.75	9.23	0.33	$-$0.34	0.88	0.51
US-IXIC	141	5.76	1.29	3.10	5.66	9.62	0.55	0.15	0.84	0.53
US-RUT	141	5.82	1.38	2.79	5.92	8.96	0.04	$-$0.29	0.88	0.57
US-SPX	137	5.67	1.48	2.65	5.45	9.26	0.36	$-$0.23	0.88	0.54
Panel C: Asia and Australia
CN-SSEC	135	5.01	0.95	3.37	4.78	7.33	0.62	$-$0.51	0.77	0.21
HK-HSI	136	5.30	0.75	3.65	5.28	8.69	0.99	2.77	0.69	0.19
IN-BSESN	140	5.70	1.24	3.59	5.61	9.99	0.79	0.75	0.86	0.54
IN-NSEI	139	5.65	1.31	3.24	5.54	10.07	0.94	1.16	0.88	0.53
JP-N225	134	5.33	1.21	2.73	5.07	9.18	0.66	0.27	0.81	0.53
KR-KS11	139	5.58	0.92	4.24	5.35	8.79	1.30	1.47	0.80	0.38
SG-STI	138	5.01	0.98	2.90	4.85	8.03	0.73	0.47	0.83	0.51
AU-AORD	141	5.66	1.41	2.66	5.58	9.40	0.30	$-$0.20	0.82	0.56

Note: The table shows the descriptive statistics for realized volatility of analysed stock indices. It represents The number of observations (Obs.), mean, standard deviation (SD), minimum (Min.), median (Med.), maximum (Max.), skewness (Skew.), kurtosis (Kurt.), first order autocorrelation coefficient ($\rho (1$), and 10th order autorocorrelation coefficient ($\rho (10)$). Kurt. is a measure of unbiased kurtosis obtained using Fisher's definition of kurtosis (kurtosis of normal distribution = 0) and the result is normalized by $N-1$.

Figs. 2 and 3 show the relationship between realized volatility and the total number of actions in the US and EU. On the left y-axis is realized volatility represented by a grey line. The right y-axis shows the number of action announcements each day represented by vertical grey bars. We can notice that after the volatility rises, the countries started announcing a large number of actions. Consequently, the volatility decreases, and markets calm down. This indicates that policy actions might lower the financial market uncertainty.

Fig. 2.

Realized volatility of SPX vs. the number of action announcements in the US.

Fig. 3.

Realized volatility of STOXX50E vs. the number of action announcements in the Euro area.

4.2. Announcements and volatility in individual countries

We next test the calming effect of policy action announcements and provide evidence of their impact on market uncertainty. The results of our full model estimation for each stock index are presented in Table 4 . RV${}^D$, RV${}^W$, and RV${}^M$ are the main components of the HAR model, and they fulfill the role of control variables in our analysis. In almost all models, RV${}^D$ and RV${}^W$ are positive and highly statistically significant. These two components are the most important for explaining the realized volatility of each day. Logically, the latest information (from the previous day and week) has the highest explaining power. On the other hand, RV${}^M$ has lower importance in most models while still statistically significant most of the time. The coefficient is negative suggesting a mean reversion of volatility during the observed period.

Table 4.

The effect of economic policy announcements on volatility.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act${}_{t+1}$	Act${}_t$	Act${}_{t-1}$	$R^2$
Panel A: Europe
BE-BFX	0.932${}^d$	0.481${}^d$	0.451${}^d$	$-$0.100${}^a$	$-$0.005	0.015	0.029${}^c$	0.788
CH-SSMI	0.505${}^b$	0.441${}^d$	0.609${}^d$	$-$0.134${}^b$	$-$0.058${}^d$	$-$0.004	$-$0.017	0.775
DE-GDAXI	0.688${}^c$	0.529${}^d$	0.470${}^d$	$-$0.124${}^b$	$-$0.012	0.033${}^c$	$-$0.004	0.808
DK-OMXC20	1.220${}^d$	0.482${}^d$	0.475${}^d$	$-$0.187${}^c$	0.027${}^b$	$-$0.006	0.020	0.740
ES-IBEX	0.757${}^c$	0.453${}^d$	0.525${}^d$	$-$0.107	0.007	0.008	$-$0.001	0.790
EU-STOXX50E	0.979${}^c$	0.499${}^d$	0.424${}^d$	$-$0.098	$-$0.008	0.052${}^c$	$-$0.011	0.765
FI-OMXHPI	1.024${}^d$	0.525${}^d$	0.398${}^d$	$-$0.120${}^a$	0.009	0.011	0.018	0.748
FR-FCHI	0.810${}^c$	0.466${}^d$	0.506${}^d$	$-$0.117${}^a$	$-$0.023	0.030	0.019	0.786
GB-FTSE	0.944${}^c$	0.356${}^d$	0.558${}^d$	$-$0.075	$-$0.032	0.040${}^b$	0.019	0.668
IT-FTMIB	0.943${}^d$	0.492${}^d$	0.445${}^d$	$-$0.118${}^b$	0.014	0.041${}^c$	$-$0.010	0.802
NL-AEX	0.903${}^d$	0.456${}^d$	0.516${}^d$	$-$0.140${}^b$	$-$0.011	0.024${}^a$	0.022${}^a$	0.782
NO-OSEAX	1.035${}^c$	0.328${}^d$	0.579${}^d$	$-$0.089	$-$0.034	0.011	0.052${}^a$	0.626
SE-OMXSPI	0.867${}^d$	0.552${}^d$	0.403${}^d$	$-$0.125${}^b$	0.021	0.011	$-$0.005	0.788
Panel B: America
BR-BVSP	0.696	0.515${}^d$	0.431${}^d$	$-$0.067	$-$0.017	0.032	0.000	0.764
CA-GSPTSE	0.335${}^b$	0.514${}^d$	0.526${}^d$	$-$0.100	0.004	$-$0.006	$-$0.030	0.814
MX-MXX	0.724${}^a$	0.266${}^c$	0.506${}^d$	0.095	$-$0.003	0.015	$-$0.016	0.496
US-DJI	0.502${}^b$	0.532${}^d$	0.461${}^d$	$-$0.073	0.004	0.047${}^d$	$-$0.065${}^d$	0.807
US-IXIC	0.353	0.622${}^d$	0.285${}^c$	0.050	$-$0.021	0.056${}^d$	$-$0.074${}^d$	0.753
US-RUT	0.474${}^a$	0.620${}^d$	0.332${}^d$	$-$0.032	$-$0.002	0.058${}^d$	$-$0.057${}^d$	0.812
US-SPX	0.346	0.592${}^d$	0.388${}^d$	$-$0.030	$-$0.016	0.072${}^d$	$-$0.080${}^d$	0.812
Panel C: Asia and Australia
CN-SSEC	0.881${}^c$	0.562${}^d$	0.347${}^c$	$-$0.090	$-$0.014	0.055${}^c$	$-$0.014	0.635
HK-HSI	1.117${}^d$	0.406${}^d$	0.432${}^d$	$-$0.046	0.016	$-$0.011	$-$0.010	0.522
IN-BSESN	1.132${}^d$	0.447${}^d$	0.429${}^d$	$-$0.090	0.067${}^c$	0.000	0.009	0.791
IN-NSEI	1.211${}^d$	0.523${}^d$	0.338${}^c$	$-$0.095	0.044${}^b$	0.028	0.018	0.803
JP-N225	0.909${}^d$	0.321${}^d$	0.617${}^d$	$-$0.128${}^a$	0.080${}^c$	0.056${}^b$	0.011	0.741
KR-KS11	1.100${}^d$	0.386${}^d$	0.544${}^d$	$-$0.140	$-$0.002	0.045${}^d$	$-$0.002	0.704
SG-STI	0.485${}^b$	0.284${}^d$	0.752${}^d$	$-$0.129${}^b$	$-$0.018	0.035${}^a$	$-$0.045${}^b$	0.752
AU-AORD	0.677${}^a$	0.316${}^c$	0.641${}^d$	$-$0.081	0.042${}^b$	0.019	$-$0.037${}^a$	0.729

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Analysing the coefficients of interactive terms around the day of action announcements, our results confirm that the volatility increases during the day of the action announcement (Act${}_t$). The coefficients are positive and significant in almost half of the analysed stock indices with the highest reaction for SPX (0.072). Logically, volatility rises during the day of the announcement because all market participants update their expectations according to the new information.

The more important effect is whether the volatility goes down back to the previous levels, which means that the situation calms down quickly. The calming effect on the markets is present mainly in the United States. All stock indices in the US exhibit lower volatility than during the announcement day. Moreover, the magnitude of the coefficients is higher than during the previous day. Therefore, the volatility decreases more than it increases the day before. We only observe similar effect in Singapore and Australia, where the volatility declines one day after the actions were announced.

The opposite effect of decreasing volatility after the action announcement is found in three European countries, namely Belgium, Netherlands, and Norway, which have positive and statistically significant coefficients of ${\mathrm{Act}}_{t-1}$. It indicates that stock indices in these countries react negatively to policy actions because their volatility increases the next day after the action.

Some significant results for the variable ${\mathrm{Act}}_{t+1}$ are found in several countries. The highest positive coefficients are observed for India, Japan, and Australia, where volatility tends to increase before the action announcement. This might indicate the significance of public discussion on possible policy actions, information leakage before the official announcement or overall increased policy uncertainty of market participants. Surprisingly, the volatility in India and Japan is not decreasing even after the announcement day. The other interesting anomaly is Switzerland, where volatility decreases before the announcement without the subsequent increase in uncertainty on or after the announcement day.

4.3. Fiscal stimulus and macropudential policy announcements

As a next step, we investigate which types of actions are the most important (also the form of robustness check for the previous results). According to Table 2, most actions belong to two categories, Fiscal Stimulus and Macroprudential Policy. Therefore, we include actions from only one of these categories into our model estimation. The results are shown in Tables 5 and 6 .

Table 5.

The effect of fiscal stimulus announcements on volatility.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act${}_{t+1}$	Act${}_t$	Act${}_{t-1}$	$R^2$
Panel A: Europe
BE-BFX	0.658${}^d$	0.511${}^d$	0.462${}^d$	$-$0.084	$-$0.020	0.015	0.016	0.786
CH-SSMI	0.703${}^c$	0.456${}^d$	0.562${}^d$	$-$0.143${}^a$	$-$0.089${}^d$	0.077${}^c$	0.036${}^a$	0.774
DE-GDAXI	0.506${}^b$	0.542${}^d$	0.481${}^d$	$-$0.108${}^a$	$-$0.025${}^a$	0.024${}^b$	$-$0.014	0.807
DK-OMXC20	0.979${}^d$	0.484${}^d$	0.468${}^d$	$-$0.133${}^c$	$-$0.000	0.022${}^a$	0.020	0.737
ES-IBEX	0.710${}^d$	0.456${}^d$	0.522${}^d$	$-$0.099	0.011	$-$0.002	0.015	0.790
EU-STOXX50E	0.689${}^c$	0.504${}^d$	0.466${}^d$	$-$0.083	$-$0.009	0.0230	$-$0.011	0.759
FI-OMXHPI	0.755${}^d$	0.532${}^d$	0.413${}^d$	$-$0.083	0.013	$-$0.006	0.006	0.746
FR-FCHI	0.673${}^c$	0.461${}^d$	0.522${}^d$	$-$0.099	$-$0.014	0.000	0.036${}^a$	0.785
GB-FTSE	0.853${}^c$	0.336${}^d$	0.590${}^d$	$-$0.070	$-$0.024	0.051${}^b$	0.017	0.667
IT-FTMIB	0.661${}^d$	0.518${}^d$	0.462${}^d$	$-$0.098	$-$0.011	0.033${}^b$	$-$0.006	0.798
NL-AEX	0.757${}^d$	0.459${}^d$	0.514${}^d$	$-$0.110${}^a$	$-$0.006	0.006	0.040${}^b$	0.783
NO-OSEAX	0.782${}^c$	0.326${}^d$	0.598${}^d$	$-$0.053	$-$0.017	$-$0.019	0.030	0.619
SE-OMXSPI	0.653${}^d$	0.558${}^d$	0.422${}^d$	$-$0.103${}^a$	0.005	$-$0.004	0.012	0.785
Panel B: America
BR-BVSP	0.723${}^b$	0.512${}^d$	0.428${}^d$	$-$0.065	$-$0.007	0.014	0.028	0.763
CA-GSPTSE	0.360${}^b$	0.536${}^d$	0.480${}^d$	$-$0.085	$-$0.080${}^d$	0.060${}^c$	$-$0.036${}^c$	0.817
MX-MXX	0.780${}^b$	0.270${}^c$	0.492${}^d$	0.093	$-$0.021	0.033	0.043	0.498
US-DJI	0.564${}^c$	0.511${}^d$	0.473${}^d$	$-$0.077	0.027${}^a$	$-$0.021	$-$0.044${}^a$	0.795
US-IXIC	0.590${}^c$	0.576${}^d$	0.301${}^c$	0.030	$-$0.009	0.004	$-$0.066${}^c$	0.735
US-RUT	0.476${}^b$	0.592${}^d$	0.360${}^d$	$-$0.031	0.035${}^b$	$-$0.018	$-$0.041	0.801
US-SPX	0.486${}^c$	0.535${}^d$	0.432${}^d$	$-$0.049	0.011	$-$0.005	$-$0.046${}^a$	0.792
Panel C: Asia and Australia
CN-SSEC	0.798${}^c$	0.606${}^d$	0.282${}^c$	$-$0.042	$-$0.045${}^b$	0.072${}^c$	$-$0.039${}^b$	0.644
HK-HSI	1.121${}^d$	0.413${}^d$	0.423${}^d$	$-$0.044	0.002	0.006	$-$0.020	0.522
IN-BSESN	0.670${}^d$	0.498${}^d$	0.487${}^d$	$-$0.103	0.080${}^c$	$-$0.049${}^d$	0.008	0.789
IN-NSEI	0.657${}^d$	0.603${}^d$	0.374${}^d$	$-$0.094${}^a$	0.068${}^b$	$-$0.048${}^c$	0.022	0.802
JP-N225	0.629${}^c$	0.358${}^d$	0.647${}^d$	$-$0.129	0.126${}^d$	0.124${}^d$	$-$0.081${}^c$	0.746
KR-KS11	0.546${}^a$	0.438${}^d$	0.518${}^d$	$-$0.050	$-$0.065${}^d$	0.033	$-$0.079${}^d$	0.705
SG-STI	0.647${}^c$	0.273${}^c$	0.743${}^d$	$-$0.147${}^b$	0.000	0.044${}^b$	$-$0.021	0.747
AU-AORD	0.551${}^a$	0.302${}^c$	0.684${}^d$	$-$0.082	$-$0.010	0.002	$-$0.032	0.722

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Table 6.

The effect of macroprudential policy announcements on volatility.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act${}_{t+1}$	Act${}_t$	Act${}_{t-1}$	$R_2$
Panel A: Europe
BE-BFX	0.983${}^d$	0.473${}^d$	0.423${}^d$	$-$0.073	0.012	$-$0.002	0.044${}^c$	0.792
CH-SSMI	0.511${}^b$	0.441${}^d$	0.631${}^d$	$-$0.157${}^b$	$-$0.064${}^d$	$-$0.035	0.007	0.776
DE-GDAXI	0.828${}^c$	0.499${}^d$	0.473${}^d$	$-$0.125${}^b$	0.024${}^a$	$-$0.002	0.022${}^a$	0.807
DK-OMXC20	0.914${}^d$	0.484${}^d$	0.495${}^d$	$-$0.145${}^c$	0.003	0.003	0.015	0.732
ES-IBEX	0.650${}^c$	0.462${}^d$	0.530${}^d$	$-$0.099	0.014	$-$0.020	0.008	0.791
EU-STOXX50E	0.950${}^c$	0.487${}^d$	0.436${}^d$	$-$0.088	0.006	0.025	0.003	0.760
FI-OMXHPI	1.032${}^d$	0.517${}^d$	0.399${}^d$	$-$0.112${}^b$	0.029${}^b$	0.012	0.018	0.750
FR-FCHI	0.914${}^c$	0.434${}^d$	0.506${}^d$	$-$0.104	0.017	0.011	0.016	0.783
GB-FTSE	0.920${}^c$	0.334${}^d$	0.562${}^d$	$-$0.051	$-$0.017	0.027	0.030	0.664
IT-FTMIB	0.834${}^d$	0.496${}^d$	0.460${}^d$	$-$0.110${}^a$	0.010	0.023	0.006	0.796
NL-AEX	0.899${}^d$	0.454${}^d$	0.516${}^d$	$-$0.135${}^b$	0.011	0.006	0.027${}^a$	0.780
NO-OSEAX	1.350${}^d$	0.323${}^d$	0.535${}^d$	$-$0.101	0.044${}^b$	0.012	0.043	0.623
SE-OMXSPI	1.091${}^d$	0.532${}^d$	0.395${}^c$	$-$0.142${}^c$	0.055${}^d$	0.012	$-$0.002	0.797
Panel B: America
BR-BVSP	0.470	0.526${}^d$	0.440${}^d$	$-$0.041	$-$0.018	0.030	$-$0.027	0.767
CA-GSPTSE	0.412${}^c$	0.527${}^d$	0.473${}^d$	$-$0.081	0.035	$-$0.012	$-$0.029	0.814
MX-MXX	0.807${}^b$	0.253${}^c$	0.515${}^d$	0.080	0.055${}^b$	$-$0.017	0.023	0.501
US-DJI	0.354	0.529${}^d$	0.472${}^d$	$-$0.049	$-$0.025	0.052${}^d$	$-$0.069${}^d$	0.803
US-IXIC	0.341	0.598${}^d$	0.304${}^c$	0.056	$-$0.018	0.065${}^c$	$-$0.094${}^d$	0.752
US-RUT	0.455${}^a$	0.598${}^d$	0.352${}^d$	$-$0.026	0.005	0.052${}^d$	$-$0.063${}^d$	0.808
US-SPX	0.290	0.563${}^d$	0.413${}^d$	$-$0.014	$-$0.020	0.070${}^d$	$-$0.091${}^d$	0.807
Panel C: Asia and Australia
CN-SSEC	0.856${}^c$	0.571${}^d$	0.296${}^c$	$-$0.027	$-$0.049${}^c$	$-$0.032	$-$0.055${}^b$	0.629
HK-HSI	1.250${}^d$	0.492${}^d$	0.317${}^c$	$-$0.047	0.193${}^a$	$-$0.087${}^d$	0.062${}^b$	0.588
IN-BSESN	0.446${}^a$	0.459${}^d$	0.557${}^d$	$-$0.087	0.001	0.002	$-$0.053${}^c$	0.783
IN-NSEI	0.501${}^c$	0.578${}^d$	0.414${}^d$	$-$0.075	0.005	0.002	$-$0.039${}^a$	0.797
JP-N225	0.523${}^b$	0.352${}^d$	0.632${}^d$	$-$0.079	0.005	$-$0.019	$-$0.050	0.722
KR-KS11	1.097${}^d$	0.394${}^d$	0.535${}^d$	$-$0.137${}^a$	$-$0.006	0.043${}^c$	0.016	0.701
SG-STI	0.492${}^b$	0.288${}^d$	0.736${}^d$	$-$0.119	$-$0.011	0.051${}^b$	$-$0.075${}^d$	0.757
AU-AORD	0.818${}^b$	0.325${}^c$	0.604${}^d$	$-$0.082	0.066${}^c$	0.013	$-$0.021	0.730

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

In biggest European stock markets, the fiscal stimulus mostly tends to increase the volatility during the announcement day, while the macroprudential policy effect is not significant. After the announcement, the volatility increase is observed in both cases with limited statistical significance. One day before the announcement, the fiscal stimulus actions did not affect or slightly decrease the volatility in Switzerland and Germany. In comparison, the macroprudential policy actions mostly increase volatility.

In America, the picture is exactly the opposite of Europe. Most of the statistically significant coefficients are negative. In the case of fiscal stimulus actions, the volatility slightly rises one day before the action for indices DJI and RUT. After the action, the volatility declines with a higher magnitude for DJI, ISIC, and SPX. For RUT, the coefficient is also negative but is not statistically significant. The macroprudential policy provides a different stronger effect. All coefficients are positive and statistically significant on the day of the action, while the following day all coefficients are also significant but negative with a higher magnitude. It indicates that the macroprudential policy actions were able to calm down the markets in the US. The Canadian stock market seems to be more sensitive to fiscal stimulus and provides no statistically significant reaction to macroprudential policy announcements.

The analysis of Asian and Australian stock markets provide mixed results. It seems that the fiscal stimulus announcement has a higher effect one day before and on the day of the action. On the other hand, macroprudential policy shows the most significant impact one day after the action with a predominantly calming effect.

In general, our results suggest that the effects of policy action announcements are stronger for developed countries than emerging markets, where the magnitude of policy effects and its statistical significance tend to be lower.

4.4. Spillover effects of US and Euro area policy action announcements

Given the previous evidence on the impact of the US monetary policy on other markets and similar development of volatility in the studied markets (Fig. 1), we also test how policy action announcements in the US and Euro area affect uncertainty levels in other markets. To achieve this goal, we include additional regressor US Act${}_{t-1}$ or EU Act${}_{t-1}$ into the baseline model specification. These variables represent the adoption of some action in the US or EU in the previous day. We include only lagged action variable to keep the number of regressors reasonable and emphasize the effect of causality.

Table 7 reports the results for the effect of action announcements in the United States on other countries. Additional variable US Act${}_{t-1}$ is highly statistically significant and negative in most European countries. It indicates a strong spillover effect of the US policy announcements on European stock markets. Similar to the calming effect of action announcements in the US, the uncertainty in Europe decreases. Same reaction is observed in Canada. On the contrary, Asian and Australian markets are not affected by the United States’ actions.

Table 7.

The effect of US action announcements on the volatility in other countries.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act${}_{t+1}$	Act${}_t$	Act${}_{t-1}$	US Act${}_{t-1}$	$R^2$
Panel A: Europe
BE-BFX	0.754${}^c$	0.491${}^d$	0.440${}^d$	$-$0.063	$-$0.005	0.021	0.043${}^d$	$-$0.042${}^c$	0.795
CH-SSMI	0.404	0.440${}^d$	0.614${}^d$	$-$0.115${}^a$	$-$0.053${}^d$	0.002	$-$0.014	$-$0.017	0.776
DE-GDAXI	0.453	0.544${}^d$	0.460${}^d$	$-$0.082	$-$0.008	0.049${}^d$	0.012	$-$0.057${}^d$	0.818
DK-OMXC20	0.802${}^d$	0.462${}^d$	0.505${}^d$	$-$0.114${}^a$	0.024${}^a$	0.008	0.028${}^b$	$-$0.052${}^d$	0.752
ES-IBEX	0.632${}^c$	0.450${}^d$	0.530${}^d$	$-$0.085	0.009	0.017	0.014	$-$0.040${}^c$	0.796
EU-STOXX50E	0.827${}^c$	0.488${}^d$	0.431${}^d$	$-$0.063	$-$0.010	0.067${}^d$	0.010	$-$0.059${}^d$	0.776
FI-OMXHPI	0.792${}^c$	0.546${}^d$	0.383${}^d$	$-$0.076	0.012	0.023${}^a$	0.030${}^b$	$-$0.047${}^d$	0.757
FR-FCHI	0.587${}^b$	0.488${}^d$	0.489${}^d$	$-$0.077	$-$0.022	0.040${}^a$	0.041${}^b$	$-$0.057${}^d$	0.796
GB-FTSE	0.947${}^c$	0.356${}^d$	0.558${}^d$	$-$0.075	$-$0.032	0.040${}^c$	0.019	0.001	0.668
IT-FTMIB	0.636${}^c$	0.545${}^d$	0.387${}^d$	$-$0.046	0.011	0.049${}^d$	0.004	$-$0.055${}^d$	0.812
NL-AEX	0.570${}^b$	0.454${}^d$	0.531${}^d$	$-$0.081	$-$0.012	0.031${}^b$	0.041${}^c$	$-$0.061${}^d$	0.794
NO-OSEAX	0.860${}^b$	0.336${}^d$	0.586${}^d$	$-$0.068	$-$0.035	0.021	0.057${}^a$	$-$0.032	0.629
SE-OMXSPI	0.613${}^b$	0.579${}^d$	0.373${}^c$	$-$0.065	0.020	0.018	0.009	$-$0.045${}^c$	0.795
Panel B: America
BR-BVSP	0.667	0.519${}^d$	0.429${}^d$	$-$0.063	$-$0.018	0.033	0.002	$-$0.005	0.764
CA-GSPTSE	0.154	0.567${}^d$	0.449${}^d$	$-$0.024	0.009	0.021	$-$0.016	$-$0.066${}^d$	0.821
MX-MXX	0.463	0.277${}^d$	0.532${}^d$	0.113	0.007	0.018	$-$0.010	$-$0.027	0.500
Panel C: Asia and Australia
CN-SSEC	0.725${}^c$	0.556${}^d$	0.321${}^c$	$-$0.015	$-$0.017	0.056${}^c$	$-$0.014	$-$0.032	0.640
HK-HSI	1.367${}^d$	0.425${}^d$	0.391${}^d$	$-$0.078	0.014	$-$0.017	$-$0.014	0.029	0.529
IN-BSESN	1.192${}^c$	0.447${}^d$	0.422${}^d$	$-$0.096	0.066${}^c$	$-$0.004	0.009	0.011	0.792
IN-NSEI	1.270${}^d$	0.530${}^d$	0.325${}^c$	$-$0.102	0.043${}^b$	0.022	0.017	0.014	0.804
JP-N225	0.92${}^d$	0.321${}^d$	0.616${}^d$	$-$0.130${}^a$	0.080${}^c$	0.056${}^b$	0.011	0.002	0.741
KR-KS11	0.953${}^c$	0.394${}^d$	0.546${}^d$	$-$0.121	0.001	0.047${}^d$	$-$0.000	$-$0.016	0.705
SG-STI	0.689${}^c$	0.278${}^c$	0.729${}^d$	$-$0.150${}^b$	$-$0.019	0.037${}^a$	$-$0.055${}^b$	0.029	0.755
AU-AORD	0.566	0.312${}^c$	0.653${}^d$	$-$0.064	0.044${}^a$	0.022	$-$0.034	$-$0.021	0.730

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. US Act.${}_{t-1}$ represents the actions from the US from the previous day, also multiplied by (${\mathit{RV}}_t^D$). $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Table 8 presents the spillover effect that stems from the action announcement in the Euro area. The EU action announcements do not influence the volatility in the United States and three other American markets. The increase of uncertainty after EU policy announcements is only found for the Swiss and two Asian stock markets (Indian NSEI and Singapore). Overall, the influence of the EU actions is minimal and much weaker than the US.

Table 8.

The effect of Euro area action announcements on the volatility in other countries.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act${}_{t+1}$	Act${}_t$	Act${}_{t-1}$	EU Act${}_{t-1}$	$R^2$
Panel A: Europe
CH-SSMI	0.851${}^d$	0.450${}^d$	0.565${}^d$	$-$0.179${}^c$	$-$0.062${}^d$	0.005	$-$0.030	0.055${}^c$	0.784
GB-FTSE	1.001${}^c$	0.356${}^d$	0.554${}^d$	$-$0.082	$-$0.034${}^a$	0.036${}^a$	0.016	0.015	0.669
Panel B: America
BR-BVSP	0.640	0.535${}^d$	0.417${}^d$	$-$0.061	$-$0.015	0.038${}^a$	0.004	$-$0.020	0.765
CA-GSPTSE	0.395${}^b$	0.501${}^d$	0.548${}^d$	$-$0.128	$-$0.003	$-$0.006	$-$0.041	0.030	0.816
MX-MXX	0.590	0.278${}^d$	0.511${}^d$	0.106	0.001	0.014	$-$0.012	$-$0.016	0.497
US-DJI	0.542${}^b$	0.535${}^d$	0.449${}^d$	$-$0.075	0.003	0.044${}^d$	$-$0.072${}^d$	0.022	0.808
US-IXIC	0.377	0.618${}^d$	0.285${}^c$	0.047	$-$0.022	0.054${}^d$	$-$0.078${}^d$	0.013	0.753
US-RUT	0.502${}^b$	0.617${}^d$	0.328${}^d$	$-$0.032	$-$0.003	0.056${}^d$	$-$0.063${}^c$	0.016	0.813
US-SPX	0.375	0.591${}^d$	0.384${}^d$	$-$0.033	$-$0.017	0.069${}^d$	$-$0.085${}^d$	0.016	0.813
Panel C: Asia and Australia
CN-SSEC	0.878${}^c$	0.561${}^d$	0.348${}^c$	$-$0.089	$-$0.014	0.055${}^c$	$-$0.014	$-$0.001	0.635
HK-HSI	1.305${}^d$	0.412${}^d$	0.391${}^d$	$-$0.053	0.008	$-$0.015	$-$0.015	0.029	0.529
IN-BSESN	1.177${}^d$	0.437${}^d$	0.430${}^d$	$-$0.092	0.066${}^c$	$-$0.004	0.006	0.015	0.792
IN-NSEI	1.285${}^d$	0.512${}^d$	0.334${}^c$	$-$0.098	0.043${}^b$	0.021	0.012	0.026${}^a$	0.805
JP-N225	1.011${}^d$	0.325${}^d$	0.597${}^d$	$-$0.138${}^a$	0.077${}^c$	0.047	0.006	0.025	0.743
KR-KS11	1.247${}^d$	0.383${}^d$	0.528${}^d$	$-$0.153${}^a$	$-$0.004	0.042${}^d$	$-$0.006	0.024	0.707
SG-STI	0.739${}^d$	0.281${}^c$	0.694${}^d$	$-$0.132${}^b$	$-$0.008	0.025	$-$0.052${}^c$	0.044${}^d$	0.761
AU-AORD	0.652${}^a$	0.317${}^c$	0.643${}^d$	$-$0.079	0.043${}^b$	0.020	$-$0.036${}^a$	$-$0.006	0.729

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. EU Act.${}_{t-1}$ represents the actions from the EU from the previous day, also multiplied by (${\mathit{RV}}_t^D$). $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

4.5. Results for the period of August 2020 to July 2021

To complement our main analysis, we also investigate the subsequent one-year period from the beginning of August 2020 till the end of July 2021. All tables with results are available in Appendix A. In general, the power of economic policy announcements to affect market uncertainty has decreased significantly and evident only for few countries. The diminishing effect is the most obvious in the United States, where compared to previous results (Table 4) overall announcements lost their power to influence stock market volatility.

In the separate analysis of fiscal and macroprudential policy actions, many countries did not announce any new policies during the selected time period (depicted by zeros in regression coefficients for action dummy variables). For fiscal policy, the results are mixed and generally weaker than during the first half of 2020, especially for the United States. Results on macroprudential policy announcements confirm our previous findings of the calming effect in the US and Asian countries.

Nevertheless, we also observe a difference in the results of spillover effects. The United States lose its power to calm European markets, as the US news is not statistically significant for almost all cases. Surprisingly, EU actions (mostly related to fiscal stimulus) seem to gain significance in the US stock market and increase volatility in blue-chip indices of S&P500 and NASDAQ perhaps due to global nature of business of included companies.

5. Conclusion

In this paper, we study market volatility during the COVID-19 pandemic and its reaction to economic policy announcements in 23 countries. Policy announcements crucially affected the financial market uncertainty in the initial phase of the pandemic. In at least 13 countries, the COVID-19 economic policy announcements increase financial market uncertainty at the day of the announcement. The local calming effect of policy announcement is strongly evident only for the US, when the decrease in market volatility the day after the announcement is higher than the increase in volatility at the announcement day. The market uncertainly in the US reacts mostly to the macroprudential policy announcements (with no statistically significant reaction to fiscal stimulus actions). Some local reactions to fiscal stimulus actions are only observed in Europe and Canada.

International dimension of calming effects due to policy responses is evident for the announcements of US macroprudential policy actions, for which we found spillovers to European and Canadian markets during the initial phase of the pandemic. Thus, our analysis of COVID-19 policy responses supports previous findings on the effects of US policy actions on volatility in other countries, especially developed economies, during turbulent times. The channel of international spillover of US actions in 2020 perhaps lies within the Fed's program of US dollar swap lines, as suggested by Bevilacqua et al. (2021).

We find no effect of policy actions on uncertainty in emerging markets, where policy action responses have been much more limited compared to developed markets. In fact, according to the Bank for International Settlements (Alberola et al., 2020), the size of budgetary measures in emerging economies represents only one fifth of that of advanced economies in per GDP terms, and the divergence in the use of non-budgetary measures is even larger.

Our findings also provide explanation for decreasing and even negative volatility risk premiums in the US in March-April 2020. Government actions in the US decreased the uncertainty, reflected in declining prices of insurance against unexpected volatility (as shown in Cheng, 2020), which subsequently spread to other developed markets. While our findings largely compliments previous evidence, we additionally show far greater importance of the macroprudential policy to calm financial markets.

Authors’ contribution

Oleg Deev: conceptualization, methodology, writing – original draft, writing – review & editing, project administration. Tomáš Plíhal: conceptualization, methodology, software, writing – original draft, writing – review & editing, visualization.

Appendix A. Analysis of the period from August 2020 to June 2021

Table A.9.

The effect of economic policy announcements on volatility.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act.${}_{t+1}$	Act.${}_t$	Act.${}_{t-1}$	$R_2$
Panel A: Europe
BE-BFX	0.554${}^c$	0.356${}^d$	0.339${}^d$	0.192${}^c$	$-$0.056${}^d$	$-$0.055${}^b$	0.159${}^c$	0.540
CH-SSMI	0.718${}^c$	0.443${}^d$	0.295${}^d$	0.098	$-$0.100${}^d$	0.261${}^d$	$-$0.052${}^d$	0.492
DE-GDAXI	1.218${}^d$	0.340${}^d$	0.345${}^d$	0.058	0.026	0.031	0.035	0.400
DK-OMXC20	2.177${}^d$	0.309${}^d$	0.357${}^d$	$-$0.088	$-$0.004	0.043	$-$0.106${}^d$	0.250
ES-IBEX	0.817${}^c$	0.431${}^d$	0.265${}^d$	0.151${}^a$	0.021	$-$0.033	$-$0.009	0.455
EU-STOXX50E	1.018${}^c$	0.257${}^d$	0.446${}^d$	0.083	0.052${}^c$	$-$0.011	$-$0.004	0.419
FI-OMXHPI	1.601${}^d$	0.328${}^d$	0.353${}^d$	$-$0.030	0.000	0.000	0.000	0.286
FR-FCHI	0.663${}^c$	0.413${}^d$	0.243${}^c$	0.203${}^c$	0.247${}^d$	$-$0.042${}^c$	0.024${}^b$	0.528
GB-FTSE	1.326${}^c$	0.256${}^d$	0.324${}^d$	0.158	0.011	$-$0.004	0.020	0.268
IT-FTMIB	0.708${}^b$	0.256${}^d$	0.435${}^d$	0.158	0.005	$-$0.010	0.010	0.407
NL-AEX	1.076${}^c$	0.259${}^d$	0.456${}^d$	0.064	$-$0.042	$-$0.040	0.065${}^a$	0.333
NO-OSEAX	2.557${}^d$	0.201${}^d$	0.316${}^d$	$-$0.031	$-$0.078${}^d$	0.072${}^d$	$-$0.052${}^d$	0.131
SE-OMXSPI	2.035${}^d$	0.290${}^d$	0.421${}^d$	$-$0.158	0.014	0.006	0.038	0.273
Panel B: America
BR-BVSP	0.524	0.453${}^d$	0.223${}^c$	0.223${}^c$	$-$0.016	$-$0.002	0.018	0.522
CA-GSPTSE	1.014${}^d$	0.425${}^d$	0.198${}^b$	0.126	$-$0.012	0.017	0.052${}^d$	0.367
MX-MXX	1.116${}^b$	0.067	0.428${}^d$	0.277${}^b$	0.034	0.052	$-$0.040${}^d$	0.198
US-DJI	0.973${}^c$	0.379${}^d$	0.271${}^c$	0.142	$-$0.013	0.028	$-$0.018	0.356
US-IXIC	1.184${}^d$	0.342${}^d$	0.384${}^d$	0.039	$-$0.009	0.027	$-$0.008	0.368
US-RUT	1.511${}^d$	0.369${}^d$	0.304${}^c$	0.035	$-$0.008	0.043	$-$0.023	0.318
US-SPX	0.943${}^c$	0.426${}^d$	0.299${}^d$	0.065	$-$0.019	0.042	0.004	0.425
Panel C: Asia and Australia
CN-SSEC	1.315${}^d$	0.309${}^d$	0.257${}^b$	0.166	$-$0.092	0.089${}^b$	$-$0.070	0.278
HK-HSI	0.880${}^c$	0.333${}^d$	0.355${}^d$	0.138	$-$0.002	0.033${}^a$	$-$0.012	0.379
IN-BSESN	0.716${}^b$	0.299${}^d$	0.219	0.329${}^b$	0.014	0.013	0.026	0.321
IN-NSEI	0.715${}^b$	0.357${}^d$	0.160	0.328${}^c$	0.018	0.021	0.025	0.340
JP-N225	1.468${}^d$	0.276${}^d$	0.295${}^c$	0.111	$-$0.096${}^d$	$-$0.057	$-$0.041	0.233
KR-KS11	0.615	0.227${}^d$	0.475${}^d$	0.174	$-$0.121${}^d$	$-$0.057${}^b$	$-$0.104${}^d$	0.438
SG-STI	1.091${}^d$	0.171${}^c$	0.438${}^d$	0.138	0.044	0.028${}^c$	0.013	0.326
AU-AORD	1.629${}^d$	0.244${}^d$	0.212${}^a$	0.171	0.063${}^c$	$-$0.012	0.049	0.221

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Table A.10.

The effect of fiscal stimulus announcements on volatility.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act.${}_{t+1}$	Act.${}_t$	Act.${}_{t-1}$	R${}_2$
Panel A: Europe
BE-BFX	0.554${}^c$	0.356${}^d$	0.339${}^d$	0.192${}^c$	$-$0.056${}^d$	$-$0.055${}^b$	0.159${}^c$	0.540
CH-SSMI	0.718${}^c$	0.443${}^d$	0.295${}^d$	0.098	$-$0.100${}^d$	0.261${}^d$	$-$0.052${}^d$	0.492
DE-GDAXI	1.126${}^d$	0.355${}^d$	0.332${}^d$	0.075	$-$0.001	0.021	0.053${}^b$	0.400
DK-OMXC20	2.176${}^d$	0.305${}^d$	0.352${}^d$	$-$0.078	0.000	0.000	0.000	0.237
ES-IBEX	0.817${}^c$	0.431${}^d$	0.265${}^d$	0.151${}^a$	0.021	$-$0.033	$-$0.009	0.455
EU-STOXX50E	0.914${}^c$	0.265${}^d$	0.439${}^d$	0.108	0.033	$-$0.012	$-$0.002	0.409
FI-OMXHPI	1.601${}^d$	0.328${}^d$	0.353${}^d$	$-$0.030	0.000	0.000	0.000	0.286
FR-FCHI	0.663${}^c$	0.413${}^d$	0.243${}^c$	0.203${}^c$	0.247${}^d$	$-$0.042${}^c$	0.024${}^b$	0.528
GB-FTSE	1.196${}^c$	0.264${}^d$	0.328${}^c$	0.174	0.012	$-$0.034	0.031	0.273
IT-FTMIB	0.653${}^b$	0.254${}^d$	0.440${}^d$	0.168${}^a$	$-$0.008	$-$0.024	$-$0.002	0.407
NL-AEX	1.042${}^c$	0.256${}^d$	0.464${}^d$	0.067	$-$0.058${}^c$	$-$0.029	0.044	0.331
NO-OSEAX	2.571${}^d$	0.195${}^d$	0.319${}^d$	$-$0.032	$-$0.031	$-$0.074	0.037	0.126
SE-OMXSPI	2.030${}^d$	0.289${}^d$	0.420${}^d$	$-$0.156	0.012	0.007	0.065${}^a$	0.277
Panel B: America
BR-BVSP	0.531${}^a$	0.445${}^d$	0.234${}^c$	0.219${}^c$	0.023	0.030	$-$0.044${}^b$	0.525
CA-GSPTSE	0.912${}^d$	0.434${}^d$	0.202${}^b$	0.141	$-$0.024	0.001	0.032	0.361
MX-MXX	1.100${}^b$	0.069	0.427${}^d$	0.280${}^b$	0.102${}^d$	0.002	$-$0.033${}^c$	0.197
US-DJI	1.050${}^c$	0.373${}^d$	0.284${}^d$	0.118	$-$0.023	0.084	0.011	0.360
US-IXIC	1.198${}^d$	0.340${}^d$	0.387${}^d$	0.035	$-$0.008	0.043	$-$0.002	0.367
US-RUT	1.514${}^d$	0.370${}^d$	0.303${}^d$	0.034	$-$0.018	0.068	$-$0.021	0.317
US-SPX	0.963${}^c$	0.426${}^d$	0.304${}^d$	0.056	$-$0.043	0.106${}^a$	0.027	0.430
Panel C: Asia and Australia
CN-SSEC	1.638${}^d$	0.266${}^c$	0.324${}^c$	0.075	0.028	0.072${}^a$	0.013	0.250
HK-HSI	0.911${}^c$	0.338${}^d$	0.353${}^d$	0.130	$-$0.022	0.030	$-$0.007	0.379
IN-BSESN	0.723${}^b$	0.275${}^d$	0.218${}^b$	0.349${}^c$	0.111${}^c$	0.166	0.090${}^d$	0.350
IN-NSEI	0.743${}^b$	0.335${}^d$	0.166	0.335${}^c$	0.125${}^d$	0.160	0.091${}^d$	0.366
JP-N225	1.477${}^d$	0.270${}^d$	0.264${}^c$	0.145	$-$0.143${}^c$	$-$0.225${}^c$	0.016	0.240
KR-KS11	0.647${}^a$	0.222${}^d$	0.498${}^d$	0.149	$-$0.088${}^d$	$-$0.094${}^d$	$-$0.150${}^d$	0.435
SG-STI	1.013${}^d$	0.179${}^c$	0.444${}^d$	0.144	0.034${}^b$	0.045${}^d$	$-$0.013	0.323
AU-AORD	1.458${}^d$	0.252${}^d$	0.245${}^b$	0.175	0.006	0.004	0.062	0.205

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Table A.11.

The effect of macroprudential policy announcements on volatility.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act.${}_{t+1}$	Act.${}_t$	Act.${}_{t-1}$	$R_2$
Panel A: Europe
BE-BFX	0.540${}^c$	0.339${}^d$	0.345${}^d$	0.206${}^c$	0.000	0.000	0.000	0.522
CH-SSMI	0.715${}^c$	0.410${}^d$	0.338${}^d$	0.088	0.000	0.000	0.000	0.468
DE-GDAXI	0.992${}^d$	0.347${}^d$	0.389${}^d$	0.056	0.298${}^d$	0.033${}^b$	$-$0.006	0.410
DK-OMXC20	2.176${}^d$	0.305${}^d$	0.352${}^d$	$-$0.078	0.000	0.000	0.000	0.237
ES-IBEX	0.848${}^c$	0.429${}^d$	0.266${}^d$	0.146${}^a$	0.000	0.000	0.000	0.451
EU-STOXX50E	0.885${}^c$	0.257${}^d$	0.474${}^d$	0.087	0.119${}^c$	0.049	0.045${}^c$	0.411
FI-OMXHPI	1.601${}^d$	0.328${}^d$	0.353${}^d$	$-$0.030	0.000	0.000	0.000	0.286
FR-FCHI	0.587${}^b$	0.391${}^d$	0.306${}^c$	0.182${}^b$	0.000	0.000	0.000	0.492
GB-FTSE	1.259${}^c$	0.251${}^d$	0.331${}^d$	0.171	0.138${}^d$	$-$0.032${}^a$	0.056	0.277
IT-FTMIB	0.704${}^c$	0.255${}^d$	0.435${}^d$	0.160${}^a$	0.000	0.000	0.000	0.406
NL-AEX	1.215${}^d$	0.274${}^d$	0.443${}^d$	0.032	0.209${}^d$	$-$0.097${}^d$	0.234${}^d$	0.337
NO-OSEAX	2.590${}^d$	0.195${}^d$	0.322${}^d$	$-$0.040	0.000	0.000	0.000	0.123
SE-OMXSPI	1.950${}^d$	0.295${}^d$	0.415${}^d$	$-$0.137	0.030	$-$0.003	0.004	0.269
Panel B: America
BR-BVSP	0.545${}^a$	0.457${}^d$	0.211${}^c$	0.225${}^c$	$-$0.014	$-$0.026	0.078${}^d$	0.532
CA-GSPTSE	0.987${}^d$	0.428${}^d$	0.205${}^b$	0.124	$-$0.039${}^b$	0.148${}^c$	0.071${}^b$	0.374
MX-MXX	1.078${}^b$	0.073	0.419${}^d$	0.288${}^b$	0.018	0.091${}^d$	$-$0.076${}^d$	0.200
US-DJI	0.943${}^c$	0.377${}^d$	0.270${}^c$	0.153	$-$0.028${}^b$	0.045${}^d$	$-$0.123${}^d$	0.356
US-IXIC	1.139${}^d$	0.339${}^d$	0.372${}^d$	0.065	$-$0.013	$-$0.088${}^d$	$-$0.143${}^d$	0.369
US-RUT	1.509${}^d$	0.363${}^d$	0.300${}^c$	0.046	$-$0.008	$-$0.056${}^d$	$-$0.131${}^d$	0.314
US-SPX	0.903${}^c$	0.428${}^d$	0.291${}^d$	0.081	0.038${}^b$	0.0360${}^c$	$-$0.089${}^d$	0.422
Panel C: Asia and Australia
CN-SSEC	1.170${}^c$	0.306${}^d$	0.267${}^b$	0.190	0.000	0.000	0.000	0.289
HK-HSI	0.931${}^c$	0.327${}^d$	0.339${}^d$	0.151	0.134${}^d$	0.070${}^d$	$-$0.026${}^b$	0.383
IN-BSESN	0.665${}^b$	0.296${}^d$	0.264${}^a$	0.301${}^b$	$-$0.061${}^d$	0.026	$-$0.098${}^d$	0.326
IN-NSEI	0.660${}^b$	0.358${}^d$	0.200	0.303${}^b$	$-$0.072${}^d$	0.063	$-$0.124${}^d$	0.346
JP-N225	1.220${}^c$	0.294${}^d$	0.233${}^b$	0.206	0.002	0.277${}^d$	$-$0.060${}^c$	0.240
KR-KS11	0.704${}^a$	0.225${}^d$	0.505${}^d$	0.127	0.000	0.000	0.000	0.429
SG-STI	1.077${}^d$	0.168${}^c$	0.451${}^d$	0.132	0.050	0.019	0.026	0.325
AU-AORD	1.367${}^c$	0.245${}^d$	0.259${}^b$	0.187	0.184${}^a$	0.074${}^d$	0.119${}^d$	0.216

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Table A.12.

The effect of US action announcements on the volatility in other countries.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act.${}_{t+1}$	Act.${}_t$	Act.${}_{t-1}$	US Act.${}_{t-1}$	$R_2$
Panel A: Europe
BE-BFX	0.530${}^c$	0.357${}^d$	0.337${}^d$	0.199${}^c$	$-$0.057${}^d$	$-$0.056${}^b$	0.157${}^c$	$-$0.011	0.541
CH-SSMI	0.763${}^c$	0.436${}^d$	0.310${}^d$	0.079	$-$0.100${}^d$	0.261${}^d$	$-$0.051${}^d$	0.020	0.494
DE-GDAXI	1.310${}^d$	0.331${}^d$	0.358${}^d$	0.031	0.028	0.035	0.036	0.033	0.403
DK-OMXC20	2.191${}^d$	0.309${}^d$	0.359${}^d$	$-$0.092	$-$0.004	0.043	$-$0.107${}^d$	0.003	0.250
ES-IBEX	0.755${}^c$	0.436${}^d$	0.258${}^d$	0.166${}^b$	0.022	$-$0.035${}^a$	$-$0.008	$-$0.019	0.457
EU-STOXX50E	1.026${}^c$	0.257${}^d$	0.448${}^d$	0.080	0.051${}^c$	$-$0.011	$-$0.005	0.008	0.420
FI-OMXHPI	1.753${}^d$	0.322${}^d$	0.374${}^d$	$-$0.081	0.000	0.000	0.000	0.043	0.293
FR-FCHI	0.670${}^c$	0.413${}^d$	0.244${}^c$	0.201${}^b$	0.247${}^d$	$-$0.041${}^c$	0.025${}^b$	0.003	0.528
GB-FTSE	1.294${}^c$	0.259${}^d$	0.321${}^c$	0.166	0.011	$-$0.005	0.0200	$-$0.012	0.268
IT-FTMIB	0.762${}^c$	0.250${}^d$	0.446${}^d$	0.140	0.003	$-$0.008	0.012	0.020	0.408
NL-AEX	1.096${}^c$	0.256${}^d$	0.462${}^d$	0.057	$-$0.042	$-$0.040	0.064${}^a$	0.011	0.334
NO-OSEAX	2.730${}^d$	0.197${}^d$	0.334${}^d$	$-$0.084	$-$0.095${}^d$	0.082${}^d$	$-$0.042${}^c$	0.052${}^a$	0.140
SE-OMXSPI	2.101${}^d$	0.279${}^d$	0.440${}^d$	$-$0.184${}^a$	0.014	0.009	0.041	0.030	0.278
Panel B: America
BR-BVSP	0.558${}^a$	0.446${}^d$	0.226${}^c$	0.218${}^c$	$-$0.017	$-$0.000	0.018	0.019	0.524
CA-GSPTSE	1.057${}^d$	0.416${}^d$	0.212${}^b$	0.108	$-$0.010	0.019	0.050${}^c$	0.031	0.369
MX-MXX	1.268${}^b$	0.062	0.427${}^d$	0.250${}^a$	0.0310	0.056${}^a$	$-$0.037${}^c$	0.029	0.203
Panel C: Asia and Australia
CN-SSEC	1.325${}^d$	0.309${}^d$	0.255${}^b$	0.166	$-$0.091	0.090${}^b$	$-$0.069	0.009	0.278
HK-HSI	0.915${}^c$	0.323${}^d$	0.377${}^d$	0.117	$-$0.009	0.031${}^b$	$-$0.015	0.040${}^c$	0.388
IN-BSESN	0.757${}^b$	0.291${}^d$	0.239${}^a$	0.306${}^b$	0.011	0.015	0.018	0.027	0.324
IN-NSEI	0.746${}^c$	0.351${}^d$	0.176	0.309${}^b$	0.016	0.023	0.017	0.024	0.342
JP-N225	1.473${}^d$	0.276${}^d$	0.294${}^c$	0.111	$-$0.094${}^d$	$-$0.056	$-$0.041	$-$0.009	0.234
KR-KS11	0.669${}^a$	0.224${}^d$	0.481${}^d$	0.158	$-$0.118${}^d$	$-$0.053${}^a$	$-$0.101${}^d$	0.022	0.439
SG-STI	1.107${}^d$	0.171${}^c$	0.440${}^d$	0.132	0.040	0.027${}^c$	0.012	0.010	0.327
AU-AORD	1.705${}^d$	0.236${}^d$	0.251${}^b$	0.118	0.064${}^c$	$-$0.009	0.043	0.064${}^a$	0.232

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. US Act.${}_{t-1}$ represents the actions from the US from the previous day, also multiplied by (${\mathit{RV}}_t^D$). $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.

Table A.13.

The effect of Euro area action announcements on the volatility in other countries.

	Const.	RV${}^D$	RV${}^W$	RV${}^M$	Act.${}_{t+1}$	Act.${}_t$	Act.${}_{t-1}$	EU Act.${}_{t-1}$	$R_2$
Panel A: Europe
CH-SSMI	0.801${}^c$	0.436${}^d$	0.286${}^d$	0.094	$-$0.122${}^d$	0.265${}^d$	$-$0.048${}^d$	0.026	0.495
GB-FTSE	1.441${}^d$	0.248${}^d$	0.329${}^d$	0.137	0.011	$-$0.004	0.015	0.039${}^b$	0.272
Panel B: America
BR-BVSP	0.554${}^a$	0.437${}^d$	0.246${}^c$	0.208${}^c$	$-$0.013	$-$0.004	0.014	0.039${}^a$	0.526
CA-GSPTSE	1.082${}^d$	0.423${}^d$	0.189${}^b$	0.119	$-$0.007	0.018	0.043${}^c$	0.044	0.370
MX-MXX	1.142${}^b$	0.071	0.427${}^d$	0.269${}^a$	0.039	0.051	$-$0.041${}^d$	$-$0.025	0.201
US-DJI	1.047${}^c$	0.371${}^d$	0.269${}^c$	0.135	$-$0.011	0.026	$-$0.017	0.034	0.358
US-IXIC	1.324${}^d$	0.336${}^d$	0.367${}^d$	0.032	$-$0.007	0.023	$-$0.007	0.064${}^c$	0.376
US-RUT	1.487${}^d$	0.365${}^d$	0.305${}^c$	0.041	$-$0.008	0.041	$-$0.023	0.022	0.319
US-SPX	1.024${}^c$	0.416${}^d$	0.293${}^d$	0.060	$-$0.014	0.039	0.005	0.050${}^b$	0.428
Panel C: Asia and Australia
CN-SSEC	1.314${}^d$	0.309${}^d$	0.260${}^b$	0.166	$-$0.087	0.088${}^b$	$-$0.061	$-$0.028	0.281
HK-HSI	0.881${}^c$	0.336${}^d$	0.352${}^d$	0.139	$-$0.003	0.032${}^a$	$-$0.012	$-$0.009	0.380
IN-BSESN	0.715${}^b$	0.300${}^d$	0.221	0.326${}^b$	0.014	0.012	0.027	$-$0.008	0.321
IN-NSEI	0.715${}^b$	0.358${}^d$	0.162	0.325${}^c$	0.018	0.020	0.026	$-$0.008	0.340
JP-N225	1.477${}^d$	0.276${}^d$	0.295${}^c$	0.109	$-$0.097${}^d$	$-$0.057	$-$0.041	$-$0.003	0.233
KR-KS11	0.647${}^a$	0.226${}^d$	0.472${}^d$	0.170	$-$0.118${}^d$	$-$0.054${}^a$	$-$0.101${}^d$	0.040${}^b$	0.441
SG-STI	1.065${}^d$	0.174${}^c$	0.437${}^d$	0.143	0.043	0.029${}^d$	0.014	$-$0.012	0.327
AU-AORD	1.853${}^d$	0.246${}^d$	0.202${}^a$	0.127	0.070${}^c$	$-$0.017	0.034	0.097${}^c$	0.239

Note: a, b, c, d in superscript denote significance at the 15%, 10%, 5%, and 1%, levels, respectively. The values in bold show all statistically significant coefficients at the 15% level. Const. represents a constant. RV${}^D$ is realized volatility from the previous day, RV${}^W$ and RV${}^M$ is the average realized volatility from the previous week (5 days) and month (2 days) respectively. Act${}_{t+1}$, Act${}_t$, Act${}_{t-1}$ are dummy variables multiplied by (${\mathit{RV}}_t^D$). It represents action that were performed after, during, or before each day, respectively. EU Act.${}_{t-1}$ represents the actions from the EU from the previous day, also multiplied by (${\mathit{RV}}_t^D$). $R^2$ represents $R$-squared. The models are estimated using ordinary least squares (OLS) and the standard errors are obtained via heteroskedasticity- and autocorrelation-consistent (HAC) estimator (Newey and West, 1994). List of countries and stock indices is presented in Table 1.