Changes In Stock Market Volatility Research Articles

Central Bank Credibility’s Effect on Stock Exchange Returns’ Volatility: Evidence from OECD Countries

Central bank characteristics are important determinants of stock market returns and their volatility. While the literature has examined the effects of transparency and independence, no research has been conducted so far on the effect of central bank credibility on stock market returns’ volatility. A panel regression using financial and macroeconomic data from 45 OECD member countries over the period of 1998–2022 tested the hypothesis that central bank credibility determines stock exchange returns’ volatility. The results indicated that credibility reduces stock returns’ volatility, remaining robust and statistically significant across models. Economic growth also decreases stock market volatility, while money-market interest rates’ volatility, the stock market’s turnover ratio, and economic/financial crises act as amplifying factors of stock market volatility. All variables, except for economic growth, exhibit unidirectional causality, leading to changes in stock market volatility.

Political shocks and asset prices

AbstractWe estimate how asset prices respond to a range of political shocks, including changes in a country's economic stewardship, national elections, coup d'états, wars, and terrorist attacks. Using an event study approach and daily prices from the Buenos Aires exchange (Argentina) between 1967 and 2020, we find that stock-market volatility increases in the days immediately following major policy-shifting events. These results hold irrespective of whether returns are measured in nominal terms, in local consumption units, or in US dollars. The most significant increase in post-event risk is associated with irregular government turnovers. Volatility also increases in the days immediately following a defeat in an international war, national elections, and changes in the country's economic stewardship. No changes in stock-market volatility occur, however, after terrorist attacks or when the date of a new administration's inauguration is publicly known and determined sufficiently far in advance.

Hedging stock market risks: Can gold really beat bonds?

This paper explores the impacts of stock market volatility on stock-bond and stock-gold correlations and compares the effectiveness of bonds with gold hedging the stock market risk. We propose an extensive DCC-MIDAS model, which takes the changes in stock market volatility as exogenously predetermined and accounts for its asymmetric impacts. The results show strong evidence that stock market volatility significantly and asymmetrically affects stock-bond and stock-gold correlations in different ways. Moreover, we prove that government bonds are more effective than gold in hedging stock return volatility, especially in the period of stock market turbulence. Our results suggest that government bonds are overall a better hedge for stock investors.

The Determinants of the Ex Ante Risk Premiumin Commercial Real Estate

We investigate the determinants of the ex ante risk premium in commercial real estate. Using a 20-year time series and Markov-switching regression, we find that the ex ante risk premium is affected by fundamental and non-fundamental determinants, albeit not symmetrically when risk premiums are increasing and decreasing. In particular, we find that changes in debt capital market conditions have a higher predictive power for changes in the ex ante risk premium when it is increasing, while changes in stock market volatility and commercial real estate market returns have a higher predictive power when the risk premium is on the decline. In addition, changes in commercial real estate sentiment and NAREIT returns can predict changes in the ex ante risk premium; however, the predictive power of these variables varies across property types and risk premium (risk perception) states.

European Significant Bank Stock Market Volatility: Is there a Bail-In Effect?

In 2016 the “bail-in” tool, set by the European Bank Recovery and Resolution Directive (BRRD), started to enter into force: shareholders of European banks lose their money in the case of bank rescue. Before the “bail-in” mechanism was introduced, governments often performed bail-out of distressed banks. The larger risk born by shareholders can be observed in the growth of stock market volatility. We implement a panel data analysis on a sample of large European banks and find that in 2016 stock market volatility changes in a way that is theoretically consistent with the introduction of the “bail-in”. Moreover, Italy shows an even higher volatility, probably due to retail investors owning bank shares, who could have understood the risk only after the burden sharing of four Italian banks in November 2015. These findings can be a warning about market efficiency, with implications for the systemic risk of a “bail-in” procedure.

Determinants of European Large Bank Stock Market Volatility: Is There a Bail-In Effect?

On the first of January 2016 the European bank recovery and resolution directive (BRRD) started to enter into force. Before the BRRD, governments often performed bail-out of distressed banks. Instead after the BRRD, the Single Resolution Fund and governments intervene only after the writing-off of the stocks (and other financial instruments), so shareholders of the biggest European banks lose their money in the case of bank rescue. This could imply a larger risk that can be observed in the stock market volatility. In this paper we perform a panel data analysis on a sample of large European banks finding that in 2016 the model for the stock market volatility changes and the changes can be theoretically consistent with the introduction of the BRRD. Moreover Italy shows an even larger volatility. This could be due to the strong presence of retail investors owning bank shares, who could have understood the risk related to the BRRD only after the burden sharing of four Italian banks in November 2015. This finding can be a warning about the level of market efficiency, with possible implications also for the systemic risk of a bail-in procedure.

Towards an asymmetric long run equilibrium between stock market uncertainty and the yield spread. A threshold vector error correction approach

This paper investigates the interrelationships and the asymmetric co-movements between the yield spread, macroeconomic factors and the stock market volatility across five major world economies. We highlight the non-linear adjusting process of the yield spread to its equilibrium value in response to changes in stock market volatility by using a consistent threshold cointegration error correction model. Our findings differ for different countries and for states of the economy. We find that for the US, the UK, Japan, and France, the adjustment of the yield spread towards its equilibrium value portrays the existence of negative asymmetric market volatility transmission. In addition, differences in the magnitude of the effects denote that yield spread changes in Japan and France appear to significantly adjust more swiftly to equilibrium values compared to the US where a higher degree of persistence is observed. Last, our results suggest evidence of bi-directional time varying Granger causality between the yield spread and stock market volatility for all countries, in both the pre- and post-crisis period.

Market Volatility and the Timing of IPO Filings

We investigate how aggregate IPO filing volume responds to changes in stock market volatility. The filing volume consists of all non-financial firms that filed with the SEC between 1984 and 2004. Controlling for factors shown in the literature to impact primary market activity, notably stock market returns, we find filing volume positively related to changes in market volatility, and the relation is especially pronounced when stock market return is at „normal‟ levels, i.e. neither too high nor too low. The relation also holds at the industry level, in a pooled time-series crossindustry regression context. The relation is more pronounced for IPO filings in „new‟ industries (computers, software, electronic equipment, and telecommunications) relative to traditional industries. These results are consistent with our hypothesis that the ability to discover investor valuations before deciding to sell shares gives firms filing with the SEC an „option‟ on the uncertain offer price. This option has value not only in a strong stock market but also in a volatile market. Furthermore, option theory implies that the marginal effect of volatility on this option is highest in „normal‟ stock markets.

Are investors moonstruck? Lunar phases and stock returns

This paper investigates the relation between lunar phases and stock market returns of 48 countries. The findings indicate that stock returns are lower on the days around a full moon than on the days around a new moon. The magnitude of the return difference is 3% to 5% per annum based on analyses of two global portfolios: one equal-weighted and the other value-weighted. The return difference is not due to changes in stock market volatility or trading volumes. The data show that the lunar effect is not explained away by announcements of macroeconomic indicators, nor is it driven by major global shocks. Moreover, the lunar effect is independent of other calendar-related anomalies such as the January effect, the day-of-week effect, the calendar month effect, and the holiday effect (including lunar holidays).

Structural changes in volatility and stock market development: Evidence for Spain

In this paper we review the factors that lead to changes in stock market volatility and use alternative methodologies of endogenous breakpoint detection in order to analyze whether the volatility of the Spanish stock market has changed significantly over the period 1941–2001. This period corresponds to years of profound development of both the financial and the productive sides of the economy in this country. The analysis of the Spanish stock market suggests that volatility has behaved in a different manner over the period 1941–2001: After three decades of low volatility, a structural break in volatility is detected in 1972, coinciding with the opening of the Spanish economy. From 1972 to 2001, the years of more intense financial development, the stock market presents a higher level of volatility and lower persistence. This effect is partly attributable to the increased growth of trading volume brought about by the economic development process.

Structural changes in volatility and stock market development: Evidence for Spain

In this paper we review the factors that lead to changes in stock market volatility and use alternative methodologies of endogenous breakpoint detection in order to analyze whether the volatility of the Spanish stock market has changed significantly over the period 1941–2001. This period corresponds to years of profound development of both the financial and the productive sides of the economy in this country. The analysis of the Spanish stock market suggests that volatility has behaved in a different manner over the period 1941–2001: After three decades of low volatility, a structural break in volatility is detected in 1972, coinciding with the opening of the Spanish economy. From 1972 to 2001, the years of more intense financial development, the stock market presents a higher level of volatility and lower persistence. This effect is partly attributable to the increased growth of trading volume brought about by the economic development process.

Stock Market Volatility Changes in Central Europe Caused by
 Asian and Russian Financial Crises

We consider four Central European stock market indexes over the period April 30, 1996 - August 31, 2001. We have found that the influence of the Asian crisis over the Central European markets is more severe than this of the Russian crisis. During the crises the Central European markets exhibit an increase in correlation and a decrease afterwards. We have found that the correlation never reaches pre-crisis level. We have found increasing persistence for Central European markets not only during the crisis but also during the postcrisis period. After the crisis, the market reaction is much weaker to the current market news than to the past information. Investigating the relationships between stock volatility and expected returns, we do not prove positive relation. During the crisis periods the relationship is positive and thus compensating risk-averse investors.

Are Investors Moonstruck? - Lunar Phases and Stock Returns

This paper investigates the relation between lunar phases and stock market returns of 48 countries. The findings indicate that stock returns are lower on the days around a full moon than on the days around a new moon. The magnitude of the return difference is 3% to 5% per annum based on analyses of two global portfolios: one equal-weighted and the other value-weighted. The return difference is not due to changes in stock market volatility or trading volumes. The data show that the lunar effect is not explained away by announcements of macroeconomic indicators, nor is it driven by major global shocks. Moreover, the lunar effect is independent of other calendar-related anomalies such as the January effect, the day-of-week effect, the calendar month effect, and the holiday effect (including lunar holidays).

Modelling Stock Market Volatility: Bridging the Gap to Continuous Time

Understanding And Specifying The Discrete Time Model: D.B. Nelson, Modelling Stock Market Volatility Changes. D.B. Nelson, Stationarity and Persistence in the GARCH(I,I) Model. D.B. Nelson, Conditional Heteroskedasticity in Asset Returns: A New Approach. P.A. Braun, D.B. Nelson and A.M. Sunier, Good News, Bad News, Volatility, and Betas. Continuous Time Limits And Optimal Filtering For ARCH Models: D.B. Nelson, ARCH Models as Diffusion Approximations. D.B. Nelson, Filtering and Forecasting with Misspecified ARCH Models I: Getting the Right Variance with the Wrong Model. D.B. Nelson and D.P. Foster, Filtering and Forecasting with Misspecified ARCH Models II: Making the Right Forecast with the Wrong Model. D.B. Nelson and D.P. Foster, Asymptotic Filtering Theory for Univariate ARCH Models. D.B. Nelson, Asymptotic Filtering Theory for Multivariate ARCH Models. D.B. Nelson and D.B. Nelson, Continuous Record Asymptotics for Rolling Sample Variance Estimators. Specification and Estimation of Continuous Time Processes: R.F. Engle and G.G.J. Lee, Estimating Diffusion Models of Stochastic Volatility. A.R. Gallant and G. Tauchen, Specification Analysis of Continuous Time Models in Finance. L.P. Hansen and J.A. Scheinkman, Back to the Future: Generating Moment Implications for Continuous-Time Markov Processes. Y.Ait-Sahalia, Nonparametric Pricing of Interest Rate Derivative Securities. Index.

Conditional Heteroskedasticity in Asset Returns: A New Approach

This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the persistence of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.