War and stock markets: The effect of World War Two on the British stock market

This paper studies the effect of World War Two (WWII) on the British stock market. It contributes to the literature in several ways. First, this paper thoroughly investigates the impact of historically major events on the British stock market using a variety of empirical approaches in order to ensure a comprehensive examination of the impact of WWII on British stock returns. We utilise an event study of pre-selected historically major events, an investigation of the possible causes of the largest price movements as well as utilising an endogenous procedure testing for structural breaks. Secondly we extend the literature on behavioural finance and investor sentiment in extreme circumstances. In particular we examine the ‘negativity effect’, documented by Akhtar et al. (2011) and determine whether stock returns reacted more strongly to negative events or positive events. Overall we find limited evidence of strong links between war events and market returns although there is support for the ‘negativity effect’.     

The trading behavior of institutions and individuals in Chinese equity markets

This paper employs a unique data set to analyze the trading behavior of 4.74 million individual and institutional investors across Mainland China. Results show that groups of individual investors with varying trade values (as proxies for wealth levels) engage in different trading strategies. Chinese institutions are momentum investors, while less wealthy Chinese individual investors at large are contrarian investors. The results also indicate that a small group of wealthiest Chinese individuals tend to behave like institutions when they buy stocks, and behave like less wealthy individuals when they sell. Furthermore, only the trading activities of institutions and of wealthiest individuals can affect future stock volatility, but those of Chinese individual investors at large have no predictive power for future stock returns.     

Preference heterogeneity and asset prices: An exact solution

We introduce a general equilibrium model of a multi-agent, pure-exchange economy and find a set of conditions that enable us to obtain explicit closed-form solutions to the equilibrium interest rate, stock price, risk premium and stock market volatility when investors have heterogenous risk aversions. Because the market is dynamically complete, full risk sharing obtains and a representative agent can be constructed, though the risk aversion of this agent fluctuates over time with the state of the economy, as the relative wealth distribution of the individual investors changes. We show that preference heterogeneity can cause asset prices to be significantly more volatile than the underlying dividends and that it can lead to leverage-like effects in volatility, in the sense that volatility increases after stock-market declines.     

Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach

Extending previous work on asset-based style factor models, this paper proposes a model that allows for the presence of structural breaks in hedge fund return series. We consider a Bayesian approach to detecting structural breaks occurring at unknown times and identifying relevant risk factors to explain the monthly return variation. Exact and efficient Bayesian inference for the unknown number and positions of the breaks is performed by using filtering recursions similar to those of the forward–backward algorithm. Existing methods of testing for structural breaks are also used for comparison. We investigate the presence of structural breaks in several hedge fund indices; our results are consistent with market events and episodes that caused substantial volatility in hedge fund returns during the last decade.     

Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model

Given that policy uncertainty shocks in the economic environment can exacerbate financial market volatility and pose financial risks, this paper utilizes a smooth transition version of the GARCH-MIDAS model to investigate the impact of different structural state changes in economic policy uncertainty (EPU) on stock market volatility. The extended model explains the nonlinear effects of the macro variables and the structural break changes in regime transitions. The empirical results confirm that the EPU indicators provide effective prediction information for stock volatility from the in-sample and out-of-sample analyses, which reveals that the smooth transition model provides an effective method for detecting the possible regime changes between stock volatility and macroeconomic uncertainty. Additionally, we further confirm that some category-specific EPU indicators also have strong smooth transition behaviour with respect to stock volatility. More important, our new model provides significant economic value to investors from a utility gain perspective. Overall, the institutional changes present in EPU play a nonnegligible and important role in stock market volatility. Accurate identification of the structural features of financial data helps investors deepen their understanding of the sources of stock market volatility.     

How do policy and information shocks impact co-movements of China’s T-bond and stock markets?

We investigate the impacts of policy and information shocks on the correlation of China’s T-bond and stock returns, using originally the asymmetric dynamic conditional correlation (DCC) model that allows for the coexistence of opposite-signed asymmetries. The co-movements of China’s capital markets react to large macroeconomic policy shocks as evidenced by structural breaks in the correlation following the drastic 2004 macroeconomic austerity. We show that the T-bond market and the bond–stock correlations bear more of the brunt of the macroeconomic contractions. We also find that the bond–stock correlations respond more strongly to joint negative than joint positive shocks, implying that investors tend to move both the T-bond and stock prices in the same direction when the two asset classes have been hit concurrently by bad news, but tend to shift funds from one asset class to the other when hit concurrently by good news. However, the stock–stock correlation is found to increase for joint positive shocks, indicating that investors tend to herd more for joint bullish than joint bearish stock markets in Shanghai and Shenzhen.     

Asymmetric volatility in the cryptocurrency market: New evidence from models with structural breaks

Previous literature shows that major cryptocurrencies exhibit inverse asymmetric volatility: positive shocks increase price volatility more than negative ones. In this study, we revisit the asymmetric volatility dynamics of major cryptocurrencies using asymmetric GARCH models that incorporate endogenously detected structural breaks. Our results show that after incorporating structural breaks, volatility persistence decreases and asymmetric volatility increases for all cryptocurrencies in this study. Thus, prior research that ignores structural breaks underestimates the impact of unexpected news on price volatility in cryptocurrency markets. We also present important economic implications of our results: ignoring structural breaks adversely affects the hedging strategies, derivatives valuations, and risk exposure measurement of investors in cryptocurrency markets.     

SENSITIVITY OF INVESTOR REACTION TO MARKET DIRECTION AND VOLATILITY: DIVIDEND CHANGE ANNOUNCEMENTS

We examine whether investor reactions are sensitive to the recent direction or volatility of underlying market movements. We find that dividend change announcements elicit a greater change in stock price when the nature of the news (good or bad) goes against the grain of the recent market direction during volatile times. For example, announcements to lower dividends elicit a significantly greater decrease in stock price when market returns have been up and more volatile. Similarly, announcements to raise dividends tends to elicit a greater increase in stock price when market returns have been normal or down and more volatile, although this latter tendency lacks statistical significance. We suggest an explanation for these results that combines the implications of a dynamic rational expectations equilibrium model with behavioral considerations that link the responsiveness of investors to market direction and volatility.     

Retail banking and behavioral financial engineering: The case of structured products

We apply cumulative prospect theory and hedonic framing to evaluate discount reverse convertibles (DRCs) and reverse convertible bonds (RCBs) as important examples of structured products from a boundedly rational investor’s point of view. While common expected utility theory would also conclude that DRCs and RCBs are of interest to investors with moderate return expectations and underestimated stock return volatility, that theory would overestimate the market success of DRCs and underestimate that of RCBs in comparison to a situation with bounded rationality. Hedonic framing and relatively low subjectively felt competence levels of investors are decisive for the demand for RCBs.     

Do structural breaks in volatility cause spurious volatility transmission?

We show through extensive Monte Carlo simulations that structural breaks in volatility (volatility shifts) across two independently generated return series cause spurious volatility transmission when estimated with popular bivariate GARCH models. However, using a dummy variable for the induced volatility shift virtually eliminates this bias. We also show that structural breaks in volatility have a substantial impact on the estimated hedge ratios. We confirm our simulation findings using the US stock market data.     

Volatility dependences of stock markets with structural breaks

We develop a Vector Heterogeneous Autoregression model with Continuous Volatility and Jumps (VHARCJ) where residuals follow a flexible dynamic heterogeneous covariance structure. We employ the Bayesian data augmentation approach to match the realised volatility series based on high-frequency data from six stock markets. The structural breaks in the covariance are captured by an exogenous stochastic component that follows a three-state Markov regime-switching process. We find that the stock markets have higher volatility dependence during turmoil periods and that breakdowns in volatility dependence can be attributed to the increase in market volatilities. We also find positive correlations between the Asian stock markets, the European stock market, and the UK stock market. The US stock market has positive correlations with all other markets for most of the sample periods, indicating the leading position of US stock market in the global stock markets. In addition, the proposed three-state VHARCJ model with Dynamic Conditional Correlation (DCC) and break structure under student-t distribution has a superior density forecast performance as compared to the competing models. The forecast models with structural breaks outperform those without structural breaks based on the log predicted likelihood, the log Bayesian factor, and the root mean square loss function.     

US presidential elections and implied volatility: The role of political uncertainty

This paper focuses on the effects of political uncertainty and the political process on implied stock market volatility during US presidential election cycles. Using monthly Iowa Electronic Markets data over five elections, we document that stock market uncertainty, as measured by the VIX volatility index, increases along with positive changes in the probability of success of the eventual winner. The association between implied volatility and the election probability of the eventual winner is positive even after controlling for changes in overall election uncertainty. These findings indicate that the presidential election process engenders market anxiety as investors form and revise their expectations regarding future macroeconomic policy.     

Volatility spillovers between oil prices and the stock market under structural breaks

This paper employs univariate and bivariate GARCH models to examine the volatility of oil prices and US stock market prices incorporating structural breaks using daily data from July 1, 1996 to June 30, 2013. We endogenously detect structural breaks using an iterated algorithm and incorporate this information in GARCH models to correctly estimate the volatility dynamics. We find no volatility spillover between oil prices and US stock market when structural breaks in variance are ignored in the model. However, after accounting for structural breaks in the model, we find strong volatility spillover between the two markets. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results which underscores the serious consequences of ignoring these structural breaks. Our findings are consistent with the notion of cross-market hedging and sharing of common information by financial market participants in these markets.     

Stale Information,Shocks, and Volatility

We propose a new approach to measuring the effect of unobservable private information on volatility. Using intraday data, we estimate the effect of a well‐identified shock on the volatility of stock returns of European banks as a function of the quality of public information available about the banks. We hypothesize that as publicly available information becomes stale, volatility effects and its persistence increase, as private information of investors becomes more important. We find strong support for this idea in the data. We further show that stock volatility is higher just before important announcements if information is stale.     

Stock market volatility around national elections

This paper investigates a sample of 27 OECD countries to test whether national elections induce higher stock market volatility. It is found that the country-specific component of index return variance can easily double during the week around an election, which shows that investors are surprised by the election outcome. Several factors, such as a narrow margin of victory, lack of compulsory voting laws, change in the political orientation of the government, or the failure to form a government with parliamentary majority significantly contribute to the magnitude of the election shock. Furthermore, some evidence is found that markets with short trading history exhibit stronger reaction. Our findings have important implications for the optimal strategies of institutional and individual investors who have direct or indirect exposure to volatility risk.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence?

This paper conducts an investigation of volatility transmission between stock markets in Hong Kong, Europe and the United States covering the time period from 2000 up to 2011. Using intra-daily data we compute realized volatility time series for the three markets and employ a Heterogeneous Autoregressive Distributed Lag Model as our baseline econometric specification. Motivated by the presence of various crisis events contained in our sample, we detect time-variation and structural breaks in volatility spillovers. Particularly during the financial crisis of 2007, we find effects consistent with the notion of contagion, suggesting strong and sudden increases in the cross-market synchronization of chronologically succeeding volatilities. Investigating the role of mean breaks and conditional heteroskedasticity in the realized volatilities, however, we find the latter to be the main driver of breaks in volatility spillovers. Taking the volatility of realized volatilities into account, we find no evidence of contagion anymore.     

The autumn effect of gold

This paper studies recurring annual events potentially introducing seasonality into gold prices. We analyze gold returns for each month from 1980 to 2010 and find that September and November are the only months with positive and statistically significant gold price changes. This “autumn effect” holds unconditionally and conditional on several risk factors. We argue that the anomaly can be explained with hedging demand by investors in anticipation of the “Halloween effect” in the stock market, wedding season gold jewelery demand in India and negative investor sentiment due to shorter daylight time. The autumn effect can also be characterized by a higher unconditional and conditional volatility than in other seasons.     

Intra-Industry Reactions to Stock Split Announcements

We examine whether favorable information conveyed by stock split announcements transfers to nonsplitting firms within the same industry. On average, nonsplitting firms' shareholders experience positive and significant abnormal returns at the stock split announcements of their industry counterparts. In addition, industrywide and firm-specific characteristics are important determinants in explaining nonsplitting firms' stock returns. These firms' earnings increase significantly, and the earnings changes are positively related to the stock price reactions. Finally, we find no evidence that investors revise the value of nonsplitting firms because they anticipate a decline in earnings volatility.     

Comparable firms and the precision of equity valuations

I investigate the relationship between the amount of information provided by a firm's comparables (i.e., firms in the same line of business as the firm being valued) and the precision of the firm's equity valuation. When investors have more information, previous studies argue that investors can make a more precise estimate of a firm's true equity value and this implies a lower (excess) stock return volatility around corporate events such as earnings announcements. I develop a simple model that shows a negative relationship between the amount of information provided by a firm's comparables and the firm's stock return volatility. Using alternative measures of information provided by comparables and different definitions of comparables, I consistently find a negative and significant relationship between these information measures and stock return volatility, ceteris paribus.