Stock Volatility during the Recent Financial Crisis

This paper uses monthly returns from 1802 to 2010, daily returns from 1885 to 2010, and intraday returns from 1982 to 2010 in the USA to show how stock volatility has changed over time. It also uses various measures of volatility implied by option prices to infer what the market was expecting to happen in the months following the financial crisis in late 2008. This episode was associated with historically high levels of stock market volatility, particularly among financial sector stocks, but the market did not expect volatility to remain high for long and it did not. This is in sharp contrast to the prolonged periods of high volatility during the Great Depression. Similar analysis of stock volatility in the United Kingdom and Japan reinforces the notion that the volatility seen in the 2008 crisis was relatively short‐lived. While there is a link between stock volatility and real economic activity, such as unemployment rates, it can be misleading.     

Media Coverage and the Cross-section of Stock Returns

By reaching a broad population of investors, mass media can alleviate informational frictions and affect security pricing even if it does not supply genuine news. We investigate this hypothesis by studying the cross-sectional relation between media coverage and expected stock returns. We find that stocks with no media coverage earn higher returns than stocks with high media coverage even after controlling for well-known risk factors. These results are more pronounced among small stocks and stocks with high individual ownership, low analyst following, and high idiosyncratic volatility. Our findings suggest that the breadth of information dissemination affects stock returns.     

High Idiosyncratic Volatility and Low Returns: A Prospect Theory Explanation

The well‐documented negative relationship between idiosyncratic volatility and stock returns is puzzling if investors are risk‐averse. However, under prospect theory, while investors are risk‐averse in the domain of gains, they exhibit risk‐seeking behavior in the domain of losses. Consistent with risk‐seeking investors’ preference for high‐volatility stocks in the loss domain, we find that the negative relationship between idiosyncratic volatility and stock returns is concentrated in stocks with unrealized capital losses, but is nonexistent in stocks with unrealized capital gains. This finding is robust to control for short‐term return reversals and maximum daily return, among other variables.     

Asymmetric risk premium in value and growth stocks

There are two competing explanations for the existence of a value premium, a rational market risk explanation, whereby value stocks are inherently more risky than growth stocks, and a market over-reaction hypothesis, where agents overstate future returns on growth stock. Using asymmetric GARCH-M models this paper tests the predictions of the two hypotheses. Specifically, examining whether returns exhibit a positive (negative) risk premium resulting from a negative (positive) shock and the relative size of any premium. The results of the paper suggest that following a shock, volatility and expected future volatility are heightened, leading to a rise in required rates of return which depresses current prices. Further, these effects are heightened for value stock over growth stock and for negative shocks over positive shocks. Thus, in support of the rational risk interpretation, with a volatility feedback explanation for predictive volatility asymmetry.     

The Effect of US Stock Market Uncertainty on Emerging Market Returns

We investigate the effects of US stock market uncertainty (VIX) on the stock returns in Latin America and aggregate emerging markets before, during, and after the financial crisis. We find that increases in VIX lead to significant immediate and delayed declines in emerging market returns in all periods. However, changes in VIX explained a greater percentage of changes in emerging market returns during the financial crisis than in other periods. The higher US stock market uncertainty exerts a much stronger depressing effect on emerging market returns than their own-lagged and regional returns. Our risk transmission model suggests that a heightened US stock market uncertainty lowers emerging market returns by both reducing the mean returns and raising the variance of returns. The VIX fears raise the volatility of emerging market returns through generalized autoregressive conditional heteroskedasticity (GARCH)-type volatility transmission processes.     

Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management

In this article we take a recent generalized VAR-GARCH approach to examine the extent of volatility transmission between oil and stock markets in Europe and the United States at the sector-level. The empirical model is advantageous in that it typically allows simultaneous shock transmission in the conditional returns and volatilities. Insofar as volatility transmission across oil and stock sector markets is a crucial element for portfolio designs and risk management, we also analyze the optimal weights and hedge ratios for oil-stock portfolio holdings with respect to the results. Our findings point to the existence of significant volatility spillover between oil and sector stock returns. However, the spillover is usually unidirectional from oil markets to stock markets in Europe, but bidirectional in the United States. Our back-testing procedures, finally, suggest that taking the cross-market volatility spillovers estimated from the VAR-GARCH models often leads to diversification benefits and hedging effectiveness better than those of commonly used multivariate volatility models such as the CCC-GARCH of Bollerslev (1990), the diagonal BEKK-GARCH of Engle and Kroner (1995) and the DCC-GARCH of Engle (2002).     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

TIME VARIATIONS IN RISK PREMIA,VOLATILITY, AND REWARD-TO-VOLATILITY

In this paper I relate the risk premia in the stock and bond markets to the conditional volatility of returns and time-varying reward-to-volatility variables. I find that the relation between the expected returns on the stocks and bonds and the volatility of returns is time varying. I provide an approach for evaluating the relative importance of the time-varying volatility of returns and reward-to-volatility variables to explain the predictability of risk premia for stock and bond returns. I show that changing reward-to-volatility variables explain more predictable variation in the risk premia for stocks and bonds than changing volatility of returns.     

Buy rough,sell smooth

Recent work has documented roughness in the time series of stock market volatility and investigated its implications for option pricing. We study a strategy for trading stocks based on measures of their implied and realized roughness. A strategy that goes long the roughest-volatility stocks and short the smoothest-volatility stocks earns statistically significant excess annual returns of 6% or more, depending on the time period and strategy details. The profitability of the strategy is not explained by standard factors. We compare alternative measures of roughness in volatility and find that the profitability of the strategy is greater when we sort stocks based on implied rather than realized roughness. We interpret the profitability of the strategy as compensation for near-term idiosyncratic event risk.     

Corporate governance and stock performance: The case of COVID-19 crisis

This paper analyzes the impact of COVID-19 on firm-level stock behaviors (including stock price volatility, trading volume and stock returns). Using US data, this paper examines whether confirmed cases (and deaths) of COVID-19 or COVID-19-associated online searches affect stock behaviors. The results show that our five COVID-19 proxies are all positively associated with stock price volatility and trading volume and negatively associated with stock returns. This paper further investigates the mitigating effect of corporate governance (viz., board and ownership structures) in this COVID-19 crisis. Overall, the results suggest that good corporate governance can mitigate the impact of COVID-19 on stock price volatility and trading volume but may not help to enhance stock returns. This paper also considers key policies used to tackle the COVID-19 pandemic and finds that government intervention plays an important role in stabilizing stock markets in this COVID-19 crisis.     

The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model

We investigate the impact of Covid-19 on stock markets across G7 countries and their business sectors. We highlight the synchronicity and severity of this unprecedented crisis. We find strong transition evidence to a crisis regime in all countries and sectors, yet crisis intensity and timings vary. The Health Care and Consumer services sectors were the most severely affected; a reflection of the Covid-19 drug-race and international travel restrictions. The Technology sector was hit the latest and least severely, as imposed lockdown measures forced people to explore various web-based entertainment and distraction options. Country-wise the UK and the US were the most affected with the highest heterogeneity in their business sectors' response; a possible reflection of the ambiguity in the initial response and adoption of lockdown measures. Financial markets' response to Covid-19 is akin to response in previous financial crisis rather than previous pandemics. A series of robustness checks confirms our findings.     

Stock return volatility,operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly

This study highlights the link between stock return volatility, operating performance, and stock returns. Prior studies suggest that there is a ‘low volatility’ anomaly, where firms with a low stock return volatility out-perform firms with a high stock return volatility. This paper confirms that low volatility stocks earn higher returns than high volatility stocks in emerging markets and developed markets outside of North America. We also show that low volatility stocks have higher operating returns and this might explain why low volatility stocks earn higher stock returns. These results provide a partial explanation for the ‘low volatility effect’ that is independent from the existence of market anomalies or per se inefficiencies that might otherwise drive a low volatility effect. We emphasize the importance of controlling for stock return volatility when analyzing operating performance and stock performance.     

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross‐section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book‐to‐market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

The Effects of Abolishing a Foreign Institutional Investment Quota in Taiwan

Policies regarding the globalization of financial markets have long been investigated with conflicting results. This paper employs an event study approach with the EGARCH process to examine the effects of lifting restrictions on qualified foreign institutional investors in the Taiwanese stock market. The empirical results indicate significant differences in the behavior of stock returns in the electronics, financial, and other nonfinancial sectors, both on and after the abolition of Taiwan's investment quota. In addition, the volatility of stock returns in the electronics sector increases following the event. Foreign ownership provides some additional explanatory power for electronics and other nonfinancial stocks in the short run.     

Aggregate Jump and Volatility Risk in the Cross‐Section of Stock Returns

We examine the pricing of both aggregate jump and volatility risk in the cross‐section of stock returns by constructing investable option trading strategies that load on one factor but are orthogonal to the other. Both aggregate jump and volatility risk help explain variation in expected returns. Consistent with theory, stocks with high sensitivities to jump and volatility risk have low expected returns. Both can be measured separately and are important economically, with a two‐standard‐deviation increase in jump (volatility) factor loadings associated with a 3.5% to 5.1% (2.7% to 2.9%) drop in expected annual stock returns.     

Cross-section of option returns and volatility

We study the cross-section of stock option returns by sorting stocks on the difference between historical realized volatility and at-the-money implied volatility. We find that a zero-cost trading strategy that is long (short) in the portfolio with a large positive (negative) difference between these two volatility measures produces an economically and statistically significant average monthly return. The results are robust to different market conditions, to stock risks-characteristics, to various industry groupings, to option liquidity characteristics, and are not explained by usual risk factor models.     

Industry characteristics and financial risk contagion

This article proposes a new measure of tail risk spillover: the conditional coexceedance (CCX), defined as the number of joint occurrences of extreme negative returns in an industry, conditional on an extreme negative return in the financial sector. The empirical application provides evidence of significant volatility and tail risk spillovers from the financial sector to many real sectors in the U.S. economy from 2001 to 2011. These spillovers increase in crisis periods. The CCX in a given sector is positively related to its amount of debt financing and negatively related to its valuation and investment. Therefore, real economy sectors—which require relatively high debt financing and whose value and investment activity are relatively lower—are prime candidates for stock price volatility and depreciation in the wake of a financial sector crisis. Evidence also suggests that the higher the industry’s degree of competition, the stronger the tail risk spillover from the financial sector.     

A Tale of Two Anomalies: Higher Returns of Low‐Risk Stocks and Return Seasonality

Prior studies have shown that low beta and low volatility stocks earn higher average returns than high beta and high volatility stocks, contradicting the prediction of the capital asset pricing model and the fundamental relationship between risk and return. In this paper, we demonstrate that this phenomenon is driven by the seasonality of stock returns. We show that the risk‐return tradeoff does hold in the nonsummer months, and that switching to a portfolio of low‐risk stocks in summer outperforms—both in terms of absolute and in risk‐adjusted returns—buy and hold strategies as well as the Sell in May strategy of switching to treasury bills in summer.     

Asset market linkages: Evidence from financial, commodity and real estate assets

We use a general Markov switching model to examine the relationships between returns over three different asset classes: financial assets (US stocks and Treasury bonds), commodities (oil and gold) and real estate assets (US Case-Shiller index). We confirm the existence of two distinct regimes: a “tranquil” regime with periods of economic expansion and a “crisis” regime with periods of economic decline. The tranquil regime is characterized by lower volatility and significantly positive stock returns. During these periods, there is also evidence of a flight from quality - from gold to stocks. By contrast, the crisis regime is characterized by higher volatility and sharply negative stock returns, along with evidence of contagion between stocks, oil and real estate. Furthermore, during these periods, there is strong evidence of a flight to quality - from stocks to Treasury bonds.