ON STOCK RETURN SEASONALITY AND CONDITIONAL HETEROSKEDASTICITY

We model the seasonal volatility of stock returns using GARCH specifications and size-sorted portfolios. Estimation results indicate that there are volatility differences between months and that these seasonal volatility patterns are conditional on firm size. Additionally, we find that seasonal volatility does not explain seasonal returns when the reward for risk is held constant over the sample period. Specifically, our results indicate that much of the abnormal return in January for small firms cannot be entirely attributed to either higher systematic risk or a higher risk premium in January.     

Additional evidence on transparency and bank financial performance

This paper examines the relationship between volatility and the probability of occurrence of expected extreme returns in the Canadian market. Four measures of volatility are examined: implied volatility from firm option prices, conditional volatility calculated using an EGARCH model, idiosyncratic volatility, and expected shortfall. A significantly positive relationship is observed between a firm's idiosyncratic volatility and the probability of occurrence of an extreme return in the subsequent month for firms. A 10% increase in idiosyncratic volatility in a given month is associated with the probability of an extreme shock in the subsequent month (top or bottom 1.5% of the returns distribution) of 26.4%. Other firm characteristics, including firm age, price, volume and book‐to‐market ratio, are also shown to be significantly related to subsequent firm extreme returns. The effects of conditional and implied volatility are mixed. The E‐GARCH and expected shortfall measures of conditional volatility are consistent with mean reversion: high short term realizations of conditional volatility foreshadow a lower probability of extreme returns.     

Cyclical Changes in Firm Volatility

We characterize trends and cycles in the volatility of U.S. firms using a measure that we argue more cleanly captures firm‐specific volatility in sales and earnings growth than standard measures do. While earlier literature has emphasized a trend increase in the volatility of publicly traded firms, we find that a typical publicly traded firm has become more stable. We find that the negative association between firm‐specific volatility and the business cycle is weaker than earlier research based on dispersion measures suggests. We find that during the Great Recession of 2007–2009, firm‐specific volatility increased moderately but never substantially exceeded its sample mean. Our results are inconsistent with the hypothesis that firm‐specific volatility is an important driver of the business cycle, as it theoretically could be through an effect of default risk on credit spreads.     

ASYMMETRIC COVARIANCE,VOLATILITY, AND THE EFFECT OF NEWS

We propose that covariance (rather than beta) asymmetry provides a superior framework for examining issues related to changing risk premiums. Accordingly, we investigate whether the conditional covariance between stock and market returns is asymmetric in response to good and bad news. Our model of conditional covariance accommodates both the sign and magnitude of return innovations, and we find significant covariance asymmetry that can explain, at least in part, the volatility feedback of stock returns. Our findings are consistent across firm size, firm leverage, and temporal and cross‐sectional aggregations.     

Risk and the January effect

We use a time-series GARCH framework with the conditional variance/covariance as proxies for systematic risk to reexamine the proposition by Rozeff and Kinney (1976) and Rogalski and Tinic (1986) that the January effect may be a phenomenon of risk compensation in the month. We find no clear evidence that either conditional volatility or unconditional volatility in January is predominantly higher across the sampling years. Hence, against the proposition, the January effect is not due to risk per se. Rather, we find strong evidence that the January effect is due to higher compensation for risk in the month. This may be possible if investors have an increasing RRA utility function. Although many studies find that volatility tends to be higher in January, we find it to be period-specific and mostly in value-weighted return series, but not in equal-weighted return series. This is true both for the unconditional and conditional return volatility.     

Asymmetric volatility and risk in equity markets

It appears that volatility in equity markets is asymmetric:returns and conditional volatility are negatively correlated.We provide a unified framework to simultaneously investigateasymmetric volatility at the firm and the market level and toexamine two potential explanations of the asymmetry: leverageeffects and volatility feedback. Our empirical application usesthe market portfolio and portfolios with different leverageconstructed from Nikkei 225 stocks. We reject the pure leveragemodel of Christie (1982) and find support for a volatility feedbackstory. Volatility feedback at the firm level is enhanced bystrong asymmetries in conditional covariances. Conditional betasdo not show significant asymmetries. We document the risk premiumimplications of these findings.     

Implied Volatility in the U.K. Commercial Property Market: Empirical Evidence Based on Transaction Data

The primary aim of this research is to compute implied volatility based on a stochastic contingent claim valuation model proposed by Dixit and Pindyck (1994). Over the sample period of 1984 to 1997, and with approximately 20,000 commercial property transactions in the United Kingdom, we find that implied volatility of rental returns is in the region of 24.83 percent. Over the same sample period, the historical and conditional standard deviations of the log returns of transaction-based rental series is estimated to be 15.60 percent and 35.64 percent, respectively. The tests of information content of these risk measures show that there is strong orthogonality in the information impounded in implied volatility estimates compared to that contained in historical standard deviations.     

Dynamic factor long memory volatility

In this paper, we develop a long memory orthogonal factor (LMOF) multivariate volatility model for forecasting the covariance matrix of financial asset returns. We evaluate the LMOF model using the volatility timing framework of Fleming et al. [J. Finance, 2001, 56, 329–352] and compare its performance with that of both a static investment strategy based on the unconditional covariance matrix and a range of dynamic investment strategies based on existing short memory and long memory multivariate conditional volatility models. We show that investors should be willing to pay to switch from the static strategy to a dynamic volatility timing strategy and that, among the dynamic strategies, the LMOF model consistently produces forecasts of the covariance matrix that are economically more useful than those produced by the other multivariate conditional volatility models, both short memory and long memory. Moreover, we show that combining long memory volatility with the factor structure yields better results than employing either long memory volatility or the factor structure alone. The factor structure also significantly reduces transaction costs, thus increasing the feasibility of dynamic volatility timing strategies in practice. Our results are robust to estimation error in expected returns, the choice of risk aversion coefficient, the estimation window length and sub-period analysis.     

Earnings Volatility and Earnings Prediction: Analysis and UK Evidence

This paper confirms that US evidence of a negative relationship between earnings persistence and earnings volatility applies to UK firms over the period 1991–2010. Our analytical framework highlights the possibility that this result may reflect downward estimation bias in earnings persistence (and persistence of cash flow and accruals components of earnings) related to transitory earnings elements. Out‐of‐sample forecasts, based on models estimated for earnings volatility quartiles, suggest significant improvement in earnings forecasts for lower volatility firms. The results also suggest that the negative association between earnings persistence and volatility may be due to both estimation bias and variation in core earnings persistence.     

The impact of news on the volatility of ESG firms

This study provides one of the first empirical investigations of asymmetric volatility for environmental, social, and governance (ESG) investing. Using the Morgan Stanley Capital International (MSCI) indices as proxies for ESG test assets, this study investigates volatility risk for the highest ESG-rated firms through an empirical analysis in assessing how good news and bad news impact the risk of ESG firms. The analysis provides empirical evidence in support of the hypothesis that the impact of news on the volatility of ESG firms is larger for bad news, compared to good news. Employing an EGARCH framework, the analysis also finds that, in response to bad news, the observed volatility increases for small size ESG firms is lower compared to large and mid-cap ESG firms. The findings provide evidence of a slow response by small size firms to news in an ESG context. In modeling the conditional volatility of the ESG test assets, the analysis also provides evidence of higher persistence in the conditional volatility dynamics for small size ESG firms.     

波动溢出网络视角的金融风险传染研究

当前各类经济风险交叉关联,金融系统的风险溢出效应备受关注,为刻画我国金融系统性风险传染的路径特征,本文从波动溢出网络的视角分析金融系统内部的风险传染机制。首先使用广义动态因子模型对收益波动的共同波动率成分和特质性波动率成分进行区分。然后,根据货币市场、资本市场、大宗商品交易市场、外汇市场、房地产市场和黄金市场之间的特质性波动溢出效应,利用基于TVP-VAR模型的方差分解溢出指数分析金融系统波动溢出的动态联动性和风险传递机制。在分析方向性波动溢出效应的基础上,采用方差分解网络方法构建起信息溢出复杂网络,从网络视角分析金融系统内部的风险传染特征。实证研究发现,房地产市场和外汇市场的净溢出效应绝对值相较于其他市场更大,其受其他市场风险冲击的影响强于对外风险溢出效应,而股票市场的单向对外风险溢出效应强度最大。在波动溢出的基础上,进一步考虑股市波动率指数与其他市场波动率指数进行投资组合的资产配置权重,计算了波动率指数投资组合的最优组合权重和对冲策略。研究结论有助于更好地理解我国金融系统的风险传染机制,对监管机构加强宏观审慎监管、投资者规避投资风险具有重要意义。     

Designing a Countercyclical Insurance Program for Systemic Risk

This article proposes a framework for measuring and managing systemic risk. Current solvency regulations have been criticized for their focus on individual firms rather than the system as a whole. We show how an insurance program can be designed to deal with systemic risk through a risk charge on participating institutions. The risk charge is based on the generalized co‐conditional tail expectation, a conditional risk measure adapted from conditional value‐at‐risk. Current regulations have been criticized on the grounds that their capital requirements are procyclical. They require extra capital in periods of extreme stress thus exacerbating a crisis. We show how to construct a countercyclical risk charge and illustrate the approach using a numerical example.     

Credit Derivatives and the Default Risk of Large Complex Financial Institutions

This paper proposes and implements a multivariate model of the coevolution of the first and second moments of two broad credit default swap indices and the equity prices of sixteen large complex financial institutions. We use this empirical model to build a bank default risk model, in the vein of the classic Merton-type, which utilises a multi-equation framework to model forward-looking measures of market and credit risk using the credit default swap (CDS) index market as a measure of the conditions of the global credit environment. In the first step, we estimate the dynamic correlations and volatilities describing the evolution of the CDS indices and the banks’ equity prices and then impute the implied assets and their volatilities conditional on the evolution and volatility of equity. In the second step, we show that there is a substantial ‘asset shortfall’ and that substantial capital injections and/or asset insurance are required to restore the stability of our sample institutions to an acceptable level following large shocks to the aggregate level of credit risk in financial markets.     

Does It Pay to Bet Against Beta? On the Conditional Performance of the Beta Anomaly

Prior studies find that a strategy that buys high‐beta stocks and sells low‐beta stocks has a significantly negative unconditional capital asset pricing model (CAPM) alpha, such that it appears to pay to “bet against beta.” We show, however, that the conditional beta for the high‐minus‐low beta portfolio covaries negatively with the equity premium and positively with market volatility. As a result, the unconditional alpha is a downward‐biased estimate of the true alpha. We model the conditional market risk for beta‐sorted portfolios using instrumental variables methods and find that the conditional CAPM resolves the beta anomaly.     

Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach

We propose a method for estimating Value at Risk (VaR) and related risk measures describing the tail of the conditional distribution of a heteroscedastic financial return series. Our approach combines pseudo-maximum-likelihood fitting of GARCH models to estimate the current volatility and extreme value theory (EVT) for estimating the tail of the innovation distribution of the GARCH model. We use our method to estimate conditional quantiles (VaR) and conditional expected shortfalls (the expected size of a return exceeding VaR), this being an alternative measure of tail risk with better theoretical properties than the quantile. Using backtesting of historical daily return series we show that our procedure gives better 1-day estimates than methods which ignore the heavy tails of the innovations or the stochastic nature of the volatility. With the help of our fitted models we adopt a Monte Carlo approach to estimating the conditional quantiles of returns over multiple-day horizons and find that this outperforms the simple square-root-of-time scaling method.     

The Political Economy of Volatility Dynamics in the Hong Kong Stock Market

Despite its obvious importance, little empirical research has examined the impact of political risk on stock market volatility. This paper uses data on the Hong Kong stock market over a long sample period to investigate whether political risk has induced regime shifts in stock market volatility. Regime shifts are modelled via a Markov switching EGARCH model that allows for regime-dependent volatility asymmetry. We find strong evidence of regime shifts in conditional volatility as well as significant volatility asymmetry in high volatility periods. Major political uncertainties were reflected in a switch to the high-volatility regime. However, contrary to popular perceptions, we find no evidence that the Hong Kong stock market has become persistently more volatile since the start of Sino-British political negotiations in 1982.     

Macroeconomic news and treasury futures return volatility: Do treasury auctions matter?

Various macroeconomic announcements are known to influence asset price volatility. In addition to non-farm payrolls, we highlight the importance of Treasury auctions – a news event that has grown in importance due to ongoing Federal deficits. The occurrence of an auction, which increases supply in the underlying cash market, pushes futures prices lower and volatility higher. Conversely, a higher bid-to-cover ratio, indicates greater demand for Treasury securities, increases Treasury futures prices and lowers volatility. The response is consistent with market participants using futures to manage inventory risk. The results are consistent across a set of volatility estimates, and in an alternate conditional volatility framework.     

What Makes a Stock Risky? Evidence from Sell‐Side Analysts' Risk Ratings

We examine the determinants and the informativeness of financial analysts' risk ratings using a large sample of research reports issued by Salomon Smith Barney, now Citigroup, over the period 1997–2003. We find that the cross‐sectional variation in risk ratings is largely explained by variables commonly viewed as measures of risk, such as idiosyncratic risk, size, book‐to‐market, and leverage. In addition, earnings‐based measures of risk, such as earnings quality and accounting losses, also contribute to explaining the cross‐sectional variation in the risk ratings. Finally, we document that the risk ratings can be used to predict future return volatility after controlling for other predictors of future volatility. We conclude that analysts play an important role as providers of information about investment risk.     

Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets

We use the All Ordinaries Index and the corresponding Share Price Index futures contract written against the All Ordinaries Index to estimate optimal hedge ratios, adopting several specifications: an ordinary least squares‐based model, a vector autoregression, a vector error‐correction model and a diagonal‐vec multivariate generalized autoregressive conditional heteroscedasticity model. Hedging effectiveness is measured using a risk‐return comparison and a utility maximization method. We find that time‐varying generalized autoregressive conditional heteroscedasticity hedge ratios perform better than constant hedge ratios in terms of minimizing risks, but when return effects are also considered, the utility‐based measure prefers the ordinary least squares method in the in‐sample hedge, whilst both approaches favour the conditional time‐varying multivariate generalized autoregressive conditional heteroscedasticity hedge ratio estimates in out‐of‐sample analyses.     

The relationship between stock returns and volatility in international stock markets

This study examines the relationship between expected stock returns and volatility in the 12 largest international stock markets during January 1980 to December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in 6 out of the 12 markets. The results lend some support to the recent claim [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42; Whitelaw, R., 2000. Stock market risk and return: an empirical equilibrium approach. Review of Financial Studies 13, 521–547] that stock market returns are negatively correlated with stock market volatility.