Effects of Passive Intensity on Aggregate Price Dynamics

We find that passive intensity (PI), measured by the passive‐linked share of total stock market trading volume, is strongly related to the overall pattern of stock price movements. A one‐standard‐deviation increase in PI is associated with an 8% higher price synchronicity. We further investigate the channels through which this relation is established by separately analyzing its impact on aggregate systematic and idiosyncratic volatility of stock returns. PI has a positive effect on systematic volatility and a negative impact on firm‐specific volatility. Consistent with the effect of passive trading on price dynamics, we find evidence that PI is negatively associated with mutual funds alpha dissimilarity. After controlling for market and idiosyncratic volatility, a one‐standard‐deviation increase in PI corresponds to a 0.20% decrease in fund dissimilarity. Our findings are robust after controlling for various macro and corporate factors known to affect systematic or firm‐specific volatility.     

The Timing of Opening Trades and Pricing Errors

After demonstrating that a zero investment trading strategy that buys stocks with overnight returns below the market average and sells stocks with overnight returns above the market average earns more than 1% monthly profit, I demonstrate that this profit is greater for stocks that start trading more quickly than for other stocks. These results control for trading costs. The resulting pricing errors are a material portion of stock price volatility and suggest that a quick response to overnight information adds non‐information‐based stock volatility to stock prices.     

基本养老保险基金投资、股票收益率与股价波动性

以2017—2022年各季度基本养老保险基金投资数据为样本,考察基本养老保险基金投资对股票收益率与股价波动性的影响。结果显示：基本养老保险基金持股比例变化对股票未来收益率有一定预测效应,持股比例增加会加剧股价波动。异质性检验表明,被持股公司规模越大,持股比例变化对股票收益率的影响越不明显,对股价波动性影响的时滞性越强。对于短期持股而言,持股比例增加会加剧股价波动,而对于长期持股而言,持股比例增加有利于稳定股价。鉴于此,应继续推动基本养老保险基金全国统筹,进一步扩大其市场化投资规模、延长投资考核期限,提升基金可持续发展能力。     

Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds

We document a highly significant, strongly nonlinear dependence of stock and bond returns on past equity market volatility as measured by the VIX. We propose a new estimator for the shape of the nonlinear forecasting relationship that exploits variation in the cross‐section of returns. The nonlinearities are mirror images for stocks and bonds, revealing flight‐to‐safety: expected returns increase for stocks when volatility increases from moderate to high levels while they decline for Treasuries. These findings provide support for dynamic asset pricing theories in which the price of risk is a nonlinear function of market volatility.     

Volatility increases Subsequent to NYSE and AMEX Stock Splits

The post-split increase in daily returns volatility is less for AMEX stocks than for NYSE stocks. The exchange trading location is a significant factor in explaining the volatility shift even after stock price and firm size are considered. Furthermore, when measured on a weekly basis, there is no increase in AMEX stocks' returns volatility. These results suggest that measurement errors created by bid-ask spreads and the 1/8 effect, and also one or more of the elements that make the NYSE different from the AMEX, explain why the estimated volatility of daily stock returns increases after the ex split date.     

The Book-to-Price Effect in Stock Returns: Accounting for Leverage

This paper lays out a decomposition of book‐to‐price (B/P) that derives from the accounting for book value and that articulates precisely how B/P “absorbs” leverage. The B/P ratio can be decomposed into an enterprise book‐to‐price (that pertains to operations and potentially reflects operating risk) and a leverage component (that reflects financing risk). The empirical analysis shows that the enterprise book‐to‐price ratio is positively related to subsequent stock returns but, conditional upon the enterprise book‐to‐price, the leverage component of B/P is negatively associated with future stock returns. Further, both enterprise book‐to‐price and leverage explain returns over those associated with Fama and French nominated factors—including the book‐to‐price factor—albeit negatively so for leverage. The seemingly perverse finding with respect to the leverage component of B/P survives under controls for size, estimated beta, return volatility, momentum, and default risk.     

Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices

This paper examines the empirical performance of jump diffusion models of stock price dynamics from joint options and stock markets data. The paper introduces a model with discontinuous correlated jumps in stock prices and stock price volatility, and with state-dependent arrival intensity. We discuss how to perform likelihood-based inference based upon joint options/returns data and present estimates of risk premiums for jump and volatility risks. The paper finds that while complex jump specifications add little explanatory power in fitting options data, these models fare better in fitting options and returns data simultaneously.     

Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors

This paper proposes a two-state Markov-switching model for stock market returns in which the state-dependent expected returns, their variance and associated regime-switching dynamics are allowed to respond to market information. More specifically, we apply this model to examine the explanatory and predictive power of price range and trading volume for return volatility. Our findings indicate that a negative relation between equity market returns and volatility prevails even after having controlled for the time-varying determinants of conditional volatility within each regime. We also find an asymmetry in the effect of price range on intra- and inter-regime return volatility. While price range has a stronger effect in the high volatility state, it appears to significantly affect only the transition probabilities when the stock market is in the low volatility state but not in the high volatility state. Finally, we provide evidence consistent with the ‘rebound’ model of asset returns proposed by Samuelson (1991), suggesting that long-horizon investors are expected to invest more in risky assets than short-horizon investors.     

Volatility spreads and earnings announcement returns

Prior research documents that volatility spreads predict stock returns. If the trading activity of informed investors is an important driver of volatility spreads, then the predictability of stock returns should be more pronounced during major information events. This paper investigates whether the predictability of equity returns by volatility spreads is stronger during earnings announcements. Volatility spreads are measured by the implied volatility differences between pairs of strike price and expiration date matched put and call options and capture price pressures in the option market. During a two-day earnings announcement window, the abnormal returns to the quintile that includes stocks with relatively expensive call options is more than 1.5% greater than the abnormal returns to the quintile that includes stocks with relatively expensive put options. This result is robust after measuring volatility spreads in alternative ways and controlling for firm characteristics and lagged equity returns. The degree of announcement return predictability is stronger when volatility spreads are measured using more liquid options, the information environment is more asymmetric, and stock liquidity is low.     

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.     

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 Intraday Relation between NYSE and CBOE Prices

I extend the literature regarding price discovery across stock and option markets through an empirical model that allows information to flow through an error‐correction term and volatility. NYSE prices tend to lead CBOE prices by at least thirty minutes over the entire six‐year sample period. In addition, informed trading in the options market is revealed more strongly through persistence in volatility and the spillover of volatility to the stock market than it is through returns.     

Bubbles,Fads and Stock Price Volatility Tests: A Partial Evaluation

This is a summary and interpretation of some of the literature on stock price volatility that was stimulated by Leroy and Porter 28 and Shiller 40 . It appears that neither small-sample bias, rational bubbles nor some standard models for expected returns adequately explain stock price volatility. This suggests a role for some nonstandard models for expected returns. One possibility is a “fads” model in which noise trading by naive investors is important. At present, however, there is little direct evidence that such fads play a significant role in stock price determination.     

Option prices and stock market momentum: evidence from China

Option prices tend to be correlated to past stock market returns due to market imperfections. We unprecedentedly examine this issue on the SSE 50 ETF option in the Chinese derivatives market. To measure the price pressure in the options market, we construct an implied volatility spread based on pairs of the SSE 50 ETF option with identical expiration dates and strike prices. By regressing the implied volatility spread on past stock returns, we find that past stock returns exert a strong influence on the pricing of index options. Specifically, we find that SSE 50 ETF calls are significantly overvalued relative to SSE 50 ETF puts after stock price increases and the reverse is also true after the stock price decreases. Moreover, we validate the momentum effects in the underlying stock market to be responsible for the price pressure. These findings are both economically and statistically significant and have important implications.     

A High‐Frequency Investigation of the Interaction between Volatility and DAX Returns

One of the most noticeable stylised facts in finance is that stock index returns are negatively correlated with changes in volatility. The economic rationale for the effect is still controversial. The competing explanations have different implications for the origin of the relationship: Are volatility changes induced by index movements, or inversely, does volatility drive index returns? To differentiate between the alternative hypotheses, we analyse the lead‐lag relationship of option implied volatility and index return in Germany based on Granger causality tests and impulse‐response functions. Our dataset consists of all transactions in DAX options and futures over the time period from 1995 to 2005. Analyzing returns over 5‐minute intervals, we find that the relationship is return‐driven in the sense that index returns Granger cause volatility changes. This causal relationship is statistically and economically significant and can be clearly separated from the contemporaneous correlation. The largest part of the implied volatility response occurs immediately, but we also observe a smaller retarded reaction for up to one hour. A volatility feedback effect is not discernible. If it exists, the stock market appears to correctly anticipate its importance for index returns.     

DOES VOLATILITY DECREASE AFTER REVERSE STOCK SPLITS?

Previous research documents that volatility decreases after reverse stock splits. I show that measurement effects bias observed volatility, especially for lower priced stocks. Based on observed returns, volatility decreases 25% after reverse splits. Controlling for bid–ask bounce, volatility still decreases for stocks with prices above $5.00. However, for stocks below $2.00, volatility increases slightly. The portion of observed volatility attributable to measurement effects declines as the stock price increases and as the minimum tick size decreases. Finally, there is a significant and positive cross‐sectional relation between changes in the number of trades and changes in volatility after reverse splits.     

STOCK RETURNS AND VOLATILITY ON CHINA'S STOCK MARKETS

We examine time‐series features of stock returns and volatility, as well as the relation between return and volatility in four of China's stock exchanges. Variance ratio tests reject the hypothesis that stock returns follow a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time‐varying volatility and shows volatility is highly persistent and predictable. The results of GARCH‐M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns. JEL classification: G15     

The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility

We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.     

人民币汇率波动与中国股票价格报酬之间的相关性——基于2005年至2007年的实证分析

本文以2005年7月21日至2007年9月18日的中国股价与人民币兑美元的名义汇率数据,利用GARCH模型来探讨在这段时间内人民币汇率波动对中国股票价格报酬的影响。实证结果得知,在这段时间内人民币兑美元名义汇率波动是负向影响中国股票价格报酬的,也符合有价证券余额理论的主张;汇率市场对股票市场的影响在宏观决策中应予以高度重视。     

Does Risk Management Add Value? A Survey of the Evidence

The fact that 92% of the world's 500 largest companies recently reported using derivatives suggests that corporate managers believe financial risk management can increase shareholder value. Surveys of finance academics indicate that they too believe that corporate risk management is, on the whole, a valueadding activity. This article provides an overview of almost 30 years of broadbased, stock‐market‐oriented academic studies that address one or more of the following questions:

• ? Are interest rate, exchange rate, and commodity price risks reflected in stock price movements?
• ? Is volatility in corporate earnings and cash flows related in a systematic way to corporate market values?
• ? Is the corporate use of derivatives associated with reduced risk and higher market values?

The answer to the first question, at least in the case of financial institutions and interest rate risk, is a definite yes; all studies with this focus find that the stock returns of financial firms are clearly sensitive to interest rate changes. The stock returns of industrial companies exhibit no pronounced interest rate exposure (at least as a group), but industrial firms with significant cross‐border revenues and costs show considerable sensitivity to exchange rates (although such sensitivity actually appears to be reduced by the size and geographical diversity of the largest multinationals). What's more, the corporate use of derivatives to hedge interest rate and currency exposures appears to be associated with lower sensitivity of stock returns to interest rate and FX changes. But does the resulting reduction in price sensitivity affect value—and, if so, how? Consistent with a widely cited theory that risk management increases value by limiting the corporate “underinvestment problem,” a number of studies show a correlation between lower cash flow volatility and higher corporate investment and market values. The article also cites a small but growing group of studies that show a strong positive association between derivatives use and stock price performance (typically measured using price‐to‐book ratios). But perhaps the nearest the research comes to establishing causality are two studies—one of companies that hedge FX exposures and another of airlines' hedging of fuel costs—that show that, in industries where hedging with derivatives is common, companies that hedge outperform companies that don't.