On the Statistical Significance of Event Effects on Unsystematic Volatility

We develop a method for determining the significance of the effect of a certain event (stock split, corporate restructuring, change in regulation, etc.) on unsystematic volatility of asset returns. Simulations show that the suggested tests reject the true null hypothesis of no effect on volatility at appropriate levels, whereas the rejection rates of a false null hypothesis increase with the magnitude of the effect. An application of the method to corporate spin‐offs reveals statistically significant and long‐lasting estimated increases in unsystematic volatility of parent companies' returns.     

The Operating Performance of Firms that Switch Their Stock Listings

In this article we examine the operating performance of stocks that switch from NASDAQ to the American Stock Exchange (AMEX) or the New Stock Exchange (NYSE) and from AMEX to the NYSE. Specifically, we investigate whether post‐listing operating performance is consistent with the reported negative long‐term drift of post‐listing stock returns and whether there is evidence of self‐selection of the listing time. We find evidence of negative post‐listing changes in operating return on assets and sales, which, on a match‐adjusted basis, are significant for the relatively small NASDAQ stocks switching to AMEX. We also find evidence that firms self‐select the time of listing changes.     

Nonparametric rank tests for event studies

Because stock prices are not normally distributed, the power of nonparametric rank tests dominate parametric tests in event study analyses of abnormal returns on a single day. However, problems arise in the application of nonparametric tests to multiple day analyses of cumulative abnormal returns (CARs) that have caused researchers to normally rely upon parametric tests. In an effort to overcome this shortfall, this paper proposes a generalized rank (GRANK) testing procedure that can be used on both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived, and their empirical properties are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to popular parametric tests.     

Nonparametric correlation models for portfolio allocation

This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis.     

Volatility spillovers and the effect of news announcements

We examine the effect of US and European news announcements on the spillover of volatility across US and European stock markets. Using synchronously observed international implied volatility indices at a daily frequency, we find significant spillovers of implied volatility between US and European markets as well as within European markets. We observe a stark contrast in the effect of scheduled versus unscheduled news releases. Scheduled (unscheduled) news releases resolve (create) information uncertainty, leading to a decrease (increase) in implied volatility. Nevertheless, news announcements do not fully explain the volatility spillovers, although they do affect the magnitude of volatility spillovers. Our results are robust to extreme market events such as the recent financial crisis and provide evidence of volatility contagion across markets.     

RISK PREMIUM EFFECTS ON IMPLIED VOLATILITY REGRESSIONS

This article provides new insights into the sources of bias of option implied volatility to forecast its physical counterpart. We argue that this bias can be attributed to volatility risk premium effects. The latter are found to depend on high‐order cumulants of the risk‐neutral density. These cumulants capture the risk‐averse behavior of investors in the stock and option markets for bearing the investment risk that is reflected in the deviations of the implied risk‐neutral distribution from the normal distribution. We show that the bias of implied volatility to forecast its corresponding physical measure can be eliminated when the implied volatility regressions are adjusted for risk premium effects. The latter are captured mainly by the third‐order risk‐neutral cumulant. We also show that a substantial reduction of higher order risk‐neutral cumulants biases to predict their corresponding physical cumulants is supported when adjustments for risk premium effects are made.     

Disentangling the effect of jumps on systematic risk using a new estimator of integrated co-volatility

We propose a new threshold–pre-averaging realized estimator for the integrated co-volatility of two assets using non-synchronous observations with the simultaneous presence of microstructure noise and jumps. We derive a noise-robust Hayashi–Yoshida estimator that allows for very general structure of jumps in the underlying process. Based on the new estimator, different aspects and components of co-volatility are compared to examine the effect of jumps on systematic risk using tick-by-tick data from the Chinese stock market during 2009–2011. We find controlling for jumps contributes significantly to the beta estimation and common jumps mostly dominate the jump’s effect, but there is also evidence that idiosyncratic jumps may lead to significant deviation. We also find that not controlling for noise and jumps in previous realized beta estimations tend to considerably underestimate the systematic risk.     

Performance of technical analysis in growth and small cap segments of the US equity market

A large universe of technical trading rules applied to a set of technology industry and small cap sector portfolios over the 1995-2010 period yields superior predictability after adjusting for data snooping bias in the first half of the sample period and delivers statistically significant profits for a number of portfolios when the transaction cost is assumed to be of small to moderate size. Technical analysis is not able to outperform the buy-and-hold approach for any portfolio in the set in the second half of the sample period. The finding that the short-term return predictability becomes much weaker in the more recent period suggests that the underlying segments of the equity market have become more efficient over time. The fact that mechanical trading strategies have been futile after adjusting for data snooping bias for two samples of portfolios where technical analysis is most anticipated to succeed suggests that it is unlikely to have delivered abnormal returns in any other segment of the domestic equity market in the last decade.     

Volatility risk and the value premium: Evidence from the French stock market

This paper documents that systematic volatility risk is an important factor that drives the value premium observed in the French stock market. Using returns on at-the-money straddles written on the CAC 40 index as a proxy for systematic volatility risk, I document significant differences between volatility factor loadings of value and growth stocks. Furthermore, when markets are classified into expected booms and recessions, volatility factor loadings are also time-varying. When expected market risk premium is above its average, i.e. during expected recessions, value stocks are seen riskier than their growth counterparts. This implies in bad times, investors shift their preferences away from value firms. Instead they use growth stocks as hedges against deteriorations in their wealth during those times. The findings are in line with the predictions of rational asset pricing theory and support a “flight-to-quality” explanation.     

Volatility and covariation of financial assets: A high-frequency analysis

Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance–covariance matrix of n assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true efficient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructure noise affects portfolio decisions and calculations of the equity beta in a CAPM setting.     

World War II events and the Dow Jones industrial index

This paper investigates which events of World War II (WWII) the US investors (at that time) considered as turning points (structural breaks) of the war. The empirical study employs daily Dow Jones industrial average stock index and volatility from January 1939 to December 1945 and applies structural shift oriented test to determine endogenously the structural breaks during the WWII period. Results show that the majority of the wartime events (on and off the battlefield) labelled important by historians did result in structural breaks in both price movement and stock returns volatility (risk). These results have major implications for investors of the present and future.     

Dynamic effects of idiosyncratic volatility and liquidity on corporate bond spreads

We study the dynamic impact of idiosyncratic volatility and bond liquidity on corporate bond spreads over time and empirically disentangle both effects. Using an extensive data set, we find that both idiosyncratic volatility and liquidity are critical mainly for the distress portfolios, i.e., low-rated and short-term bonds; for others only volatility matters. The effects of volatility and liquidity shocks on bond spreads were both exacerbated during the recent financial crisis. Liquidity shocks are quickly absorbed into bonds prices; however, volatility shocks are more persistent and have a long-term effect. Our results overall suggest significant differences between how volatility and liquidity dynamically impact bond spreads.     

The S&P500 index effect reconsidered: Evidence from overnight and intraday stock price performance and volume

This study focuses on S&P500 inclusions and deletions, examining the impact of potential overnight price adjustment after the announcement of an S&P500 index change. We find evidence of a significant overnight price change that diminishes the returns available to speculators although there are still profits available from the first day after announcement until a few days after the actual event. More importantly, observing the tick-by-tick stock price performance and volume effects on the key days during the event window for the first time, we find evidence of consistent trading patterns during trading hours. A separate analysis of NASDAQ and NYSE listed stocks allows for a detailed examination of the price and volume effect at an intra-day level. We find that index funds appear to cluster their rebalancing activities near to and after the close on the event date, suggesting that they are more concerned with tracking error than profit.     

The impact of naked short selling on the securities lending and equity market

This paper examines the impact of naked short selling on equity markets where it is restricted to securities on an approved list. Consistent with Miller's (1977) intuition, stocks with the highest dispersion of opinions and short sale constraints are the only stocks to exhibit significant and negative abnormal returns in the post-event period. We also find slightly higher stock return volatility and a small reduction in liquidity when naked short sales are allowed. Overall, it impairs market quality (liquidity and volatility), although there appears to be some improvement in price efficiency in stocks with high short sale constraints.     

Public information arrival: Price discovery and liquidity in electronic limit order markets

How information is translated into market prices is still an open question. This paper studies the impact of newswire messages on intraday price discovery, liquidity, and trading intensity in an electronic limit order market. We take an objective ex ante measure of the tone of a message to study the impacts of positive, negative, and neutral messages on price discovery and trading activity. As expected, we find higher adverse selection costs around the arrival of newswire messages. Negative messages are associated with higher adverse selection costs than positive or neutral messages. Liquidity increases around positive and neutral messages and decreases around negative messages. Available order book depth as well as the trading intensity increases around all news. Our results suggest that market participants possess different information gathering and processing capabilities and that negative news messages are particularly informative and induce stronger market reactions.     

Liquidity and market efficiency: A large sample study

Chordia et al. (2008, hereafter CRS) examine short horizon return predictability from past order flows of large, actively traded NYSE firms across three tick size regimes and conclude that higher liquidity facilitates arbitrage trading which enhances market efficiency. We extend CRS to a comprehensive sample of all NYSE firms and examine the dynamics between liquidity and market efficiency during informational periods. Our results indicate that although all NYSE firms experience an overall improvement in market efficiency across periods of different tick size regimes, this improvement varies significantly across the portfolios of sample companies formed on the basis of trading frequency, market capitalization, and trading volume. After controlling for these factors, we further document a positive association between a continuous measure of liquidity and market efficiency, and show that this effect is amplified during periods that contain new information, as reflected in high adverse selection component of the bid-ask spread.     

The Nasdaq-Amex Merger, Nasdaq Reforms, and the Liquidity of Small Firms

After the Nasdaq and American Stock Exchange (AMEX) merged in 1998, officials of the new entity argued that some “smaller, harder to trade” companies on Nasdaq should switch to AMEX to improve liquidity. This recommendation is based on the traditional view among academics and practitioners alike that a substantial trading cost reduction should be realized when a company switches from the multidealer Nasdaq system to the AMEX specialist system. However, in light of the 1997 Nasdaq reforms, we reexamine the validity of these arguments using data from 1996–98 on firms that switch from the Nasdaq to the AMEX or the New York Stock Exchange. Evidence from transaction costs, volatility, and stock returns shows declining benefits to switching during the sample period. Our findings indicate that the liquidity improvement from exchange listing is limited in the wake of the Nasdaq reforms of 1997.     

Price Momentum and Idiosyncratic Volatility

We find that returns to momentum investing are higher among high idiosyncratic volatility ( IVol) stocks, especially high IVol losers. Higher IVol stocks also experience quicker and larger reversals. The findings are consistent with momentum profits being attributable to underreaction to firm‐specific information and with IVol limiting arbitrage of the momentum effect. We also find a positive time‐series relation between momentum returns and aggregate IVol. Given the long‐term rise in IVol, this result helps explain the persistence of momentum profits since Jegadeesh and Titman's (1993) study.     

Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices

We address the question whether the evolution of implied volatility can be forecasted by studying a number of European and US implied volatility indices. Both point and interval forecasts are formed by alternative model specifications. The statistical and economic significance of these forecasts is examined. The latter is assessed by trading strategies in the recently inaugurated CBOE volatility futures markets. Predictable patterns are detected from a statistical point of view. However, these are not economically significant since no abnormal profits can be attained. Hence, the hypothesis that the volatility futures markets are efficient cannot be rejected.     

THE ECONOMIC VALUE OF USING REALIZED VOLATILITY IN FORECASTING FUTURE IMPLIED VOLATILITY

We examine the economic benefits of using realized volatility to forecast future implied volatility for pricing, trading, and hedging in the S&P 500 index options market. We propose an encompassing regression approach to forecast future implied volatility, and hence future option prices, by combining historical realized volatility and current implied volatility. Although the use of realized volatility results in superior performance in the encompassing regressions and out-of-sample option pricing tests, we do not find any significant economic gains in option trading and hedging strategies in the presence of transaction costs.