Asymmetric Volatility and Trading Activity in Index Futures Options

We examine the impact of option trading activity on implied volatility changes to returns in the index futures option market. Controlling for option moneyness, delta‐to‐option‐premium ratio, and liquidity, we find that net buying pressure, profit‐maximization behavior, and liquidity are interrelated and affect asymmetric responses of implied volatilities to returns. Implied volatilities of options with more liquidity, a higher exercise price, and a higher delta‐to‐option‐premium ratio have the most profound asymmetric response.     

Corporate Bond Returns and Volatility

Recent literature emphasizes the relation of stock volatility to corporate bond yields. We demonstrate that during 1996–2005 corporate bond excess return volatility is directly related to contemporaneous corporate bond excess returns. In fact, the decompositions of aggregate bond volatility have a higher contemporaneous correlation with bond yields in comparison to idiosyncratic stock risk. Additionally, bond volatility and idiosyncratic risk are significant predictors of corporate three‐month and six‐month ahead bond excess returns. We also find that corporate bond volatility contains both slow moving and time‐varying components.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

A Better Asymmetric Model of Changing Volatility in Stock and Exchange Rate Returns: Trend-GARCH

Abstract

The impact of short run price trending on the conditional volatility is tested empirically. A new family of conditionally heteroscedastic models with a trend-dependent conditional variance equation: The Trend-GARCH model is described. Modern microeconomic theory often suggests the connection between the past behaviour of time series, the subsequent reaction of market individuals, and thereon changes in the future characteristics of the time series. Results reveal important properties of these models, which are consistent with stylized facts found in financial data sets. They can also be employed for model identification, estimation, and testing. The empirical analysis supports the existence of trend effects. The Trend-GARCH model proves to be superior to alternative models such as EGARCH, AGARCH, TGARCH OR GARCH-in-Mean in replicating the leverage effect in the conditional variance, in fitting the news impact curve and in fitting the volatility estimates from high frequency data. In addition, we show that the leverage effect is dependent on the current trend, i.e. it differentiates between bullish and bearish markets. Furthermore, trend effects can account for a significant part of the long memory property of asset price volatilities.     

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.     

Returns and option activity over the option-expiration week for S&P 100 stocks

For S&P 100 stocks, we find that the weekly returns over option-expiration (OE) weeks (a month’s third-Friday week) tend to be high, relative to: (1) the third-Friday weekly returns of other stocks with less option activity, (2) the own stock’s other weekly returns, (3) the risk, based on asset-pricing alphas. For these same stocks, a month’s fourth-Friday weekly returns underperform modestly. We suggest the following two avenues are likely partial contributors towards understanding these return patterns: (1) delta-hedge rebalancing by option market makers, with a reduction in short-stock hedge positions over the OE week, and (2) declining risk perceptions over the OE week, as measured by option-derived implied volatilities. Our findings suggest option activity can induce reliable patterns in the weekly returns of option-active large-cap stocks.     

Asymmetric Volatility,Skewness, and Downside Risk in Different Asset Classes: Evidence from Futures Markets

This study examines the cross‐sectional variation of futures returns from different asset classes. The monthly returns are positively correlated with downside risk and negatively correlated with coskewness. The asymmetric volatility effect generates negatively skewed returns. Assets with high coskewness and low downside betas provide hedges against market downside risk and offer low returns. The high returns offered by assets with low coskewness and high downside betas are a risk premium for bearing downside risk. The asset pricing model that incorporates downside risk partially explains the futures returns. The results indicate a unified risk perspective to jointly price different asset classes.     

The cross-sectional relation between conditional heteroskedasticity,the implied volatility smile,and the variance risk premium

This paper estimates how the shape of the implied volatility smile and the size of the variance risk premium relate to parameters of GARCH-type time-series models measuring how conditional volatility responds to return shocks. Markets in which return shocks lead to large increases in conditional volatility tend to have larger variance risk premia than markets in which the impact on conditional volatility is slight. Markets in which negative (positive) return shocks lead to larger increases in future volatility than positive (negative) return shocks tend to have downward (upward) sloping implied volatility smiles. Also, differences in how volatility responds to return shocks as measured by GARCH-type models explain much, but not all, of the variations in excess kurtosis and multi-period skewness across different markets.     

The Lead‐Lag Relationship Between Cash and Stock Index Futures in a New Market

This paper investigates the lead‐lag relationship in daily returns and volatilities between price movements of the FTSE/ATHEX‐20 and FTSE/ATHEX Mid‐40 stock index futures and the underlying cash indices in the relatively new futures market of Greece. Empirical results show that there is a bi‐directional relationship between cash and futures prices. However, futures lead the cash index returns, by responding more rapidly to economic events than stock prices. This speed is much higher in the more liquid FTSE/ATHEX‐20 market. Moreover, results indicate that futures volatilities spill information over to the corresponding cash market volatilities in both investigated futures markets, but volatilities in the cash markets have no effect on the volatilities of futures markets. Overall, it seems that new market information is disseminated faster in the futures market compared to the stock market. This implies that the futures markets can be used as price discovery vehicles, providing further evidence that derivatives markets contribute to completing and stabilising capital markets in Greece. A further finding of this study is that futures volume and disequilibrium effects between cash and futures prices are important variables in the explanation of volatilities in cash and futures markets.     

Further insights on the relationship between SP500, VIX and volume: a new asymmetric causality test1

In the aim to explore the complex relationships between S&P500, VIX and volume we introduce a Granger causality test using the nonlinear statistic of Asymmetric Partial Transfer Entropy (APTE). Through a simulation exercise, it arises that the APTE offers precise information on the nature of the connectivity. Our empirical findings concretize the information flow that links volume, S&P500 and VIX, and merge the leverage effect and the asymmetric stock return-volume relationship into a unified framework of analysis. More specifically, when we condition on the tails, the detected causal channel provides empirical validation of the noise trading contribution to large swings in financial markets, because of the increase of trading volume and the subsequent worsening ability of market prices to adjust to new information.     

A New Method to Measure the Performance of Leveraged Exchange‐Traded Funds

We examine the effects of daily return compounding, financing costs, and management factors on the performance of leveraged exchange‐traded funds (LETFs) over various holding periods. We propose a new method to measure LETFs’ tracking errors that allows us to disentangle these effects. Our results show that the compounding effect generally has more influence on tracking errors than other factors, especially for long holding periods and in a “sideways” market. The explicit costs (i.e., the expense ratios) and other factors (e.g., financing costs) can materially affect the performance of LETFs, especially for those with high leverage ratios and bear funds.     

Empirical Analysis of Stock Returns and Volatility: Evidence from Seven Asian Stock Markets Based on TAR-GARCH Model

This paper investigates the time-series behavior of stock returns for seven Asian stock markets. In most cases, higher average returns appear to be associated with a higher level of volatility. Testing the relationship between stock returns and unexpected volatility, the evidence shows that four out of seven Asian stock markets have significant results. Further analyzing the relationship between stock returns and time-varying volatility by using Threshold Autoregressive GARCH(1,1)-in-mean specification indicates that the null hypothesis of no asymmetric effect on the conditional volatility is rejected for the daily data. However, the null cannot be rejected for the monthly data.     

The effect of macroeconomic news on beliefs and preferences: Evidence from the options market

We examine the effect of regularly scheduled macroeconomic announcements on the beliefs and preferences of participants in the U.S. Treasury market by comparing the option-implied state-price densities (SPDs) of bond prices shortly before and after the announcements. We find that the announcements reduce the uncertainty implicit in the second moment of the SPD regardless of the content of the news. The changes in the higher-order moments, in contrast, depend on whether the news is good or bad for economic prospects. We explore three alternative explanations for our empirical findings: relative mispricing, changes in beliefs, and changes in preferences. We find that our results are consistent with time-varying risk aversion.     

Risk‐based explanation for the book‐to‐market effect

This paper proposes a risk‐based explanation for the book‐to‐market (B/M) effect. I decompose B/M into net operating asset‐to‐market (NOA/M) and net financing asset‐to‐market (NFA/M) components. Portfolio analysis shows that (i) positive B/M, NOA/M and NFA/M are positively related to future returns and (ii) negative B/M, NOA/M and NFA/M are negatively related to future returns. To the extent that positive B/M, NOA/M and NFA/M act as measures of asset risk and negative B/M, NOA/M and NFA/M act as inverse measures of borrowing risk, the nonlinear relations between B/M, NOA/M and NFA/M and future returns provide some evidence to support the risk‐based explanation for the book‐to‐market effect in stock returns.     

Interaction of size,book‐to‐market and momentum effects in Australia

This study seeks to disentangle the effects of size, book‐to‐market and momentum on returns. Initial results show that each characteristic has a role in explaining returns, but that there is interaction between size and momentum, as well as between size and book‐to‐market. Three key findings emerge. First, the size premium is the strongest, particularly in the loser portfolios. Second, the value premium is generally limited to the smallest portfolios. Third, the momentum premium is evident for the large‐ and middle‐sized portfolios, but loser stocks significantly outperform winner stocks in the smallest size portfolio. When these interactions are controlled with multivariate regression, we find a significant negative average relation between size and returns, a significant positive average relation between book‐to‐market and returns, and a significant positive average relation between momentum and returns.     

Regime Shifts in Price‐Dividend Ratios and Expected Stock Returns: A Present‐Value Approach

We incorporate regime shifts in the mean of price‐dividend ratios into the present value model of van Binsbergen and Koijen (2010) who propose a latent variable approach to modeling expected returns and dividend growth rates. We find that accounting for regime shifts results in much lower persistence of expected returns and higher volatility of expected returns, and thus higher in‐sample predictability, when compared to the results from the van Binsbergen and Koijen (2010) model. We also show that the main source of the increase in the mean of price‐dividend ratios in the mid‐1990s is a decrease in the mean of expected returns.     

A Long‐Run Performance Perspective on the Technology Bubble

The events surrounding the stock price peak of March 2000 are commonly interpreted as the bursting of a technology or Internet bubble, with some researchers pointing out that the pattern could also arise in fundamental models. We inform the debate by studying the long‐run performance of Internet and technology stocks from March 2000 onward. Using calendar‐time regressions, we do not find conclusive evidence of negative abnormal returns. The results are consistent with a new interpretation of the events; namely, the price drop of the early 2000s was not warranted in light of future cash flows and risk.     

Conference Calls and Stock Price Volatility in the Post‐Reg FD Era

Past research has documented that the utilisation of conference calls is greater in the high tech sector than in other industries. Do high tech firms benefit from that? This study attempts to answer this question by examining the impact of ‘post‐Reg FD’ conference calls on the price volatility of high tech firms listed in the US market. We find evidence that more open conference calls results in lower idiosyncratic volatility.     

A compound option approach to model the interrelation between banking crises and country defaults: The case of Hungary 2008

We analyze the Hungarian financial crisis of 2008 in a stochastic framework that advances structural credit risk models for country defaults: by applying compound option theory we consider payments for bailing-out the banking sector together with debt service payments in a joint crisis model. We estimate the model parameters by applying the time series maximum-likelihood approach of Duan (1994) on yield spreads of Hungarian Bonds. We find that difficulties in acquiring funds for debt servicing in combination with high outstanding debt triggered the crisis, rather than problems in the domestic banking sector. The estimated crisis probabilities dramatically rise during 2008.     

Idiosyncratic volatility and cross‐sectional stock returns in Southeast Asian stock markets

We examine the role of idiosyncratic risk in five ASEAN markets of Malaysia, Singapore, Thailand, Indonesia, and the Philippines. Our research was motivated by the findings of Ang et al. (2006, 2009) of a ‘puzzling’ negative relation between idiosyncratic volatility and 1‐month ahead stock returns in developed markets and the suggestion of the ubiquity of these results in other markets. In contrast, we find no evidence of an idiosyncratic volatility puzzle in these Asian stock markets; instead, we document a positive relationship between idiosyncratic volatility and returns in Malaysia, Singapore, Thailand, and Indonesia and no relationship in the Philippines. The idiosyncratic volatility trading strategy could result in significant trading profits in Malaysia, Singapore, Thailand, and to some extent in Indonesia. Our study underscores the fact that generalizing empirical results obtained in developed stock markets to new and emerging markets could potentially be misleading.