The volatility effect of futures trading: Evidence from LSE traded stocks listed as individual equity futures contracts on LIFFE

This study investigates the impact of LIFFE's introduction of individual equity futures contracts on the risk characteristics of the underlying stocks trading on the LSE. We employ the Fama and French three-factor model (TFM) to measure the change in the systematic risk of the underlying stocks which arises subsequent to the introduction of futures contracts. A GJR-GARCH(1,1) specification is used to test whether the futures contract listing affects the permanent and/or the transitory component of the residual variance of returns, and a control sample methodology isolates changes in the risk components that may be caused by factors other than futures contract innovation. The observed increase (decrease) in the impact of current (old) news on the residual variance implies that futures contract listing enhances stock market efficiency. There is no evidence that futures innovation impacts on either the systematic risk or the permanent component of the residual variance of returns.     

The jump component of S&P 500 volatility and the VIX index

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing of past volatility to generate forecasts. Implied volatility has the potential to reflect information that a model-based forecast could not. This paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information pertaining to future jump activity relative to model-based forecasts. It is found that the VIX index both subsumes information relating to past jump contributions to total volatility and reflects incremental information pertaining to future jump activity. This issue has not been examined previously and expands our understanding of how option markets form their volatility forecasts.     

Maximum likelihood estimation of stochastic volatility models

We develop and implement a method for maximum likelihood estimation in closed-form of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by proxies based on the implied volatility of a short-dated at-the-money option. The approximation results in a small loss of accuracy relative to the standard errors due to sampling noise. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine Heston model and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.     

Does implied volatility provide any information beyond that captured in model-based volatility forecasts?

This paper contributes to our understanding of the informational content of implied volatility. Here we examine whether the S&P 500 implied volatility index (VIX) contains any information relevant to future volatility beyond that available from model based volatility forecasts. It is argued that this approach differs from the traditional forecast encompassing approach used in earlier studies. The findings indicate that the VIX index does not contain any such additional information relevant for forecasting volatility.     

The information content of implied volatilities and model-free volatility expectations: Evidence from options written on individual stocks

We measure the volatility information content of stock options for individual firms using option prices for 149 US firms and the S&P 100 index. We use ARCH and regression models to compare volatility forecasts defined by historical stock returns, at-the-money implied volatilities and model-free volatility expectations for every firm. For 1-day-ahead estimation, a historical ARCH model outperforms both of the volatility estimates extracted from option prices for 36% of the firms, but the option forecasts are nearly always more informative for those firms that have the more actively traded options. When the prediction horizon extends until the expiry date of the options, the option forecasts are more informative than the historical volatility for 85% of the firms. However, at-the-money implied volatilities generally outperform the model-free volatility expectations.     

The day of the week effect on stock market volatility and volume: International evidence

This study investigates the day of the week effect on the volatility of major stock market indexes for the period of 1988 through 2002. Using a conditional variance framework, we find that the day of the week effect is present in both return and volatility equations. The highest volatility occurs on Mondays for Germany and Japan, on Fridays for Canada and the United States, and on Thursdays for the United Kingdom. For most of the markets, the days with the highest volatility also coincide with that market's lowest trading volume. Thus, this paper supports the argument made by Foster and Viswanathan [Rev. Financ. Stud. 3 (1990) 593] that high volatility would be accompanied by low trading volume because of the unwillingness of liquidity traders to trade in periods of high stock market volatility.     

Information demand and stock market volatility

We study information demand and supply at the firm and market level using data for 30 of the largest stocks traded on NYSE and NASDAQ. Demand is approximated in a novel manner from weekly internet search volume time series drawn from the recently released Google Trends database. Our paper makes contributions in four main directions. First, although information demand and supply tend to be positively correlated, their dynamic interactions do not allow conclusive inferences about the information discovery process. Second, demand for information at the market level is significantly positively related to historical and implied measures of volatility and to trading volume, even after controlling for market return and information supply. Third, information demand increases significantly during periods of higher returns. Fourth, analysis of the expected variance risk premium confirms for the first time empirically the hypothesis that investors demand more information as their level of risk aversion increases.     

The day-of-the-week effect and conditional volatility: Sensitivity of error distributional assumptions

We test for reliable evidence of the day-of-the-week effect on both the mean and volatility for the S&P/TSX Canadian return index. Unlike previous studies, we permit several specifications for the error distribution — GARCH normal, Student's t, generalized error distribution, and double exponential distribution. Unlike other studies, we find that the day-of-the-week effect in both mean and conditional volatility is sensitive to the particular specification of the underlying distributions. We also find that using a regression analysis assuming a Student's t distribution is a better way to investigate this effect. Our evidence demonstrates the apparent fragility of previous empirical studies on calendar anomalies. Thus, our results serve as a warning that with financial data, the error distributional assumptions are critical to correctly identifying empirical regularities in the data.     

Are survey forecasts of individual and institutional investor sentiments rational?

We examine the effects of rational risk factors on investor sentiments. We find that institutional investor sentiments are more rational than individual investor sentiments. There are significant positive effects of, market return and dividend yield and negative effect of inflation on both types of sentiments. These risk factors have stronger effects on institutional than individual investor sentiments. Also, there are significant effects of term spread and HML on the institutional investor sentiments. The evidence suggests that linkages between sentiments and stock return stems from a combination of rational outlook and noise i.e. expectations that are not fully justified by information.     

Dynamics and causality in industry-specific volatility

This paper presents comprehensive empirical evidence on the dynamics and causality within 30 US industry-specific volatilities during July 1963 and June 2008. We find that linear trends are present in 17 of the 30 industry volatilities. Granger-causality tests reveal that the industry of business supplies and the industry of finance are the most important lead indicators of industry volatilities. To uncover contemporaneous causal relationships in the market, we implement an emerging data-driven method of directed acyclic graphs. The results suggest that volatility shocks originating from business supplies, machinery, and consumer goods industries are sources of risks that affect most other industries. By contrast, volatilities in the two traditionally important industries, oil and autos, do not appear to have a substantial influence on other large industries in the contemporaneous time. Finally, business equipment and services, both containing information technology components, are the most important driving forces of the industry volatility surge in the late 1990s.     

The leverage effect puzzle: Disentangling sources of bias at high frequency

The leverage effect refers to the generally negative correlation between an asset return and its changes of volatility. A natural estimate consists in using the empirical correlation between the daily returns and the changes of daily volatility estimated from high frequency data. The puzzle lies in the fact that such an intuitively natural estimate yields nearly zero correlation for most assets tested, despite the many economic reasons for expecting the estimated correlation to be negative. To better understand the sources of the puzzle, we analyze the different asymptotic biases that are involved in high frequency estimation of the leverage effect, including biases due to discretization errors, to smoothing errors in estimating spot volatilities, to estimation error, and to market microstructure noise. This decomposition enables us to propose novel bias correction methods for estimating the leverage effect.     

Consumption, the persistence of shocks, and asset price volatility

In a general equilibrium setting, a temporary component in consumption introduces a wedge between the volatility of equity returns and the volatility of consumption growth. This paper explores the asset pricing consequences of this property in a model in which consumption is the sum of a permanent and a transitory component. Permanent shocks are assumed to be rare events, while transitory shocks follow a diffusion process. When calibrated to US annual data, the model matches first and second moments of equity and bond returns for preference parameters within acceptable bounds. Permanent and transitory shocks together explain the equity premium, while transitory shocks alone explain the excess volatility of returns.     

Stock market volatility around national elections

This paper investigates a sample of 27 OECD countries to test whether national elections induce higher stock market volatility. It is found that the country-specific component of index return variance can easily double during the week around an election, which shows that investors are surprised by the election outcome. Several factors, such as a narrow margin of victory, lack of compulsory voting laws, change in the political orientation of the government, or the failure to form a government with parliamentary majority significantly contribute to the magnitude of the election shock. Furthermore, some evidence is found that markets with short trading history exhibit stronger reaction. Our findings have important implications for the optimal strategies of institutional and individual investors who have direct or indirect exposure to volatility risk.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.     

Stock returns and volatility following the September 11 attacks: Evidence from 53 equity markets

September 11 attacks matter, and why not? Given that globalization has integrated financial markets, the magnitudes of the effect of the September 11 attacks on global markets are expected to be pervasive. We used data from 53 equity markets to investigate the short term impact of the September 11 attacks on markets' returns and volatility. Our empirical findings indicate that the impact of the attacks resulted in significant increases in volatility across regions and over the study period. However, stock returns experienced significant negative returns in the short-run but recovered quickly afterwards. Nevertheless, we find that the impact of the attacks on financial markets varied across regions. The implication here is that the less integrated regions (e.g., Middle East and North Africa) are with the international economy, the less exposed they are to shocks.     

The impact of macroeconomic news on quote adjustments, noise, and informational volatility

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. We decompose bid and ask returns into a common (“efficient return”) factor and two market-side-specific components capturing market microstructure effects. The corresponding variance components reflect information-driven and noise-induced volatilities. We find that all volatility components reveal distinct dynamics and are positively influenced by news. The proportion of noise-induced variances is highest before announcements and significantly declines thereafter. Moreover, news-affected responses in all volatility components are influenced by order flow imbalances.     

A behavioural explanation to the asymmetric volatility phenomenon: Evidence from market volatility index

This study examines how the behavioural explanations, in particular loss aversion, can be used to explain the asymmetric volatility phenomenon by investigating the relationship between stock market returns and changes in investor perceptions of risk measured by the volatility index. We study the behaviour of India volatility index vis‐à‐vis Hong Kong, Australia and UK volatility index, and provide a comprehensive comparative analysis. Using Bai‐Perron test, we identify structural breaks and volatility regimes in the time series of volatility index, and investigate the volatility index‐return relation during high, medium and low volatility periods. Regardless of volatility regimes, we find that volatility index moves in opposite direction in response to stock index returns, and contemporaneous return is the most dominating across the four markets. The negative relation is strongest for UK followed by Australia, Hong Kong and India. Second, volatility index reacts significantly different to positive and negative returns; negative return has higher impact on changes in volatility index than positive return across the markets over full‐sample and sub‐sample periods. The asymmetric effect is stronger in low volatility regime than in high and medium volatility periods for all the markets except UK. The strength of asymmetric effect is strongest for Hong Kong and weakest for India. Finally, negative returns have exponentially increasing effect and positive returns have exponentially decreasing effect on the changes in volatility index.     

Identifying volatility risk premia from fixed income Asian options

Fixed income options are frequently adopted by companies to hedge interest rate risk. Their payoff dependence on the cumulative short-term rate makes them particularly informative about interest rate volatility risk. Based on a joint dataset of bonds and Asian interest rate options, we study the interrelations between bond and volatility risk premia in a major emerging fixed income market. We propose a dynamic term structure model that generates an incomplete market compatible with a preliminary empirical analysis of the dataset. Approximation formulas for at-the-money Asian option prices avoid the use of computationally intensive Fourier transform methods, allowing for an efficient implementation of the model. The model generates a bond risk premium strongly correlated with a widely accepted emerging market benchmark index (EMBI-Global), and a negative volatility risk premium, consistent with the use of Asian options as insurance in this market.     

Forecasting the return distribution using high-frequency volatility measures

The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1, 1) model when the 30-year Treasury bond futures return is considered.     

Bubbles in the dividend–price ratio? Evidence from an asymmetric exponential smooth-transition model

Recent stock price movements have led to a re-examination of the present value model. An increasing belief is that although dividends and prices are indeed cointegrated, they may exhibit non-linear dynamics in the process of reversion. This paper implements an empirical model designed to capture two possible explanations for such non-linearity, namely transaction costs and noise traders. Utilising data from a number of countries we show that the dynamics of the log dividend yield are, first, characterised by an inner random walk regime, where the benefits of engaging in trade do not outweigh the costs and so the process moves randomly. Second, a reverting outer regime where the dynamics of reversion differ between positive and negative deviations, such that price rises greater than the level supported by dividends exhibit a greater degree of persistence than price falls relative to dividends.