The incremental volatility information in one million foreign exchange quotations

The volatility information found in high-frequency exchange rate quotations and in implied volatilities is compared by estimating ARCH models for DM/$ returns. Reuters quotations are used to calculate five-minute returns and hence hourly and daily estimates of realised volatility that can be included in equations for the conditional variances of hourly and daily returns. The ARCH results show that there is a significant amount of information in five-minute returns that is incremental to options information when estimating hourly variances. The same conclusion is obtained by an out-of-sample comparison of forecasts of hourly realised volatility.     

Stock prices and the flow of information in the Athens Stock Exchange

The paper investigates the dynamics of price changes and information flow to the market in the Athens Stock Exchange in Greece using daily data over the period 1988 to 1993. A generalised autoregressive conditional heteroskedastic (GARCH) model in stock returns is shown to reflect time dependence in the process generating information flow to the market. Using daily trading volume or value as proxies for information flow, we find them to be significant in explaining the variance of daily returns and to reduce GARCH effects substantially. This has implications for the informational efficiency of the market.     

THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

Volatility and information flows in emerging equity market: A case of the Korean Stock Exchange

Applying the generalized autoregressive conditional heteroskedasticity (GARCH) model to the Korean Stock Exchange, this study examines: (1) the statistical property of time-varying volatility in returns and trading volume data found in an emerging capital market, and (2) the property of the conditional variances of returns in predicting the flow patterns of information across the firms of different sizes. The results find that current trading volume as a proxy of information arrival dramatically reduces the persistence of the conditional variance, meaning that the arrival of information is a source of the ARCH effect in the emerging market just as it is in the U.S. The results also show that just as the volatility of larger firms can be predicted by shocks to smaller firms, the volatility of smaller firms can be predicted by shocks to larger firms. However, the volatility spillover effect from larger to smaller firms is more significant than that from smaller to larger firms.     

Intraday Return Volatility Process: Evidence from NASDAQ Stocks

This paper presents a comprehensive analysis of the distributional and time-series properties of intraday returns. The purpose is to determine whether a GARCH model that allows for time varying variance in a process can adequately represent intraday return volatility. Our primary data set consists of 5-minute returns, trading volumes, and bid-ask spreads during the period January 1, 1999 through March 31, 1999, for a subset of thirty stocks from the NASDAQ 100 Index. Our results indicate that the GARCH(1,1) model best describes the volatility of intraday returns. Current volatility can be explained by past volatility that tends to persist over time. These results are consistent with those of Akgiray (1989) who estimates volatility using the various ARCH and GARCH specifications and finds the GARCH(1,1) model performs the best. We add volume as an additional explanatory variable in the GARCH model to examine if volume can capture the GARCH effects. Consistent with results of Najand and Yung (1991) and Foster (1995) and contrary to those of Lamoureux and Lastrapes (1990), our results show that the persistence in volatility remains in intraday return series even after volume is included in the model as an explanatory variable. We then substitute bid-ask spread for volume in the conditional volatility equation to examine if the latter can capture the GARCH effects. The results show that the GARCH effects remain strongly significant for many of the securities after the introduction of bid-ask spread. Consistent with results of Antoniou, Homes and Priestley (1998), intraday returns also exhibit significant asymmetric responses of volatility to flow of information into the market.     

A re-examination of the asymmetric power ARCH model

The purpose of this paper is to provide a comprehensive methodology for the analysis of the Asymmetric Power ARCH model. First, it gives the ARMA representations of a power transformation of the conditional variance and the absolute returns. Second, it derives a certain fractional moment of the absolute observations. Third, it obtains the autocorrelation function of the power-transformed absolute returns. Finally, the practical implications of the results are illustrated empirically using daily data on five East Asia stock indices.     

Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation

Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. We find evidence of significant bidirectional nonlinear causality between returns and volume. We also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, we continue to find evidence of nonlinear causality from volume to returns.     

Conditional Heteroskedasticity and Global Stock Return Distributions

This paper investigates conditional return distribution characteristics for seven developed markets (DMs) and eight emerging markets (EMs). With the exception of Germany and Japan, the behavior of monthly returns of DM sample countries is similar to that of the U.S. In contrast, EM returns exhibit a substantially greater degree of serial correlation and a higher incidence of autoregressive conditional heteroskedasticity (ARCH) in monthly data. Aggregation of returns into two- and three-month holding periods decreases the significance of the ARCH effects. However, there are cross-sectional differences in the rate at which ARCH effects become insignificant. The findings of ARCH in monthly returns sample data is attributed to differences in the rate at which information arrives and is transmitted into prices in each market.     

Smooth Transition ARCH Models: Estimation and Testing

In this paper, we suggest an extension of the ARCH model, the smooth-transition autoregressive conditional heteroskedasticity (STARCH) model. STARCH models endogenously allow for time-varying shifts in the parameters of the conditional variance equation. The most general form of the model that we consider is a double smooth-transition model, the STAR-STARCH model, which permits not only the conditional variance, but also the mean, to be a function of a smooth-transition term. The threshold ARCH model, the Markov-ARCH model and the standard ARCH model are special cases of our STARCH model. We also develop Lagrange multiplier tests of the hypothesis that the smooth-transition term in the conditional variance is zero. We apply our STARCH model to excess Treasury bill returns. We find some evidence of a smooth transition in excess returns, but in contrast to previous studies, we find almost no evidence of volatility persistence once we allow for smooth transitions in the conditional variance. Thus, the apparent persistence in the conditional variance reported by many researchers could be a mere statistical artifact. We conduct in-sample tests comparing STARCH models to nested competitors; these suggest that STARCH models hold promise for improved predictions. Finally, we describe further extensions of the STARCH model and suggest issues in finance to which they might profitably be applied.     

Modelling S&P 100 volatility: The information content of stock returns

Theoretical models that relate volatility to the quantity of information are extended to a multi-asset setting and it is deduced that stock returns may or may not have incremental information when modelling index volatility, depending on the sources of information that move stock prices. The first empirical study that can help resolve this theoretical uncertainty is presented. A detailed analysis of the daily volatility of the S&P 100 index from 1984 to 1998 shows there is some incremental volatility information in the returns from the 100 shares that define the index. This evidence is obtained from ARCH models that incorporate leverage effects, dummy variables for the 1987 crash and aggregate measures of stock return volatility. Significant differences between estimated volatilities are found for various stock measures and sub-periods.     

The dynamic interaction between volatility and returns in the US stock market using leveraged bootstrap simulations

One of the most important stylized facts in finance is that stock index returns are inversely related to volatility. The theoretical rationale behind the proposition is still controversial. The causal relationship between returns and volatility is investigated in the US stock market over the period 2004-2009 using daily data. We apply a bootstrap test with leveraged adjustments that is robust to non-normality and ARCH. We find that the volatility causes returns negatively and returns cause volatility positively. The policy implications of our findings are discussed in the main text.     

VOLATILITY FORECASTS,TRADING VOLUME,AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t?1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t?1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.     

Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility

The paper develops an empirical return volatility-trading volume model from a microstructure framework in which informational asymmetries and liquidity needs motivate trade in response to information arrivals. The resulting system modifies the so-called “Mixture of Distribution Hypothesis” (MDH). The dynamic features are governed by the information flow, modeled as a stochastic volatility process, and generalize standard ARCH specifications. Specification tests support the modified MDH representation and show that it vastly outperforms the standard MDH. The findings suggest that the model may be useful for analysis of the economic factors behind the observed volatility clustering in returns.     

Deutsche Mark–Dollar Volatility: Intraday Activity Patterns, Macroeconomic Announcements, and Longer Run Dependencies

This paper provides a detailed characterization of the volatility in the deutsche mark–dollar foreign exchange market using an annual sample of five-minute returns. The approach captures the intraday activity patterns, the macroeconomic announcements, and the volatility persistence (ARCH) known from daily returns. The different features are separately quantified and shown to account for a substantial fraction of return variability, both at the intraday and daily level. The implications of the results for the interpretation of the fundamental "driving forces" behind the volatility process is also discussed.     

Index-option pricing with stochastic volatility and the value of accurate variance forecasts

In pricing primary-market options and in making secondary markets, financial intermediaries depend on the quality of forecasts of the variance of the underlying assets. Hence, pricing of options provides the appropriate test of forecasts of asset volatility. NYSE index returns over the period of 1968–1991 suggest that pricing index options of up to 90-days maturity would be more accurate when: (1) using ARCH specifications in place of a moving average of squared returns; (2) using Hull and White's (1987) adjustment for stochastic variance in the Black and Scholes formula; (3) accounting explicitly for weekends and the slowdown of variance whenever the market is closed. (JEL C22, C53, C10, G11, G12)     

The Distributional Characteristics of a Selection of Contracts Traded on the London International Financial Futures Exchange

This study examines the distributional properties of futures prices for contracts traded on LIFFE. A filtering process is employed to remove day of the week and holiday effects, a maturity effect, moving average effects and the influence of an asset's conditional variance from the raw returns series. Alternative distributional models from the stable paretian and ARCH families are examined for their applicability to futures data using a stability under additions. The results conclusively reject the hypothesis that futures returns are normally distributed with findings in favour of two related hypotheses – the mixtures of stable distribution and the ordinary stable distribution.     

The Distributional Characteristics of a Selection of Contracts Traded on the London International Financial Futures Exchange

This study examines the distributional properties of futures prices for contracts traded on LIFFE. A filtering process is employed to remove day of the week and holiday effects, a maturity effect, moving average effects and the influence of an asset's conditional variance from the raw returns series. Alternative distributional models from the stable paretian and ARCH families are examined for their applicability to futures data using a stability under additions. The results conclusively reject the hypothesis that futures returns are normally distributed with findings in favour of two related hypotheses – the mixtures of stable distribution and the ordinary stable distribution.     

Forecasting daily exchange rate volatility using intraday returns

This study investigates whether intraday returns contain important information for forecasting daily volatility. Whereas in the existing literature volatility models for daily returns are improved by including intraday information such as the daily high and low, volume, the number of trades, and intraday returns, here the volatility of intraday returns is explicitly modelled. Daily volatility forecasts are constructed from multiple volatility forecasts for intraday intervals. It is shown for the DEM/USD and the YEN/USD exchange rates that this results in superior forecasts for daily volatility.     

An empirical investigation of trading volume and return volatility of the Taiwan Stock Market

This study examines the Mixed Distribution Hypothesis (MDH) using 5-min interval stock returns of the Taiwan Stock Index (TSI). Startlingly enough, the persistence of stock volatility remains dominant after the stochastic mixing variable was included in the variance equation. It implies that the MDH cannot explain away the ARCH phenomenon. We have found that the composition of participants (approximately 92% of participants are individual investors) in TSI is a major contributing factor to the persistent volatility. In addition, the existence of limits on price changes, to some extent, accounts for the persistence phenomenon. Similar results are also found for individual stocks in the sample. Interestingly enough, the explanatory power of trading volume exhibits a U-shaped pattern in explaining return volatility in Taiwan Stock Market.     

The Risk‐Return Relation in International Stock Markets

We investigate the risk‐return relation in international stock markets using realized variance constructed from MSCI (Morgan Stanley Capital International) daily stock price indices. In contrast with the capital asset pricing model, realized variance by itself provides negligible information about future excess stock market returns; however, we uncover a positive and significant risk‐return tradeoff in many countries after controlling for the (U.S.) consumption‐wealth ratio. U.S. realized variance is also significantly related to future international stock market returns; more importantly, it always subsumes the information content of its local counterparts. Our results indicate that stock market variance is an important determinant of the equity premium.