Stock market volatility and the crash of 1987: evidence from six emerging markets

This paper studies volatility, risk premia and the persistence of volatility in six emerging stock markets before and after the 1987 stock market crash. The empirical investigation is conducted by means of the GARCH in the mean model (GARCH-M) and monthly data from Argentina, Greece, India, Mexico, Thailand, and Zimbabwe between January of 1976 and August of 1994. Results indicate changes in the ARCH parameter, risk premia and persistence of volatility before and after the 1987 crash. But these noted changes are not uniform and depend upon the individual markets. Factors other than the 1987 crash may also be responsible for the changes.     

Volume and Volatility: News or Noise?

This paper presents a market microstructure model that is consistent with several empirical regularities. The model embeds separate latent ARCH‐like volatility processes: one representing movements in the underlying fundamental and one representing noise caused by the trading process. This structure allows the regularities to depend either on news or noise. The heteroskedasticity and persistence in the data are due to both ARCH‐like processes. The model has difficulty in simultaneously capturing the size and persistence of trading volume. Several extensions of the basic model, particularly including a constant level of non‐informational trading, improve the model's ability to capture the relevant characteristics of the data.     

Sensitivity of the bank stock returns distribution to changes in the level and volatility of interest rate: A GARCH-M model

The objective of this paper is to employ the generalized autoregressive conditionally heteroskedastic in the mean (GARCH-M) methodology to investigate the effect of interest rate and its volatility on the bank stock return generation process. This framework discards the restrictive assumptions of linearity, independence, and constant conditional variance in modeling bank stock returns. The model presented here allows for shifts in the volatility equation in response to the changes in monetary policy regime in 1979 and 1982 to be estimated. ARCH, GARCH, and volatility feed back effects are found to be significant. Interest rate and interest rate volatility are found to directly impact the first and the second moments of the bank stock returns distribution, respectively. The latter also affects the risk premia indirectly. The degree of persistence in shocks is substantial for all the three bank portfolios and sensitive to the nature of the bank portfolio and the prevailing monetary policy regime.     

Volatility and cross correlation across major stock markets

Several papers have documented the fact that correlations across major stock markets are higher when markets are more volatile—this is done by comparing unconditional correlations over sub-periods or by using conditional correlations that are time varying. In this paper we examine the relation between correlation and variance in a conditional time and state varying framework. We use a switching ARCH (SWARCH) technique that does two things. One, it enables us to model variance as state varying. Two, a bivariate SWARCH model allows us to go from conditional variance to state varying covariances and correlations and hence test for differences in correlations across variance regimes. We find that the correlations between the U.S. and other world markets are on average 2 to 3.5 times higher when the U.S. market is in a high variance state as compared to a low variance regime. We also find that, compared to a GARCH framework, the portfolio choices resulting from our SWARCH model lead to higher Sharpe ratios.     

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.     

Volatility forecasts and at-the-money implied volatility: a multi-component ARCH approach and its relation to market models

This article explores the relationships between several forecasts for the volatility built from multi-scale linear ARCH processes, and linear market models for the forward variance. This shows that the structures of the forecast equations are identical, but with different dependencies on the forecast horizon. The process equations for the forward variance are induced by the process equations for an ARCH model, but postulated in a market model. In the ARCH case, they are different from the usual diffusive type. The conceptual differences between both approaches and their implication for volatility forecasts are analysed. The volatility forecast is compared with the realized volatility (the volatility that will occur between date t and t + ΔT), and the implied volatility (corresponding to an at-the-money option with expiry at t + ΔT). For the ARCH forecasts, the parameters are set a priori. An empirical analysis across multiple time horizons ΔT shows that a forecast provided by an I-GARCH(1) process (one time scale) does not capture correctly the dynamics of the realized volatility. An I-GARCH(2) process (two time scales, similar to GARCH(1,1)) is better, while a long-memory LM-ARCH process (multiple time scales) replicates correctly the dynamics of the implied and realized volatilities and delivers consistently good forecasts for the realized volatility.     

中国和台湾地区经济波动的实证研究

本文利用ARCH类模型对中国和台湾地区的实际GDP增长率的波动进行了实证分析,结果表明,中国实际GDP增长率的波动有ARCH效应,并且GARCH模型拟合效果最好,而台湾地区实际GDP增长率的波动没有ARCH效应。这表明中国经济波动率是变化的,实际GDP的增长率是对称的,而台湾地区的GDP的波动率是不变的。     

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.     

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.     

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.     

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.     

Exploring international differences in inflation dynamics

Standard closed-economy DSGE models have difficulty replicating the persistence of inflation. We use a multicountry dataset to establish some empirical regularities on persistence and volatility of aggregate consumer prices for 135 countries since 1993. We find both persistence and volatility to be low (high) in developed (developing) countries relative to the full sample average. This pattern is also observed in low (high) inflation countries. We then employ a two-country DSGE framework to investigate the extent to which structural open economy features, such as incomplete exchange rate pass-through, the existence of nontraded goods, and international financial market incompleteness, can help in replicating the persistence and volatility of consumer prices. Our simulation results indicate that the model can replicate the degree of inflation persistence seen in the data for both developed and developing countries, but cannot generate the low levels of volatility observed in developed economies.     

An evaluation of volatility forecasting techniques

The existing literature contains conflicting evidence regarding the relative quality of stock market volatility forecasts. Evidence can be found supporting the superiority of relatively complex models (including ARCH class models), while there is also evidence supporting the superiority of more simple alternatives. These inconsistencies are of particular concern because of the use of, and reliance on, volatility forecasts in key economic decision-making and analysis, and in asset/option pricing. This paper employs daily Australian data to examine this issue. The results suggest that the ARCH class of models and a simple regression model provide superior forecasts of volatility. However, the various model rankings are shown to be sensitive to the error statistic used to assess the accuracy of the forecasts. Nevertheless, a clear message is that volatility forecasting is a notoriously difficult task.     

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.     

石油市场与黄金市场收益率波动溢出效应研究

在总结国内外相关研究的基础上,基于2002年12月2日到2010年9月30日的日数据,建立相应的ARCH族模型,并进行Granger因果关系检验,本文对石油市场和黄金市场收益的波动性、波动的非对称性及其波动溢出效应进行实证分析。结果表明:两市均具有显著的方差时变性及新信息对波动冲击的持续性;GARCH(1,1)模型能够很好地消除其ARCH效应;两市均存在明显的非对称性,即石油市场中利空消息引起的波动比同等利好消息引起的波动要大,而黄金市场相反;两市只存在从石油市场到黄金市场的单向波动溢出效应。研究结果对该领域投资者的相关投资及决策人的决策制定具有重要的参考价值。     

The intertemporal volatility structure of Euro CD rates

In this paper we examine the intertemporal volatility structure of Eurocurrency rates of five different maturities ranging from seven days to twelve months for six Euro CD currency denominations spanning the 1986–1992 period. the analysis used the common ARCH-feature testing methodology recently developed by Engle and Kozicki (1993). First, the results indicate presence of ARCH effects in the Eurocurrency rate series. This result suggests that modelling of Eurocurrency rates requires the inclusion of time-varying risk premia. Second, our evidence reveals that short- and long-term Eurocurrency rate series have the same volatility process. the results point out that a common time-varying volatility process characterises most Eurocurrency rate series across maturities and currency denominations. Hence, the common ARCH results imply that a common time-varying variance model would be the appropriate specification of the conditional heterscedasticity for most Eurocurrency rates.     

Volatility switching and regime interdependence between information technology stocks 1995–2005

In this paper we use SWARCH models to analyze volatility regime switching and regime interdependence for information technology (IT) stocks in Canada, France, Hong Kong, Japan, Taiwan, the United States and a composite Emerging Markets (EM) index. We find that prior to the IT bubble country effects were more important for IT stocks, but the effect of the IT bubble has been to make industry effects more important than country effects in explaining the volatility switching behavior of IT stocks.     

The quality of market volatility forecasts implied by S&P 100 index option prices

This study examines the performance of the S&P 100 implied volatility as a forecast of future stock market volatility. The results indicate that the implied volatility is an upward biased forecast, but also that it contains relevant information regarding future volatility. The implied volatility dominates the historical volatility rate in terms of ex ante forecasting power, and its forecast error is orthogonal to parameters frequently linked to conditional volatility, including those employed in various ARCH specifications. These findings suggest that a linear model which corrects for the implied volatility's bias can provide a useful market-based estimator of conditional volatility.     

Exchange volatility and trade performance in Morocco and Tunisia: what have we learned so far?

This paper attempts to assess two interesting issues for two small open economies (Morocco and Tunisia). First, it analyses the historical behaviour of nominal exchange rate, differential price and real exchange rate uncertainties. Second, it investigates the stability of the interaction between exchange volatility and exports in nominal and real terms. Our main results reveal that the effect of differential price volatility on exports exceeds that of nominal exchange rate by a large margin in terms of duration of persistence, ARCH and GARCH effects and intensity of shock. The relationship appears complex. In Morocco, it is negative and significant in 75.82% (as average) of cases in nominal terms and in 77.22% in real terms. This link is stronger in Tunisia with averages, respectively, equal to 85.88% and 89.99%. We associate the apparently mixed results to the differential price uncertainty itself sensitive to ups and down oil price movements, switching regime and leverage effects.