Good News,Bad News,Volatility, and Betas

We investigate the conditional covariances of stock returns using bivariate exponential ARCH (EGARCH) models. These models allow market volatility, portfolio-specific volatility, and beta to respond asymmetrically to positive and negative market and portfolio returns, i.e., “leverage” effects. Using monthly data, we find strong evidence of conditional heteroskedasticity in both market and non-market components of returns, and weaker evidence of time-varying conditional betas. Surprisingly while leverage effects appear strong in the market component of volatility, they are absent in conditional betas and weak and/or inconsistent in nonmarket sources of risk.     

CONDITIONAL HETEROSCEDASTICITY IN THE MARKET MODEL AND EFFICIENT ESTIMATES OF BETAS

Previous studies have investigated only unconditional heteroscedasticity in the market model. This paper tests for both conditional and unconditional heteroscedasticities as well as normality. Using the monthly stock rate of return data secured from the Center for Research in Security Prices (CRSP) tape for 1976 through 1983, this paper shows that conditional heteroscedasticity is more widespread than unconditional heteroscedasticity, suggesting the necessity of model refinements that take conditional heteroscedasticity into account. This paper provides an alternative estimation of betas of individual securities and portfolios based on the autoregressive conditional heteroscedastic (ARCH) model introduced by Engle. The efficiency of the market model coefficients is markedly improved across all firms in the sample through the ARCH technique.     

Evaluating the Forecast Accuracy of Emerging Market Stock Returns

This paper analyzes the forecast performance of emerging market stock returns using standard autoregressive moving average (ARMA) and more elaborated autoregressive conditional heteroskedasticity (ARCH) models. Our results indicate that the ARMA and ARCH specifications generally outperform random walk models. Models that allow for asymmetric shocks to volatility are better for in-sample estimation (threshold autoregressive conditional heteroskedasticity for daily returns and exponential generalized autoregressive conditional heteroskedasticity for longer periods), and ARMA models are better for out-of-sample forecasts. The results are valid using both U. S. dollar and domestic currencies. Overall, the forecast errors of each Latin American market can be explained by the forecasts of other Latin American markets and Asian markets. The forecast errors of each Asian market can be explained by the forecasts of other Asian markets, but not by Latin American markets. Our predictability results are economically significant and may be useful for portfolio managers to enter or leave the market.     

Evidence of psychological barriers in the conditional moments of major world stock indices

This study investigates the existence of psychological barriers in the Dow Jones Industrial Average, the S&;P 500, and six foreign stock indices. It is believed by many in the investment community that index levels that are multiples of 100 serve as barriers, and that markets may resist crossing these barriers. Although return dynamics in the neighborhood of barrier points are not identical for all series studied, we find aberrations in the conditional means and variances consistent with psychological barriers. In five of the eight indices studied, conditional mean returns are significantly higher after crossing a barrier as part of an upward move, while only two series exhibit significant mean effects after crossing a barrier as part of a downward move. In seven of the eight series studied, we find significant conditional variance effects coincident with a barrier crossing. In addition, most series exhibit evidence of autoregressive conditional heteroskedastic (ARCH), generalized ARCH (GARCH), and leverage effects.     

Determinants of returns and volatility of Chinese ADRs at NYSE

This paper examines the determinants of returns and of volatility of the Chinese ADRs as listed at NYSE. Using an autoregressive conditional heteroskedasticity (ARCH) model and data from 16 April 1998 through 30 September 2004, we find that Hong Kong stock market (underlying market), US stock market (host market), and local (Shanghai A and B) markets all are important determinants of returns of the Chinese ADRs. However, the underlying Hong Kong market has the most significant impact on mean returns of the ADRs. In terms of the determinants of the conditional volatility of the ADRs returns, only shocks to the underlying markets are significant. These results are consistent with [Kim, M., Szakmary, A.C., Mathur, I., 2000. Price transmission dynamics between ADRs and their underlying foreign securities. Journal of Banking and Finance 24, 1359–1382] who find that the most influential factor in pricing the ADRs in Japan, UK, Sweden, The Netherlands and Australia is their underlying shares. Implications of the results for investors are discussed.     

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 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.     

Emerging Markets and Stock Market Bubbles: Nonlinear Speculation?

Daily returns of stock markets in emerging markets in Asia, Africa, South America, and Eastern Europe from the early 1990s through 2006 are analyzed for the possible presence of nonlinear speculative bubbles. The absence of these is tested for by studying residuals of vector autoregressive-based fundamentals, using the Hamilton regimeswitching model and the rescaled range analysis of Hurst. For the first test, absence of bubbles is rejected for twenty-four countries (except Mexico, Sri Lanka, and Taiwan); for the second test, it is rejected for twenty-six countries (except Malaysia). BDS testing on these residuals after autoregressive conditional heteroskedasticity (ARCH) effects are removed fails to reject further nonlinearity (except for Israel). Policy issues are discussed, noting that what is appropriate varies from country to country and time period to time period.     

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.     

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.     

Currency excess returns and global downside market risk

We assess cross-sectional differences in 23 bilateral currency excess returns in an empirical model that distinguishes between US-specific and global risks, conditional on US bull (upside) or bear (downside) markets. Using the US dollar as numeraire currency, our results suggest that global downside risk is compensated in conditional and unconditional, bilateral currency excess returns. This finding is mostly driven by the emerging markets' currencies in our sample. We also find that the link between the global downside risk and risks associated with a typical carry trade strategy is much weaker for emerging markets' currencies than for developed markets' currencies.     

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.     

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.     

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 impact of delivery terms on stock return volatility

The application of generalized ARCH models to daily stock returns shows changes in delivery and payment terms to be an important factor in determining measured volatility. In contrast, the holding period between trading days when markets are closed is relatively unimportant. This new approach allows fresh insights into stock return volatility and indicates that subsequent research on stock return volatility should incorporate the effects of payment delays.     

Asymmetric Price and Volatility Adjustments in Emerging Asian Stock Markets

This paper tests the hypothesis that stock returns in emerging stock markets adjust asymmetrically to past information. The evidence suggests that both the conditional mean and the conditional variance respond asymmetrically to past information. In agreement with studies dealing with developed stock markets, the conditional variance is an asymmetrical function of past innovations, rising proportionately more during market declines. More importantly, the conditional mean is also an asymmetrical function of past returns. Specifically, positive past returns are more persistent than negative past returns of an equal magnitude. This behaviour is consistent with an asymmetric partial adjustment price model where news suggesting overpricing (negative returns) are incorporated faster into current prices than news suggesting underpricing (positive returns). Furthermore, the asymmetric adjustment of prices to past information could be partially responsible for the asymmetries in the conditional variance if the degree of adjustment and the level of volatility are positively related.     

Dynamic relationship between exchange rate and stock price: Evidence from China

The paper empirically analyzes the dynamic relationship between Renminbi (RMB) real effective exchange rate and stock price with VAR and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models using monthly data from January 1991 to June 2009. The results show that there is not a stable long-term equilibrium relationship between RMB real effective exchange rate and stock price. There are also not mean spillovers between the foreign exchange and stock markets. Furthermore, the paper examines the cross-volatility effects between foreign exchange and stock markets using likelihood ratio statistic. There exist the bidirection volatility spillovers effects between the two markets, indicating the past innovations in stock market have the great effect on future volatility in foreign exchange market, and vice versa.     

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks

We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility.     

How do global investors differentiate between sovereign risks? The new normal versus the old

When global investors go into emerging markets or get out of them, how do they differentiate between economies? Has this behavior changed since the crisis of 2008 to reflect a “new normal”? We consider these questions by focusing on sovereign risk as reflected in monthly returns on credit default swaps (CDS) for 18 emerging markets and 10 developed countries. Tests for breaks in the time series of such returns suggest a new normal that ensued around October 2008 or soon afterwards. Dividing the sample into two periods and extracting risk factors from CDS returns, we find an old normal in which a single global risk factor drives half of the variation in returns and a new normal in which that risk factor becomes even more dominant. Surprisingly, in both the old and new normal, the way countries load on this factor depends not so much on economic fundamentals as on whether they are designated an emerging market.     

Downside Risk in Emerging Markets

This paper investigates the relation between downside risk and expected returns on the aggregate stock market in an international context. Nonparametric and parametric value at risk are used as measures of downside risk to determine the existence of a risk-return trade-off. For emerging markets, fixed effects panel data regressions provide evidence for a significantly positive relationship between monthly expected market returns and downside risk. This result is robust after controlling for aggregate dividend yield and price-to-fundamental ratios. The relationship between expected returns and downside risk is weaker for developed markets and vanishes when control variables are included in the specification.