Sudden changes in volatility: The case of five central European stock markets

This paper investigates sudden changes in volatility in the stock markets of new European Union (EU) members by utilizing the iterated cumulative sums of squares (ICSS) algorithm. Using weekly data over the sample period 1994–2006, the time period of sudden change in variance of returns and the length of this variance shift are detected. A sudden change in volatility seems to arise from the evolution of emerging stock markets, exchange rate policy changes and financial crises. Evidence also reveals that when sudden shifts are taken into account in the GARCH models, the persistence of volatility is reduced significantly in every series. It suggests that many previous studies may have overestimated the degree of volatility persistence existing in financial time series.     

Price and volatility spillovers between exchange rates and stock indexes for the pre- and post-euro period

In this paper we explore the nature of the mean, volatility and causality transmission mechanism between stock and foreign exchange markets for the United States and some major European markets for the periods pre- and post-euro. The asymmetric volatility transmission is described by an extended Multivariate Exponential Generalized Autoregressive Conditionally Heteroskedastic (EGARCH) model. The results support the asymmetric and long-range persistence volatility spillover effect and show strong evidence of causality in the mean and variance between foreign exchange rate and stock price for both pre- and post-euro periods. However, the stock price has a more significant effect on foreign exchange rate for the two subsamples. These results are robust to the cross-correlation function test suggested by Cheung and Ng. The implication is particularly important for international portfolio managers when devising hedging and diversification strategies for their portfolios.     

Implied integrated variance and hedging

If the volatility is stochastic, stock price returns and European option prices depend on the time average of the variance, i.e. the integrated variance, not on the path of the volatility. Applying a Bayesian statistical approach, we compute a forward-looking estimate of this variance, an option-implied integrated variance. Simultaneously, we obtain estimates of the correlation coefficient between stock price and volatility shocks, and of the parameters of the volatility process. Due to the convexity of the Black–Scholes formula with respect to the volatility, pricing and hedging with Black–Scholes-type formulas and the implied volatility often lead to inaccuracies if the volatility is stochastic. Theoretically, this problem can be avoided by using Hull–White-type option pricing and hedging formulas and the integrated variance. We use the implied integrated variance and Hull–White-type formulas to hedge European options and certain volatility derivatives.     

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.     

Re-examining the asymmetric predictability of conditional variances: The role of sudden changes in variance

The existence of “spillover effects” in financial markets is well documented and multivariate time series techniques have been used to study the transmission of conditional variances among large and small market value firms. Earlier research has suggested that volatility surprises to large capitalization firms are a reliable predictor of the volatility of small capitalization firms. A related line of research has examined how regime shifts in volatility may account for a considerable amount of the persistence in volatility. However, these studies have focused on univariate modeling and many have imposed regime changes on a priori grounds. This paper re-examines the asymmetry in the predictability of the volatilities of large versus small market value firms allowing for sudden changes in variance. Our method of analysis extends the existing literature in two important ways. First, recent advances in time series econometrics allow us to detect the time periods of sudden changes in volatility of large cap and small cap stocks endogenously using the iterated cumulated sums of squares (ICSS) algorithm. Second, we directly incorporate the information obtained on sudden changes in volatility in a Bivariate GARCH model of small and large cap stock returns. Our findings indicate that accounting for volatility shifts considerably reduces the transmission in volatility and, in essence, removes the spillover effects. We conclude that ignoring regime changes may lead one to significantly overestimate the degree of volatility transmission that actually exists between the conditional variances of small and large firms.     

Asymmetric Mean Reversion in European Interest Rates: A Two-factor Model

Abstract

This paper tests for asymmetric mean reversion in European short-term interest rates using a combination of the interest rate models introduced by Longstaff and Schwartz (Longstaff, F.A., Schwarts, E.S. (1992) Interest rate volatility and the ferm structure: A two factor general equilibrium model, Journal of Finance, 48, pp. 1259–1282.) and Bali (Bali, T. (2000) Testing the empirical performance of stochastic volatility models of the short-term interest rates, Journal of Financial and Quantitative Analysis, 35, pp. 191–215.). Using weekly rates for France, Germany and the United Kingdom, it is found that short-term rates follow in all instances asymmetric mean reverting processes. Specifically, interest rates exhibit non-stationary behavior following rate increases, but they are strongly mean reverting following rate decreases. The mean reverting component is statistically and economically stronger thus offsetting non-stationarity. Volatility depends on past innovations past volatility and the level of interest rates. With respect to past innovations volatility is asymmetric rising more in response to positive innovations. This is exactly opposite to the asymmetry found in stock returns.     

The determination and international transmission of stock market volatility

This paper extends the literature on low-frequency analysis of the causes and transmission of stock market volatility. It uses end-monthly data on stock market returns, interest rates, exchange rates, inflation, and industrial production for five countries (Britain, France, Germany, Japan, and the US) from July 1973 to December 1994. Efficient portfolios of world, European, and Japanese/US equity are first constructed, the existence of multivariate cointegrating relationships between them is demonstrated, and the transmission of conditional volatility between them is described. The transmission of conditional volatility from world equity markets and national business cycle variables to national stock markets is then modeled. Among the main findings are: first, world equity market volatility is caused mostly by volatility in Japanese/US markets and transmitted to European markets, and second, changes in the volatility of inflation are associated with changes of the opposite sign in stock market volatility in all markets where a significant effect is found to exist. To the extent that the volatility of inflation is positively related to its level, this implies that low inflation tends to be associated with high stock market volatility.     

Cross-border transmission of stock price volatility: evidence from the overlapping trading hours

Using high-frequency (5-minute returns) data, the transmission pattern of intraday volatility among three international stock markets (i.e., the United States, the United Kingdom, and Canada) during their overlapping trading hours (9:30–11:30 a.m. New York time). The major findings are as follows. First, the conditional variance of a domestic market is affected not only by the volatility surprises of its own market, but also by those of foreign markets. This finding holds for the United States as well as for Canada and the United Kingdom, implying that the information contained in the volatility surprises of each national market is clearly transmitted to other national markets. The volatility spillover is not unidirectional. Second, the magnitude of volatility spillover does not decrease monotonically as the lag length increases, indicating that the effect of a foreign volatility shock on the conditional variance of the domestic market tends to persist.     

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.     

Forecasting European stock volatility: The role of the UK

This study investigates the lead–lag relationships of volatility among European stock markets. Using weakly realized variance measures, we examine volatility spillover dynamics between the UK and other major stock markets in Europe, thereby identifying a long-run leading role for the UK market portfolio. Lagged UK volatility can significantly predict volatilities in non-UK countries, whereas lagged non-UK volatility has a limited association with UK volatility. Moreover, pairwise Granger causality estimations, predictive regression specifications, and out-of-sample validations reveal that volatility shocks in the UK are gradually reflected in market fluctuations across Europe with varying market-specific delays. Our findings support the limited attention explanation for the volatility predictability of the lagged UK equity index.     

不同的政策不确定性对股市波动影响相同吗?

本文基于多因子混频波动率模型,研究经济政策不确定性对股市行业波动的影响,为预防出现结构性断点,将样本分为经济增长和经济平稳两个时期,分别探讨两个时期内经济政策不确定性对股市波动的影响。研究发现,在全样本时期货币政策不确定性会显著增强行业波动,贸易和外汇政策不确定性会抑制行业波动,而财政政策不确定性的影响存在行业差异性;子样本结果显示,贸易政策不确定性对行业波动的影响存在非对称性,在经济增长期存在助推作用,在经济平稳期存在抑制作用;同时行业波动在经济增长期对贸易政策反应敏感,在经济平稳期对财政政策反应敏感。     

Dependence and mean reversion in stock prices: The case of the MENA region

This paper provides empirical evidence on the long memory behavior of the stock markets of Egypt, Jordan, Morocco, and Turkey. To test for long memory in the returns and volatility, we employ the modified rescaled range statistic R/S proposed by Lo [Lo, A.W., 1991. Long-term memory in stock market prices. Econometrica 59, 1279–1313] and the recently proposed rescaled variance V/S statistic developed by Giraitis et al. [Giraitis, L., Kokoszka, P.S. Leipus, R., Teyssiere, G., 2003. Rescaled variance and related tests for long memory in volatility and levels. J. Econ. 112, 265–294]. Further analysis is conducted by employing the ARFIMA (p, d, q) model to estimate the long memory parameters. Egypt and Morocco show evidence of long memory in the return series, while Jordan and Turkey display negative persistence. For the volatility series, long memory is conclusively demonstrated for all markets. Then, we compare the forecasting performance of ARMA and ARFIMA models and find that the ARFIMA model outperforms in out-of-sample forecasting of the markets. Our results should be useful to regulators, practitioners and derivative market participants, whose success depends on the ability to forecast stock price movements in these markets.     

Autoregressive stochastic volatility models with heavy-tailed distributions: A comparison with multifactor volatility models

This paper examines two asymmetric stochastic volatility models used to describe the heavy tails and volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student's t-distribution (ARSV-t), and the second is the multifactor stochastic volatility (MFSV) model. In order to estimate these models, the analysis employs the Monte Carlo likelihood (MCL) method proposed by Sandmann and Koopman [Sandmann, G., Koopman, S.J., 1998. Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Journal of Econometrics 87, 271–301.]. To guarantee the positive definiteness of the sampling distribution of the MCL, the nearest covariance matrix in the Frobenius norm is used. The empirical results using returns on the S&P 500 Composite and Tokyo stock price indexes and the Japan–US exchange rate indicate that the ARSV-t model provides a better fit than the MFSV model on the basis of Akaike information criterion (AIC) and the Bayes information criterion (BIC).     

信息不对称、过度自信与股价变动

信息传导的非同步和投资者情绪变化是股票市场的两个典型特征,前者会引发投资者之间出现信息不对称问题,后者主要体现为投资者过度自信,两者共同作用影响股票价格变动。本文将信息不对称和投资者过度自信情绪置于同一个分析框架,建立两阶段动态序贯定价理论模型研究现实市场上信息传导过程中股价变动的内在机制。结果表明:(1)面临新信息的进入,投资者对股票收益预期的调整与均衡价格之间具有正相关关系;(2)面临有利消息时,过度自信投资者比例越大,股票的均衡价格越高,投资收益将越低;面临不利消息时则相反;(3)随着过度自信投资者比例以及过度自信程度升高,市场风险溢价将下降;(4)投资者群体在信息传导过程中出现分化,对股价变动形成异质信念,未获取信息和获取信息但未出现过度自信的投资者认为股价被高估,获取信息且出现过度自信的投资者认为价格被低估,促使更多的交易,引发市场成交量和股价变动;(5)过度自信投资者比例与过度自信程度提高均会对市场效率产生正向影响,而对市场深度具有负向效应。最后,基于理论结果对非对称性和持续性等典型的市场波动性特征进行解释。     

Why are stock returns and volatility negatively correlated?

The literature documents that low stock returns are associated with increased volatility, but two competing explanations have proved difficult to disentangle. A negative return increases leverage, making equity value more volatile. However, an increase in volatility that persists causes stock prices to drop. We follow Bekaert and Wu [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42.] in controlling for leverage, but distinguish between volatility regimes that persist from less persistent changes using GARCH. For post-World War II returns on the value-weighted portfolio of all NYSE stocks, we find that changes in the volatility regime are reflected in stock returns but not in GARCH.     

The effects of the introduction of the euro on the volatility of European stock markets

Have convergence of European economies and introduction of the euro produced some effects on European stock markets? Theory suggests that stabilization of fundamentals should decrease variance of stock returns for historically unstable stock markets. We test this proposition with daily data for the period January 1988–May 2000 and apply a three-regime Markov switching model for the variance-covariance matrix among several stock indices, including the UK and the US. The analysis shows that introduction of the euro, after an initial burst of volatility common to all European stock markets, has indeed stabilized the Spanish and Italian stock markets.     

Value-at-Risk Forecasting of Chinese Stock Index and Index Future Under Jumps,Permanent Component,and Asymmetric Information

This article investigates the performance of time series models considering the jumps, permanent component of volatility, and asymmetric information in predicting value-at-risk (VaR). We use evaluation statistics including size and variability, accuracy, and efficiency to determine some suitable VaR measures for the Chinese stock index and its futures. The results reveal that models with jumps can provide VaR series that are less average conservative and have higher variability. Furthermore, additional considering the permanent component of volatility and asymmetric effect can induce more accurate and efficient risk measure in the long and short positions of the stock index and its futures.     

Financial crises and the dynamics of the spillovers between the U.S. and BRICS stock markets

We examine the spillover dynamics between the U.S. and BRICS stock markets using the multivariate DECO-GJR-GARCH model and spillover index method. We identify time variations in volatility equicorrelation and significant dynamic spillovers between these stock markets, as well as an increased impact of uncertainty on spillovers. Spillovers between markets intensify after the inception of the global financial crisis and subsequent European sovereign debt crisis. We also find, following the commencement of the crisis periods, that the U.S., Brazilian, and Chinese markets are net volatility transmitters, whereas the Russian, Indian, and South African markets are net recipients. These results shed new light on the information transmission channels between the U.S. and BRICS stock markets.     

Volatility transmission patterns and terrorist attacks

The objective of this study is to analyse volatility transmission between the US and Eurozone stock markets considering the financial market responses to the September 11, March 11 and July 7 terrorist attacks. In order to do this, we use a multivariate GARCH model and take into account the asymmetric volatility phenomenon, the non-synchronous trading problem and the turmoil periods themselves. Moreover, a graphical analysis of the Asymmetric Volatility Impulse-Response Functions (AVIRF) is introduced, which takes into consideration the financial market responses to the terrorist attacks. Results suggest that there is bidirectional and asymmetric volatility transmission and show the different impacts that terrorist attacks had on both markets.     

The relationship between stock returns and volatility in international stock markets

This study examines the relationship between expected stock returns and volatility in the 12 largest international stock markets during January 1980 to December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in 6 out of the 12 markets. The results lend some support to the recent claim [Bekaert, G., Wu, G., 2000. Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1–42; Whitelaw, R., 2000. Stock market risk and return: an empirical equilibrium approach. Review of Financial Studies 13, 521–547] that stock market returns are negatively correlated with stock market volatility.