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。     

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.     

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.     

Common stochastic volatility trends in international stock returns

The aim of this work is to capture common stochastic trends in weekly volatilities of the Dow Jones, Nikkei, Hang Seng and Strait Times index using a multivariate stochastic volatility (SV) model. The results suggest a very high correlation among the volatility innovations, so that it is examined whether the four series share any common stochastic trends. A Principal Component Analysis and a Factor Analysis in the state space setting reveal that two common stochastic trends can be found to underlie the volatility series. The resulting linear combinations of the volatility series no more exhibit any stochastic trend but are stationary in the state space framework. Thus, it can be concluded that volatilities of the four stock indexes are in essence co-persistent.     

Optimal portfolio diversification with a multi-chain regime-switching spillover GARCH model

A multi-chain regime-switching spillover GARCH (MCRSSG) model is proposed for optimal portfolio diversification. MCRSSG specifies the within-regime time-varying correlation via a multi-chain state-dependent spillover factor and quantifies the magnitude of volatility spillovers under different regime combinations. MCRSSG is applied to investigate the diversification benefit of precious metals, crude oil, and financial securities for the Korean stock market at a sector level. The empirical results reveal that the Dow Jones Islamic market US total return index provides the best diversification benefit and MCRSSG exhibits superior effectiveness for risk-adjusted return and reward-to-semivariance ratio.     

Market imperfections and the information content of implied and realized volatility

The information content of option implied volatility and realized volatility under market imperfections are studied in the context of GARCH modeling and volatility forecasts of Taiwan stock market (TAIEX) returns. Consistent with most studies, we find that the Taiwan implied volatility index (TVIX) calculated from the TAIEX option prices contains most of the information, and that White's [White, H., 2000. A reality check for data snooping. Econometrica 68, 1097–1126] reality check test cannot reject the null hypothesis that the TVIX provides the best forecast. Possibly due to market imperfections, however, the incremental information content of realized volatility as well as daily returns cannot be ruled out. Finally, we also find that the information is found only in the most recent TVIX, indicating information is being efficiently impounded on the TAIEX option prices. This finding suggests that appropriately designed derivative products can alleviate the problems caused by market imperfections.     

The Dynamics of Discrete Bid and Ask Quotes

This paper presents an empirical microstructure model of bid and ask quotes that features discreteness, random costs of market making, and ARCH volatility effects. Applied to intraday quotes at 15-minute intervals for Alcoa (a randomly chosen Dow stock), the results show that quote exposure costs contain stochastic components that are persistent and large relative to the deterministic intraday "U" components. Analysis of the filtered estimates of the system suggest that bid and ask costs contain common components, and that these costs reflect risk as proxied by ARCH variance forecasts.     

FORECASTING STOCK INDEX VOLATILITY: COMPARING IMPLIED VOLATILITY AND THE INTRADAY HIGH–LOW PRICE RANGE

The intraday high–low price range offers volatility forecasts similarly efficient to high‐quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in‐sample and out‐of‐sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.     

The cost of capital- the practitioners view

The application of a SWARCH model to stock market returns allows one to endogenously determine the regime dependence of the stock market volatility. Comparison of the results from a sample of daily data from five major stock markets shows that the majority of the markets switch regimes simultaneously. This fact is used to investigate the relation between market volatility and the behaviour of the variance—;covariance matrix. It is found that the international variance—;covariance matrix is not stable and that changes in the matrix are dependent on the volatility regime. A high level of variance causes an increase in the average correlation coefficient. The co-movement of the markets is further described by a steady increase in the covariance over the whole sample period. It can be shown that both the time component and the regime dependence of the average correlation have separate and significant explanatory power.     

Subordinated Market Index Models: A Comparison

The paper compares various processes subordinated to the Wiener process tomodel the leptokurtic characteristics of index returns. Empirical analysisis performed on the Dow Jones and Nikkei 225 indexes. A good model to capturethe typical tail behaviour of these indexes turns out to be a long Studentt distributed one.     

The Effect of Volatility Changes on the Level of Stock Prices and Subsequent Expected Returns

This paper estimates volatility changes in daily returns to the Dow Jones Industrial Average over the sample period 1897 through 1988. This allows a direct investigation of the reaction of the level of stock prices and subsequent expected returns to these estimated changes in volatility. We provide empirical evidence consistent with relatively large and systematic revisions in stock prices and subsequent expected returns to volatility changes. However, there appears to be an asymmetry in the market's reaction to volatility increases as opposed to volatility decreases. A majority of our volatility changes cannot be associated with the release of significant economic information.     

A tale of two regimes: Theory and empirical evidence for a Markov-modulated jump diffusion model of equity returns and derivative pricing implications

We provide closed-form solutions for a continuous time, Markov-modulated jump diffusion model in a general equilibrium framework for options prices under a variety of jump diffusion specifications. We further demonstrate that the two-state model provides the leptokurtic return features, volatility smile, and volatility clustering observed empirically for the Dow Jones Industrial Average (DJIA) and its component stocks. Using 10 years of stock return data, we confirm the existence of jump intensity switching and clustering, illustrate transition probabilities, and verify superior empirical fit over competing Poisson-style models.     

World War II events and the Dow Jones industrial index

This paper investigates which events of World War II (WWII) the US investors (at that time) considered as turning points (structural breaks) of the war. The empirical study employs daily Dow Jones industrial average stock index and volatility from January 1939 to December 1945 and applies structural shift oriented test to determine endogenously the structural breaks during the WWII period. Results show that the majority of the wartime events (on and off the battlefield) labelled important by historians did result in structural breaks in both price movement and stock returns volatility (risk). These results have major implications for investors of the present and future.     

The Impact of Public Information on the Stock Market

We study the relation between the number of news announcements reported daily by Dow Jones & Company and aggregate measures of securities market activity including trading volume and market returns. We find that the number of Dow Jones announcements and market activity are directly related and that the results are robust to the addition of factors previously found to influence financial markets such as day-of-the-week dummy variables, news importance as proxied by large New York Times headlines and major macroeconomic announcements, and noninformation sources of market activity as measured by dividend capture and triple witching trading. However, the observed relation between news and market activity is not particularly strong and the patterns in news announcements do not explain the day-of-the-week seasonalities in market activity. Our analysis of the Dow Jones database confirms the difficulty of linking volume and volatility to observed measures of information.     

Price formation on stock exchanges: the evolution of trading within the day

Prior analyses of prices of the NYSE and other exchanges findthat transitory price volatility is greater at the open of tradingthan at the close. We extend this line of research by using40 years of hourly Dow Jones 65 composite price index data toestimate transitory volatility throughout the trading day. Ourresults indicate that transitory volatility steadily declinesduring the trading day. We find a similar intraday decline intransitory volatility for a 2-year sample of the individualfirms in the Dow Jones 30 Industrials Index. The results areconsistent with the hypothesis that trading aids price formationand do not support the argument that particular trading mechanismsare the source of greater volatility at the open of trading.     

Intraday trading halts in the Nikkei futures market

The Osaka Securities Exchange (OSE) halts Nikkei 225 index-futures trading when the next transaction is to take place at a price more than ¥30 (prior to February 1994) or ¥60 (from February 1994) away from the previous trading price. This paper examines the efficacy of the intraday price limit rule in terms of price discovery, liquidity and volatility. We also include transaction data from the Singapore International Monetary Exchange (SIMEX) where Nikkei futures are traded simultaneously. The intraday price limit rule generally appears to be ineffective in reducing volatility and avoiding price jumps, at least partly because OSE traders have access to the alternative market at SIMEX.     

How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

Abstract:  We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed.     

Price discovery and changes in regimes for stock index futures

In this paper, we propose a cointegration system that considers regime shifts in the stock index futures markets. Meanwhile, three such markets—the S&P, the CAC 40, and the Nikkei 225 index futures—are examined using the proposed model. The empirical evidence shows that the cointegration system with consideration of regime shifts performs better than the usual cointegration system without considering regime shifts. Moreover, the three futures markets exhibit different patterns for distinct regimes.     

Asymmetric volatility connectedness between Islamic stock and commodity markets

This paper examines the asymmetric volatility connectedness amongst the Dow Jones Islamic Market Index (DJIM) and the Brent crude oil, gold, and silver markets. We use the Diebold and Yilmaz methodology to examine asymmetric volatility connectedness associated with bad (negative semi-variance) and good (positive semi-variance) volatility in these markets. We identify significant volatility connectedness between the DJIM and commodity markets, with the DJIM and Brent oil markets being the largest net contributors of spillovers. Furthermore, the evidence on semi-volatility connectedness displays asymmetric behavior. Bad volatilities are associated with net transmission of spillovers to other markets, except for silver. Our results have significant implications for investors dealing with the DJIM and commodity markets in terms of asset management and diversification.