Volatility and Links between National Stock Markets

King, Mervyn; Sentana, Enrique; Wadhwani, Sushil

doi:10.2307/2951737

Cited by 687 publications

(439 citation statements)

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“…King et al, 1994;Harvey (1991a, b)), and evidence of time-varying betas for international asset returns (e.g. Jorion, 1987, 1989;Mark, 1985;Harvey, 1993, 1994b), the model allows for time-variation in the conditional betas.…”

Section: The Empirical Modelsmentioning

confidence: 99%

Fundamental Determinants of National Equity Market Returns: A Perspective on Conditional Asset Pricing

Ferson

Harvey

1996

SSRN Journal

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This paper provides a global asset pricing perspective on the debate over the relation between predetermined attributes of common stocks, such as ratios of price-to-bookvalue, cash-¯ow, earnings, and other variables to the future returns. Some argue that such variables may be used to ®nd securities that are systematically undervalued by the market, while others argue that the measures are proxies for exposure to underlying economic risk factors. It is not possible to distinguish between these views without explicitly modelling the relation between such attributes and risk factors. We present an empirical framework for attacking the problem at a global level, assuming integrated markets. Our perspective pulls together the traditional academic and practitioner viewpoints on lagged attributes. We present new evidence on the relative importance of risk and mispricing e ects, using monthly data for 21 national equity markets. We ®nd that the cross-sectional explanatory power of the lagged attributes is related to both risk and mispricing in the two-factor model, but the risk e ects explain more of the variance than mispricing. Ó 1998 Elsevier Science B.V. All rights reserved. 0378-4266/97/$17.00 Ó 1997 Elsevier Science B.V. All rights reserved. PII S 0 3 7 8 -4 2 6 6 ( 9 7 ) 0 0 0 4 4 -7

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Section: The Empirical Modelsmentioning

confidence: 99%

Fundamental Determinants of National Equity Market Returns: A Perspective on Conditional Asset Pricing

Ferson

Harvey

1996

SSRN Journal

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“…Alternatively, other studies use econometric factor models to extract the latent variables. King et al (1994) use 16 national stock markets and a multivariate factor model in which the volatility of returns is induced by changing volatility in the orthogonal factors. They …nd that only a small proportion of the time variation in the covariances between national stock markets can be accounted for by observable economic variables.…”

Section: Introductionmentioning

confidence: 99%

In search of the determinants of European asset market comovements

Gomes

Taamouti

2016

International Review of Economics & Finance

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. The information improves the portfolio performance compared to an equally weighted portfolio.JEL Classi…cation: C22, G12, G15, G17, E44.

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“…Engle et al [122] and Ng et al [250] applied factor GARCH models on treasury bills and stock returns. Diebold and Nerlove [95], Harvey et al [164], King et al [182] and Alexander [4] proposed latent factor GARCH models, based on the assumption that only a few factors influence the conditional variances and covariances of asset returns, which are not functions of the information set.…”

Section: Multivariate Arch Modelsmentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

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Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

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Volatility and Links between National Stock Markets

Cited by 687 publications

References 0 publications

Fundamental Determinants of National Equity Market Returns: A Perspective on Conditional Asset Pricing

Fundamental Determinants of National Equity Market Returns: A Perspective on Conditional Asset Pricing

In search of the determinants of European asset market comovements

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

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