The effect of the 1987 Stock Crash on international financial integration
TLDR
In this article, the authors examined daily open-to-close returns of major stock market indices on the New York Stock Exchange, Tokyo Stock Exchange and the London Stock Exchange over the 1985-1990 period, which encompasses the October 1987 Stock Market Crash.Abstract:
This paper examines daily open-to-close returns of major stock market indices on the New York Stock Exchange, Tokyo Stock Exchange and the London Stock Exchange over the 1985-1990 period, which encompasses the October 1987 Stock Market Crash. We estimate volatility spillover effects across the 24 hour day using a GARCH-M model. We find evidence that volatility spillover effects emanating from Japan have been gathering strength over time, especially after the 1987 Crash. This may reflect a growing awareness by domestic investors of the economic interdependence of international financial markets since the 1987 Stock Market Crash.read more
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Energy Shocks and Financial Markets
TL;DR: The authors examined the information transmission mechanism linking oil futures with stock prices, where they examined the lead and lag cross-correlations of returns in one market with the others and investigated the dynamic interactions between oil futures prices traded on the New York Mercantile Exchange (NYMEX) and US stock prices.
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Asymmetric volatility transmission in international stock markets
TL;DR: In this paper, the authors investigated the transmission mechanism of price and volatility spillovers across the New York, Tokyo and London stock markets using an extended multivariate Exponential Generalized Autoregressive Conditionally Heteroskedastic (EGARCH) model.
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Stock market linkages: Evidence from Latin America
TL;DR: In this paper, the authors investigated the dynamic interdependence of the major stock markets in Latin America using data from 1995 to 2000, and found that there is one cointegrating vector which appears to explain the dependencies in prices.
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High frequency data in financial markets: Issues and applications
Charles Goodhart,Maureen O'Hara +1 more
TL;DR: In this article, the authors set out some of the many important issues connected with the use, analysis, and application of high-frequency data sets, including the effect of market structure on the availability and interpretation of the data, methodological issues such as the treatment of time, the effects of intra-day seasonals, and the effects on time-varying volatility, and information content of various market data.
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Hourly volatility spillovers between international equity markets
Raul Susmel,Robert F. Engle +1 more
TL;DR: This article examined the timing of mean and volatility spillovers between New York and London equity markets using an ARCH model and found that the evidence of volatility spillover between these markets is minimal and have a duration which lasts only an hour or so.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
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Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
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Expected stock returns and volatility
TL;DR: In this article, the authors examined the relation between stock returns and stock market volatility and found that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns.
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A conditionally heteroskedastic time series model for speculative prices and rates of return
TL;DR: In this article, a simple time series model designed to capture the dependence of speculative price changes and rates of return data was presented, which is an extension of the autoregressive conditional heteroskedastic (ARCH) and generalized ARCH (GARCH) models obtained by allowing for conditionally t-distributed errors.
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Estimation of Time Varying Risk Premia in the Term Structure: the ARCH-M Model
TL;DR: In this paper, an extension of the ARCH model was proposed to allow the conditional variance to be a determinant of the mean and is called ARCH-M. The model explains and interprets the recent econometric failures of the expectations hypothesis of the term structure.