Multivariate volatility models are widely used in finance to capture both volatility clustering and contemporaneous correlation of asset return vectors. Here, we focus onmultivariate GARCHmodels. In this commonmodel class, it is assumed that the covariance of the error distribution follows a time dependent process conditional on information which is generated by the history of the process. To p...

This research investigates the hedging effectiveness of stock index futures markets in Malaysia and Singapore by employing various hedge ratio estimation methods, which comprises of the conventional OLS model, VECM, EGARCH and bivariate GARCH. The empirical results indicate that the Kuala Lumpur Futures Index (KLFI) provides higher hedging effectiveness compared to the Straits Times Index (STI)...

We measure global real and nominal macroeconomic uncertainty and analyze its impact on individual countries’ macroeconomic performance. Global uncertainty is measured through the conditional variances of global factors in inflation and output growth, estimated from a bivariate dynamic factor model with GARCH errors. The impact of global uncertainty is measured by including the conditional varia...

This paper develops a method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. As the association between the underlying assets may vary over time, the dynamic copula with time-varying parameter offers a better alternative to any static model for dependence structure and even to the dynamic copula model determined by dynamic depe...

This paper proposes an efficient approach to compute the prices of American style options in the GARCH framework. Rubinstein’s (1998) Edgeworth tree idea is combined with the analytical formulas for moments of the cumulative return under GARCH developed in Duan et al. (1999, 2002) to yield a simple recombining binomial tree for option valuation in the GARCH context. Since the resulting tree is ...

The main purpose of this paper is to verify the effectiveness of the bivariate Component GARCH-in-mean (GARCHM) model and analyze the interactions and risk premium of equity markets by exploring the shortand long-run volatility components on both the Taiwanese and Japanese equity markets. We show that unexpected shocks of volatility will in general influence the fluctuations of both equity and ...

The empirical joint distribution of return-pairs on stock indices displays high tail-dependence in the lower tail and low tail-dependence in the upper tail. The presence of tail-dependence is not compatible with the assumption of (conditional) joint normality. The presence of asymmetric-tail dependence is not compatible with the assumption of a joint student -t distribution. A general test for ...

The role of price risk in sow farrowings is investigated by using bivariate ARCH-M and GARCH-M models and a nonparametric kernel estimator. To account for the relevant time horizon of irreversible supply decisions, predictions for mean price and conditional price variance are iterated forward. The empirical results vary markedly in terms of their implications for risk response in hog supply dec...

We investigate the conditional interest rate risk premium in Treasury bill futures returns. A one-factor model predicts that the premium depends on the conditional variance. An Intertemporal CAPM based two-factor model predicts that it also depends on conditional covariance with the equity premium. Univariate and bivariate Integrated GARCH-in-Mean models suggest that the premium relates positiv...