Forcasting Portofolio Value-At-Risk for International Stocks, Bonds, and Foreign Exchange Emerging Market Evidence

Abstract

This paper uncovers the nature of conditional correlations between and volatility spillovers across bond, stock and foreign exchange in Indonesia, Malaysia, the Philippines, and Thailand. Using various multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models, it finds the evidence of highly persistence in the conditional variance, volatility spillovers across assets, and time-varying conditional correlations in all markets. It also provides Value-at-Risk forecast based on the estimated models. Assuming normal distribution, the tests suggest that incorporating volatility spillovers and time-varying conditional correlations does not help in providing Value-at-Risk forecasts. Assuming t distribution, the tests suggest that incorporating volatility spillovers provides better VaR forecasts. Keywords: conditional correlations, volatility spillovers, VaR forecast