Forecasting the Volatilities and Covariances of ISE Government Debt Securities Indices
Financial institutions should forecast the volatilities and correlations (thus covariances) of the financial instruments in their portfolios in order to calculate their market risk exposure correctly. This study examines the price volatility and covariance of interest related securities. In this study, the volatilities and covariances of ISE GDS price indices returns, which are taken as proxy for returns of debt-related securities, are modeled by using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Exponentially Weighted Moving Average (EWMA) methods. The models having been estimated, out-of-sample forecasting performance of the models for the next day’s variance and covariance are analyzed. The analyses show that in general GARCH models are more efficient to forecast both next day’s variance and covariance. This result is in line with other studies in the literature which used different financial instruments.
Volume (Year): 7 (2003)
Issue (Month): 27 ()
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