Abstract
The paper provides a framework to model and forecast volatility of EUR/USD exchange rate based on the unbiased AddRS estimator as proposed by Kumar and Maheswaran [1]. The framework is based on the heterogeneous auto-regressive (HAR) model to capture the heterogeneity in a market and to ac-count for long memory in data. The results indicate that the framework based on the unbiased extreme value volatility estimator generates more accurate forecasts of daily volatility in comparison to alternative volatility models.
Highlights
The volatility of the financial market has implications towards asset pricing, portfolio and fund management and in risk measurement and management
The orders that minimize the Schwarz information criterion (SIC) is an heterogeneous autoregressive (HAR)-AddRS-Generalized Autoregressive Conditional Heteroskedasticity (GARCH)(1, 1) specification. For both the HAR-AddRS model and the HAR-AddRS-GARCH model, the coefficients of lagged weekly and monthly volatility components are significant at 1% level of significance indicating that the lagged weekly and monthly volatility components have the greatest impact on the current volatility of the given exchange rates
We propose to use a simple HAR model-based framework to model and to generate more accurate volatility forecasts based on the AddRS estimator
Summary
The volatility of the financial market has implications towards asset pricing, portfolio and fund management and in risk measurement and management. More accurate prediction of volatility is important for option valuation, in implementing successful trading strategies and in construction of the optimal hedge using futures. Another important application of volatility is in the estimation and forecasting of Value-at-Risk and expected shortfall. The squared return and the absolute return are the very basic estimates of daily volatility based on close-to-close prices. These estimates of daily volatility are highly inefficient in nature [2].
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