Various volatility estimators and models have been proposed in the literature to measure volatility of asset returns. In this paper, we compare empirical performance of various unconditional volatility estimators and conditional volatility models (GARCH and EGARCH) using time-series data of S&PCNX Nifty, a value-weighted index of 50 stocks traded on the National Stock Exchange (NSE), Mumbai. Th...

In this paper we document that realized variation measures constructed from highfrequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictab...

the emphasis of this paper is the role of volatility indices on improvement artificial neural networks (anns) forecasting models for the daily usd/eur and usd/gbp exchange rates two volatility indices are used. first; the realized volatility, which is based on intra-daily data, and second the garch volatility. they are applied into the model in two ways. firstly, the lagged volatility index is ...

! ! The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. In addition to the stochastic volatility model with leverage for the daily returns, ARFIMA process is jointly considered for the realized volatilities. Using a state space representation of the model, we estimate parameters by Markov chain Monte Carlo met...