fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias …-correction step to improve Value-at-Risk (VaR) forecasting ability of the n-EGARCH (normal EGARCH) model and correct the VaR for both … long and short positions. Our aim is to utilize the advantages of this model, but still use the bootstrap resampling method …

We examine how sensitive the new performance indexes incorporating high moments and disaster risk are to disaster risk …. The new performance indexes incorporating high moments and disaster risk are the Aumann-Serrano performance index and … underlying risk. We show, by numerical examples and empirical examples, how sensitive these indexes are to disaster risk …

Diversification of financial securities is considered a substantial element of portfolio risk. In this context, the … construction of an optimal portfolio is an ongoing concern for portfolio managers. This study measures the risk-reward tradeoffs … risk for DAX, MDAX, and CAC40 decreases from joining a common hypothetical stock market, while for FTSE100, FTSE MIB, and …

with Sharpe's ratio, offering an approach to point and confidence interval estimation which employs bootstrap resampling …The Sharpe ratio is a common financial performance measure that represents the optimal risk versus return of an … issues surrounding its statistical properties. Given the importance of obtaining robust determinations of risk versus return …

irrelevant for stocks with extremely high risk. This study finds that the SAI in India explains the variation in the excess …

attractive attracted under risk conditions. …

overall equity market returns and volatility. The risk associated with investment decisions is measured by the exponential … regime-switching regression model on the two market risk factors, we develop an entropy-based dynamic portfolio selection … is employed for the estimation of the hidden Markov model including the asset return parameters, while the out …

-horizon returns and the negligible impacts of estimation errors on the expected returns. This study uses the innovative simulation … return distribution has the slowest rate of convergence to normality among groups of assets. Estimation errors of the … imprecisions persist over the investment horizons, the estimation errors of the monthly return have a strong effect on the …