investment's effectiveness and risk. At first, we give an overview of various methods of bootstrap confidence interval estimation …This paper is aimed at presenting application of bootstrap interval estimation methods to the assessment of financial …, i.e. bootstrap-t interval, percentile interval and BCa interval. Then, bootstrap confidence interval estimation methods …

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 …

An intensive and still growing body of research focuses on estimating a portfolio’s Value-at-Risk.Depending on both the … orhistorical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation have receivedsubstantial … proposed estimation approach pairs intuitiveappeal with computational efficiency. We evaluate various alternative estimation …

fifth risk factor based on realized volatility of index returns. Moreover, instead of using data for stocks of a particular … investable equity indices in the period of 2000-2015. Such an approach is proposed to estimate an equity risk premium for a … years we illustrate the importance of model risk and data overfitting effects when drawing conclusions upon results of …

-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate … covariance estimation and the jump robustness of the estimator are illustrated in a simulation study. In an application to the …

-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 …

33 funds we employ Fama and French’s cross-sectional bootstrap. The results show that a large proportion of funds fail to …