We analyze the impact of the estimation frequency - updating parameter estimates on a daily, weekly, monthly or quarterly basis - for commonly used GARCH models in a large-scale study, using more than twelve years (2000-2012) of daily returns for constituents of the S&P 500 index. We assess the...

Financial experts assume that measures the risk of financial asset returns generally have a normal distribution. Reality often shows asset returns are not normally distributed, so that the constraints and make it difficult to estimate the risk of taking the measurements. For it is necessary to...

The catastrophic failures of risk management systems in 2008 bring to the forefront the need for accurate and flexible estimators of market risk. Despite advances in the theory and practice of evaluating risk, existing measures are notoriously poor predictors of loss in high-quantile events. To...

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation's distribution is estimated with the fully...

We improve on the Instrumented Principal Component Analysis (IPCA) model developed in Kelly, Pruitt and Su (2019) by providing more efficient Generalized Least Square (GLS) estimators with a closed-form limiting distribution allowing for a more consistent (mis)pricing inference. The IPCA model...

We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995-2014...

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...

In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the...

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...