This paper studies the performance of nonparametric quantile regression as a tool to predict Value at Risk (VaR). The approach is flexible as it requires no assumptions on the form of return distributions. A monotonized double kernel local linear estimator is applied to estimate moderate (1%)...

The paper offers an alternative approach to analyzing stock market time series data. The purpose is to develop descriptive, more intuitive, and closer to reality analogs of the behavior of US stock market prices, as indexed by the S&P500 stock price index covering the period October 2003 to...

In this paper the authors introduce a new hybrid approach based on the Extreme Value Theory (EVT) to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. The approach is suitable for measuring market risk in the emerging markets. It is...

A dynamic semi-parametric framework is proposed to study time variation in tail fatness of sovereign bond yield changes during the 2010--2012 euro area sovereign debt crisis measured at a high (15-minute) frequency. The framework builds on the Generalized Pareto Distribution (GPD) for modeling...

We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation...

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional parameters, and show that it can be reliably estimated via the Stochastic Approximation Expectation-Maximization algorithm. Applying our model to high-frequency transaction data, we...

Using daily data of the S&P 500 index from 1950 to 2015, we investigate the relation between return and transaction volume in the statistical distribution tails associated with booms and crashes in the US stock market. We use extreme value theory (peaks-over-threshold method) to study the...

The aim of this paper is to derive a coherent risk measure for heavy tailed GARCH processes using extreme value theory. For the proposed measure, the risk associated to a given portfolio is less than the sum of the stand-alone risks of its components. This measure which is value at risk (VaR),...

The aim of this paper is to derive a coherent risk measure for heavy tailed GARCH processes using extreme value theory. For the proposed measure, the risk associated to a given portfolio is less than the sum of the stand-alone risks of its components. This measure which is value at risk (VaR),...

The aim of this paper is to derive a coherent risk measure for heavy tailed GARCH processes using extreme value theory. For the proposed measure, the risk associated to a given portfolio is less than the sum of the stand-alone risks of its components. This measure which is value at risk (VaR),...