This article proposes an alternative approach of Value-at-Risk (VaR) estimation. Financial assets are known to have irregular return patterns; not only the volatility but also the distribution functions themselves may vary with time. Therefore, traditional time-series models of VaR estimation...

Given the vast growth in credit default swap (CDS) market over the last few years, a dramatic improvement is projected in pricing discovery of sovereign CDS as well as its interaction with the underlying bond markets. In this article, a recent comprehensive sample of 20 sovereign CDS spreads,...

This paper examines the recent interactive relationships between the crude oil prices and stock performances of alternative energy companies. The examination was conducted from 2001 to mid-2010, and the sample period is divided into three sub-periods according to two Middle East wars. Different...

This paper finds that the dynamics of stock price continuation are asymmetrical, in terms of both business cycles and past performances. During times of recession, stock returns are explained differently for past losers and winners; the level of credit quality dominates the return dynamics for...

A new method is proposed to estimate Value-at-Risk (VaR) by Monte Carlo simulation with optimal back-testing results. The Monte Carlo simulation is adjusted through an iterative process to accommodate recent shocks, thereby taking into account the latest market conditions. Empirical validation...

Exchange rates are known to have irregular return patterns; not only their return volatilities but the distribution functions themselves vary with time. Quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution. This...

The volatility of financial asset returns is a key variable in risk management and derivative pricing. The behaviours of emerging equity markets are now significant to global economies. This research examines the performance of five popular categories of volatility forecasting models on 31...

This paper investigates the dynamics of credit default swap (CDS) spread. We first find auto-correlations and cross-correlations of the CDS series and the CDS average by employing detrended cross-correlation analysis (DCCA). We then employ smooth transition autoregressive (STAR) models to...

Quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution. Financial assets are known to have irregular return patterns; not only the volatility but also the distribution functions themselves may vary with time, so...