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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, along with their underlying bonds, is examined for the pricing equilibrium for emerging markets. On the basis of the tests of vector error correction model and Granger causality, and by comparing with prior studies, this article finds that sovereign CDS market has served as more significant tool in measuring sovereign credit risk than before.

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 assuming constant and specific distribution are often unreliable. The study addresses the issue and employs the nonparametric kernel estimator technique directly on the tail distributions of financial assets to produce VaR estimates. Various key methodologies of VaR estimation are briefly discussed and compared. The empirical study utilizing a sample of stocks and stock indices for almost 14 years data shows that the proposed approach outperforms other existing methods.

This article proposes a threshold stochastic volatility model that generates volatility forecasts specifically designed for value at risk (VaR) estimation. The method incorporates extreme downside shocks by modelling left-tail returns separately from other returns. Left-tail returns are generated with a t-distributional process based on the historically observed conditional excess kurtosis. This specification allows VaR estimates to be generated with extreme downside impacts, yet remains empirically widely applicable. This article applies the model to daily returns of seven major stock indices over a 22-year period and compares its forecasts to those of several other forecasting methods. Based on back-testing outcomes and likelihood ratio tests, the new model provides reliable estimates and outperforms others.