We formulate a bivariate stochastic volatility jump-diffusion model with correlated jumps and volatilities. An MCMC Metropolis-Hastings sampling algorithm is proposed to estimate the model's parameters and latent state variables (jumps and stochastic volatilities) given observed returns. The...

Actual portfolios contain fewer stocks than are implied by standard financial analysis that balances the costs of diversification against the benefits in terms of the standard deviation of the returns. Suppose a safety first investor cares about downside risk and recognizes the heavytail feature...

Financial contagion and systemic risk measures are commonly derived from conditional quantiles by using imposed model assumptions such as a linear parametrization. In this paper, we provide model free measures for contagion and systemic risk which are independent of the specifcation of...

Risk managers use portfolios to diversify away the unpriced risk of individual securities. In this article we compare the benefits of portfolio diversification for downside risk in case returns are normally distributed with the case of fat-tailed distributed returns. The downside risk of a...

Most traditional Value at Risk models neglect market liquidity risk and hence only consider the market price risk (i.e. risk associated with holding a certain position). In order to fully capture the market risk associated to holding and trading a position, we first define market liquidity risk,...

Value-at-risk (VaR) is a useful risk measure broadly used by financial institutions all over the world. VaR has been extensively used to measure systematic risk exposure in developed markets like of the US, Europe and Asia. This paper analyzes the accuracy of VaR measure for Pakistan's emerging...

Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating...

We show that VAR calculation speedup of an order of magnitude can be obtained using Smart Monte Carlo with a sophisticated interpolator. As a byproduct, we give some encouraging numerical results for evaluating N-dimensional Gaussian integrals without doing any integrals at all

The purpose of this research is the realistic forecast of volatility in frame of a risk parity class of strategies. The custom rescaling of volatility – naïve risk parity - doesn't consider market inefficiencies which correspond to cyclical patterns like crisis and the following recovery. The...

In this paper, I construct an optimal portfolio by minimizing the expected tail loss (ETL) derived from the forward-looking natural distribution of the Recovery Theorem (RT). The RT is one of the first successful attempts at deriving an unparameterized natural distribution of future asset...