risk. The key insight behind our importance sampling based approach is the sequential construction of marginal and …We present an accurate and efficient method for Bayesian forecasting of two financial risk measures, Value-at-Risk and … Expected Shortfall, for a given volatility model. We obtain precise forecasts of the tail of the distribution of returns not …

characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in … market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models … that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long …

characterized by volatility clustering and asymmetry. Also revealed as a stylized fact is Long memory or long range dependence in … market volatility, with significant impact on pricing and forecasting of market volatility. The implication is that models … that accomodate long memory hold the promise of improved long-run volatility forecast as well as accurate pricing of long …

of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the … volatility with piecewise constant realisations on bins forming a partition of the time interval. The values on the bins are … assigned an inverse Gamma Markov chain (IGMC) prior. Posterior inference is straightforward to implement via Gibbs sampling, as …

greater risk-free rate volatility. But raising the prior uncertainty on dividend growth rates has ambiguous effects on the … a parsimonious set of prior parameters, the model generates a sizeable equity premium and a low risk-free rate even with … a power utility function, low risk aversion, and absence of persistence in growth rates. Raising the prior uncertainty …

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of … Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the … [increasingly] Bayesian—continues to be a key methodological foundation of risk management and regulation-related risk modeling …