When analysing the volatility related to high frequency financial data, mostly non-parametric approaches based on … stochastic volatility. Estimation of the model delivers measures of daily variation outperforming their non …

suggested by new growth theory, while addressing the variable selection problem by means of Bayesian model averaging …. Controlling for variable selection uncertainty, we confirm the evidence in favor of new growth theory presented in several earlier …

This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead …). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model … model improves density forecasts, compared to the SV-DPM, a stochastic volatility with Student-t innovations and other fat …

measures, resulting from the new model, can be used to implemennt joint risk scenario analysis. …

In this paper, we extend the standard Gaussian stochastic-volatility Bayesian VAR by employing the generalized …

historical stochastic volatility and equity risk premium series display pronounced countercyclical fluctuations …We develop a Bayesian Markov chain Monte Carlo algorithm for estimating risk premia in dynamic stochastic general … equilibrium (DSGE) models with stochastic volatility. Our approach is fully Bayesian and employs an affine solution strategy that …

risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one … uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior … better results in terms of profit per capital engagement and risk than using a standard linear normalization …