The relationship between risk and return is one of the most studied topics in finance. The majority of the literature … is based on a linear, parametric relationship between expected returns and conditional volatility. This paper models the …-realized variance. We find strong robust evidence of volatility feedback in monthly data. Once volatility feedback is accounted for …

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied … Volatility Surface (IV S). Practical applications require reducing the dimension and characterize its dynamics through a small … investigating long range dependence in the factor loadings series. Our result reveals that shocks to volatility persist for a very …

The volatility implied by observed market prices as a function of the strike and time to maturity form an Implied … Volatility Surface (IVS). Practical applications require reducing the dimension and characterize its dynamics through a small … investigating long range dependence in the factor loadings series. Our result reveals that shocks to volatility persist for a very …

-frequency data, in line with IT developments, enables the use of more information to estimate not only the variance (volatility), but … with respect to the bandwidth selection as well as the sampling frequency selection. The main finding is that the kernel … bandwidth is strongly related to the sampling frequency at the slow-time-time scale when applying a two-scale estimator, while …

parametric approach utilizing a Stochastic-Volatility-Jump-Diffusion (SVJD) model, estimated with MCMC and extended with Particle … method may be biased in the case when large outlier jumps occur in the time series as well as when the stochastic volatility …

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility … series and two stock index return series. We find that estimates of volatility using the model can differ dramatically from …

A Bayesian semiparametric stochastic volatility model for financial data is developed. This estimates the return … between the returns and changes in volatility, the leverage effect. An efficient MCMC algorithm for inference is described …

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 …

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. …