forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that …, 60 and 300 seconds), forecast horizons (1, 5, 22 and 66 days) and the use of standard and robust-to-noise volatility and …-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility measures at the …

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional … Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV … two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV …

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 …

frequency volatilities and correlations ; Dynamic conditional correlation ; Spline-GARCH ; Idiosyncratic volatility ; Long …

form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that …

nonlinearity and asymmetry in the drift, and incorporates the level effect and stochastic volatility in the diffusion function is … asymmetric drift of the short rate, and the presence of nonlinearity, GARCH, and level effects in its volatility. The empirical … volatility of interest rate changes …

wide variety of stocks, bonds and options. Evidence suggests that both the expected return and the volatility vary over … considerable effort has been devoted to the modelling of time-varying volatility. Recent attention has moved to examining the … daily stock market volatility in a sample of significant emerging stock markets using an Asymetric Volatility Model (ASV …

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus … years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and … volatility models, if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at …

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly …, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Our approach allows … autoregressive conditional heteroskedasticity (GARCH), and stochastic volatility with jumps (SVJ) models. Our results are not due to …

multivariate t. This result is then applied to models of conditionally random volatility and used to derive exact results for the …