Volatility long memory is a stylized fact that has been documented for a long time. Existing literature have two ways … to model volatility long memory: component volatility models and fractionally integrated volatility models. This paper … GARCH(1, 1) model by generating 37% less option pricing errors. With stronger volatility persistence, it also dominates a …

volatility's hidden state. Stochastic volatility is the unobserved state in a hidden Markov model (HMM), and can be tracked using … density on volatility. Our analysis relies on a specification of the martingale change of measure, which we will refer to as … separability. This specification has a multiplicative component that behaves like a risk premium on volatility-uncertainty in the …

Brownian motion. -- asymptotic uniformity ; local limit theorem ; volatility …

We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete … series per day varies from 1000 to 10,000. Complexities in the intraday dynamics of volatility and in the frequency of trades … intraday volatility shows that the dynamic modified Skellam model provides accurate forecasts compared to alternative modeling …

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 …