Deepvol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Volatility Forecasting with High Frequency Data

The daily volatility is typically unobserved but can be estimated using high frequent tick-by-tick data. In this paper, we study the problem of forecasting the unobserved volatility using past values of measured volatility. Specifically, we use daily estimates of volatility based on high frequency data, called realized variance, and construct the optimal linear forecast of future volatility. Ut...

Forecasting Volatility using High Frequency Data

Volatility forecasting from multiscale and high-dimensional market data

Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identi/ed. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of e0ectively extracting information from multiscale and high-dimensional market data. SVM-based models can han...

Consistent High-Precision Volatility from High-Frequency Data

Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high-frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effect...

Combining high frequency data with non-linear models for forecasting energy market volatility

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets’ price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two wa...