Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern … forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time …-ahead volatility by using high-frequency data. We show that the dilated convolutional filters are ideally suited to extract relevant …

March 2020 packed 2 ½ years of normal U.S. stock market volatility into one month, making it the most volatile month on … respond to such volatility? In this article we explore four possible approaches, two long-term and two short-term in nature …. We give particular focus to Volatility Targeting and Momentum strategies, discussing the investor behavior that might …

evolve, many investors continue to forecast volatility using traditional approaches that are ill-suited to the time …-changing nature of volatility. In this paper, I analyze the performance of seven different multivariate-volatility models using a new … poorly when trying to forecast short-term volatility, and that a more dynamic model often provides superior out …

middle-term asset volatility is used for determination of the size of opening position when buy signal is obtained from trend … following model. The strategy is named as timing-and-volatility strategy.Two ways of implementation of the timing-and-volatility … strategy are proposed: without use of leverage and with use of leverage. Testing of the non-leveraged timing-and-volatility …