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 …

The volatility of equity and foreign exchange market is an important input to portfolio selection and to asset pricing … models. Many investment decisions and valuation of derivatives frequently rely on predictions of volatility. In this paper we … review the existing empirical literature in forecasting volatility of financial time series. Particularly, we decompose the …

for leverage effects in the realized volatility process and the long memory of the conditional variance of the HAR … component structure in asymmetric effects and a statistically significant long memory property in the “volatility of realized … volatility”. Compared with established HAR and ARFIMA realized volatility models, the proposed model exhibits superior in …

of equity price volatility. We show that future volatility is much more strongly related to the volatility of past … models. We also find that the impact of a jump on future volatility critically depends on the sign of the jump, with negative … (positive) jumps in prices leading to significantly higher (lower) future volatility. We show that models exploiting these …

to predict future S&P realized volatility. We evaluate the aggregate volatility predictions of regularization methods … main drivers of aggregate volatility are several financial and macroeconomic uncertainty proxies …

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 … real-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility …

Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to … on Standard and Poor 500 and Dow Jones Industrial Average indices show that volatility estimates using QRS measures …-of-sample forecast. For return models, the constant mean structure with Student-t errors and QRS volatility estimates provides the best …

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in … constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose … realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions …

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …