This paper introduces a parsimonious and yet flexible semiparametric model to forecast financial volatility. The new …

volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a …

and counterparty risks does not improve forecast accuracy and the predictability seems to derive from the econometric …

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the …-known stylized effects present in financial data. We consider an HAR model with asymmetric effects with respect to the volatility and … distribution for the innovations. The analysis of the forecast performance during the different periods suggests that including the …

This paper shows that combinations of option implied and time series volatility forecasts that are conditional on … used in this paper extends the application of conditional predictive ability tests to select forecast combinations. We show … that this method works well in practice by applying it to volatility forecasts for the Mexican Peso-US Dollar exchange rate …

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous …-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump … volatility decomposition or relative transformations of volatility, in the forecasting models …

Forecasting realized volatility in exchange rates is very important for both practitioners and academics. Our aim is to … realized volatility. We employ four widely traded currencies, namely GBP, CHF, YEN and EUR and we also construct a basket of … learning, dimensionality reduction, forecast combination and amalgamation approaches. Our results highlight the predictive …

Despite the vast academic literature on modelling stochastic volatility, many finance practitioners still use the … simple "RiskMetrics" approach of J. P. Morgan (1997), based on the exponentially weighted moving average (EWMA) volatility … combined with the $\sqrt{h}$-rule for scaling volatility with horizon. In this paper, we evaluate this approach using a …

This paper proposes a novel algorithm called Persistent Homology for Realized Volatility (PH-RV), which aims to … effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting … realized volatility (RV). This paper also proposes a novel neural network model for multi-step forecasting that systematically …

topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility … (RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation … indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a …