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 …

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 …

-day and intra-day volatility models by estimating the AR(1)-GARCH(1,1)-skT and the AR(1)-HAR-RV-skT frameworks, respectively … intra-day volatility model is not as appropriate as it was expected to be for each of the different asset classes; stock … performance of the inter-day and intra-day volatility models across various markets. The inter-day specification predicts and …

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 …

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory … behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure … topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility …

so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This … study proposes a simple way of avoiding this problem and undertakes an out-of-sample forecast evaluation of the methodology … applied to the modelling of weekly exchange rate volatility. Our findings suggest that GETS specifications are especially …

model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in …In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto …

Many researchers use GARCH models to generate volatility forecasts. We show, however, that such forecasts are too … variable. To correct for this, we extend the GARCH model by distinguishing two regimes with different volatility levels. GARCH … terms only. The empirical application on U.S. dollar exchange rates shows that our model indeed yields better volatility …

We propose a more flexible range-based volatility model which can capture volatility process better than conventional … time series data. Range-based volatility CARR model with Markov-switching structure can assist us to describe the effect … for exogenous shock to market data. After the data fitting and VaR estimation, we conclude that the range-based volatility …