Finding a suitable weight matrix in spatial GARCH models is a challenge when the actual locations are not known. Thus, we introduce an estimation procedure for spatial GARCH models when the locations are unknown. We suggest to use balance sheet data of companies as proxy for the spatial distance...

We study the estimation of the high-dimensional covariance matrix and its eigenvalues under dynamic volatility models. Data under such models have nonlinear dependency both cross-sectionally and temporally. We first investigate the empirical spectral distribution (ESD) of the sample covariance...

We propose a novel method to estimate risk-neutral quantiles that uses sorting to minimize an objective function given by a convex combination of call and put option prices over the range of available strike prices. We demonstrate that this new method significantly improves the accuracy of...

This article introduces the rough path-dependent volatility (RPDV) model, a model structurally adapted to jointly capture two major empirical features of volatility: its rough behavior and its path-dependence.After presenting it in its general form and its link with other existing models in the...

We proposed the new method (GARCH-M-LEV) that allows capturing the asymmetry both in the variance and return equations. The development of the model is encouraged by the stylized fact that investors demand a higher risk premium during ”bad” volatility periods rather than ”good” ones. To...

The CCC-GARCH model, and its dynamic correlation extensions, form the most important model class for multivariate asset returns. For multivariate density and portfolio risk forecasting, a drawback of these models is the underlying assumption of Gaussianity. This paper considers the so-called...

The episodes of stock market crises in Europe and the U.S.A. since the year 2000, and the fragility of the international stock markets, have sparked the interest of researchers in understanding and in modeling the markets’ rising volatilities in order to prevent against crises. Portfolio...

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...

The dynamic conditional correlation (DCC) and co-range models are two main frameworks widely used to employ range-based univariate volatility. Using the two approaches, we build novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, as well...

This paper investigates whether geopolitical risks (GPRs) contains incremental information content to predict crude oil futures volatility under high-frequency Heterogeneous Autoregressive (HAR) model specifications. Moreover, considering structural breaks in crude oil volatility, we extend the...