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Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction

Liao, Yin - 2012

Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting...

Persistent link: https://www.econbiz.de/10013105658

Dynamic correlations and optimal hedge ratios

Bos, Charles S.; Gould, Phillip - 2007

The focus of this article is using dynamic correlation models for the calculation of minimum variance hedge ratios between pairs of assets. Finding an optimal hedge requires not only knowledge of the variability of both assets, but also of the co-movement between the two assets. For this...

Persistent link: https://www.econbiz.de/10011372522

Volatility predictability in crude oil futures : evidence based on OVX, GARCH and stochastic volatility models

Zhang, Zheng; Raza, Muhammad Yousaf; Wang, Wenxue; Sui, Lu - In: Energy strategy reviews 50 (2023), pp. 1-12

The paper examines the volatility predictive ability of the CBOE crude oil volatility index (OVX), GARCH and Stochastic Volatility Models in the crude oil market. Specifically, the dynamics of two major crude oil pricing benchmarks - Brent in Europe and WTI in America are compared. OVX index is...

Persistent link: https://www.econbiz.de/10014574074

Investing in Emerging Markets : An Asymmetric Stochastic Multivariate Volatility Model to Diversify Efficiently a Portfolio of Assets

Rannou, Yves - 2014

The asset allocation is a practical problem for most institutional and private investors, who routinely deal with a wide variety of stocks, bonds and options. Evidence suggests that both the expected return and the volatility vary over time. Many studies find that the expected returns have...

Persistent link: https://www.econbiz.de/10013055149

To jump or not to jump : momentum of jumps in crude oil price volatility prediction

Zhang, Yaojie; Wang, Yudong; Ma, Feng; Wei, Yu - In: Financial innovation : FIN 8 (2022), pp. 1-31

A well-documented finding is that explicitly using jumps cannot efficiently enhance the predictability of crude oil price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the jump component is persistent when forecasting the oil...

Persistent link: https://www.econbiz.de/10013272635

Mixed-Frequency Predictive Regressions with Parameter Learning

Leippold, Markus; Yang, Hanlin - 2023

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

Persistent link: https://www.econbiz.de/10014348997

Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models

Nguyen, Hoang; Virbickaite, Audrone - 2022

Stock and oil relationship is usually time-varying and depends on the current economic conditions. In this study, we propose a new Dynamic Stochastic Mixed data frequency sampling (DSM) copula model, that decomposes the stock-oil relationship into a short-run dynamic stochastic component and a...

Persistent link: https://www.econbiz.de/10013258038

Multivariate Stochastic Volatility-Double Jump Model : An Application for Oil Assets

Laurini, Marcio Poletti - 2017

We propose a new multivariate model to capture the presence of jumps in mean and conditional variance in the returns of oil prices and companies in this sector. The model is based on the presence of common factors associated with jumps in mean and variance, as it performs a decomposition of the...

Persistent link: https://www.econbiz.de/10012947795

Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance

Asai, Manabu; McAleer, Michael - 2014

Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory....

Persistent link: https://www.econbiz.de/10010259630

Weighted-indexed semi-Markov model : calibration and application to financial modeling

De Blasis, Riccardo - In: Financial innovation : FIN 9 (2023) 1, pp. 1-16

derived by the application of two popular machine learning algorithms, namely the k-means and Gaussian mixture model (GMM …). Moreover, by comparing the Bayesian information criterion (BIC) scores, the GMM approach allows for the selection of the number … usefulness of the proposed discretization approaches. In particular, GMM discretization is well suited for high-frequency returns …

Persistent link: https://www.econbiz.de/10014288949

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