Results 51 to 60 of about 13,849,659 (169)

Examining Oil Prices for Chaos, Entropy, Fractionality and Complexity, and Modeling the Impacts of Economic Policy Uncertainty and Geopolitical Risks: GARCH–MIDAS–LSTM Approach

International Journal of Energy Research, Volume 2026, Issue 1, 2026.
As the leading energy source, oil price volatility has crucial effects in energy markets, and geopolitical risks (GPRs) and economic policy uncertainties contribute to its volatility. Further, chaos, long‐range dependence, fractionality, and complexity significantly reduce modeling and forecast performances.
Özgür Ömer Ersin, Melike Bildirici, Rajashik Paul   +2 more
wiley   +1 more source

Studying the effects of USING GARCH-EVT-COPULA METHOD TO ESTIMATE VALUE AT RISK OF PORTFOLIO [PDF]

Iranian Journal of Finance, 1999
Value at Risk (VaR) plays a central role in risk management. There are several approaches for the estimation of VaR, such as historical simulation, the variance-covariance and the Monte Carlo approaches. This work presents portfolio VaR using an approach
Ghodratollah Emamverdi
doaj   +1 more source

Renewables, Spillovers, and Volatility: Evidence From Romania’s Restructuring Electricity Market

International Journal of Energy Research, Volume 2026, Issue 1, 2026.
Our research study explores the dynamic interplay between Romania’s day‐ahead market (DAM) and intraday continuous (IDC) market in the context of rising renewable energy sources (RES) integration in Romania. Utilizing market data from June 2024 to mid of December 2025, the analysis incorporates both market transactions (prices and volumes) and system ...
Simona-Vasilica Oprea, Adela Bâra
wiley   +1 more source

Negative volatility spillovers in the unrestricted ECCC-GARCH model [PDF]

, 2010
Copyright @ 2010 Cambridge University Press.This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign.
Karanasos, Menelaos, Conrad, C, Conrad, Christian, Karanasos, M   +3 more
core   +1 more source

Forecasting the time-varying beta of UK firms: GARCH models vs Kalman filter method

, 2007
This paper forecast the weekly time-varying beta of 20 UK firms by means of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model.
Wu, Hao, Choudhry, Taufiq
core   +1 more source

Volatility Modeling of Currency Returns: A Bayesian Multivariate GARCH‐EVT Framework

Journal of Probability and Statistics, Volume 2026, Issue 1, 2026.
Exchange rate volatility is widely recognized as a major driver of financial instability in emerging markets, driven by its complex dynamics, time‐varying dependence structures, and the frequent occurrence of extreme events. However, existing models often treat these interrelated features in isolation, limiting their ability to adequately capture their
Jean De Dieu Ntawihebasenga, Marcel Ndengo, Charline Uwilingiyimana, Denis Ndanguza, Smritijit Sen   +4 more
wiley   +1 more source

Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian MS-GJR-GARCH(1,1) Frank copula EVT VaR model.

, 2018
Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian MS-GJR-GARCH(1,1) Frank copula EVT VaR model.
Haslifah M. Hasim (4839987), Hongsheng Dai (819650), Marius Galabe Sampid (5439320)   +2 more
core   +1 more source

The Volatility Forecasting of Tehran& International Stock Exchanges [PDF]

مطالعات تجربی حسابداری مالی, 2008
Stock prices are one of the most volatile economic variables and forecasting stock prices and their returns has proved very challenging, if not impossible.
H. Khaleghi Moghadam, S. Moshiri, K. Pakizeh   +2 more
doaj  

Estimation and Inference for Higher‐Order Stochastic Volatility Models With Leverage

Journal of Time Series Analysis, Volume 46, Issue 6, Page 1064-1084, November 2025.
ABSTRACT Statistical inference—estimation and testing—for stochastic volatility models is challenging and computationally expensive. This problem is compounded when leverage effects are allowed. We propose efficient, simple estimators for higher‐order stochastic volatility models with leverage [SVL(p)$$ (p) $$], based on a small number of moment ...
Md. Nazmul Ahsan, Jean‐Marie Dufour, Gabriel Rodriguez‐Rondon   +2 more
wiley   +1 more source

Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian MS-GJR-GARCH(1,1) Student’s-t copula EVT VaR model.

, 2018
Forecasts daily VaR estimates and daily profit and loss (P&L) plots for an investment in a portfolio consisting of all banks following Bayesian MS-GJR-GARCH(1,1) Student’s-t copula EVT VaR model.
Haslifah M. Hasim (4839987), Hongsheng Dai (819650), Marius Galabe Sampid (5439320)   +2 more
core   +1 more source