Results 41 to 50 of about 4,724 (162)
Volatility forecasting for crude oil futures [PDF]
This paper studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005.
Marzo, Massimiliano, Zagaglia, Paolo
core +3 more sources
Spatial and spatiotemporal volatility models: A review
Journal of Economic Surveys, Volume 39, Issue 3, Page 1037-1091, July 2025.Abstract Spatial and spatiotemporal volatility models are a class of models designed to capture spatial dependence in the volatility of spatial and spatiotemporal data. Spatial dependence in the volatility may arise due to spatial spillovers among locations; that is, in the case of positive spatial dependence, if two locations are in close proximity ...
Philipp Otto +4 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
Modelling Volatility Cycles: The MF2‐GARCH Model
Journal of Applied Econometrics, Volume 40, Issue 4, Page 438-454, June/July 2025.ABSTRACT We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors.
Christian Conrad, Robert F. Engle
wiley +1 more source
Portfolio Selection under Systemic Risk
Journal of Money, Credit and Banking, Volume 57, Issue 4, Page 905-949, June 2025.Abstract This paper proposes a modified Sharpe ratio to construct optimal portfolios under systemic events. The portfolio allocation problem is solved analytically under the absence of short‐selling restrictions and numerically when short‐selling restrictions are imposed.
WEIDONG LIN +2 more
wiley +1 more source
Studying the Impact of Weather Anomaly and Air Pollution on Return of Tehran Stock Exchange Index [PDF]
راهبرد مدیریت مالی, 2014 One of the market anomalies which have been recently taken into consideration in financial literature is weather anomaly. In this study we attempted to determine the relationship between stock returns and weather variables such as temperature, cloud ...
reza raei +2 more
doaj +1 more source
International Journal of Mathematics and Mathematical Sciences, Volume 2025, Issue 1, 2025.Stock price forecasting is complex due to the nonlinear and nonstationary nature of financial time series. This study proposes a hybrid variational mode decomposition (VMD)–generalized autoregressive conditional heteroskedasticity (GARCH)–long short‐term memory (LSTM) model to predict Airtel’s stock prices, integrating VMD, GARCH, and LSTM networks ...
John Kamwele Mutinda +3 more
wiley +1 more source
Applied Stochastic Models in Business and Industry, Volume 40, Issue 6, Page 1689-1711, November/December 2024.ABSTRACT The Hawkes model is suitable for describing self and mutually exciting random events. In addition, the exponential decay in the Hawkes process allows us to calculate the moment properties of the model. However, owing to the complexity of the model and formula, few studies have examined the Hawkes volatility. In this study, we derive a variance
Kyungsub Lee
wiley +1 more source
Modeling and Forecasting Volatility of the Malaysian and the Singaporean stock indices using Asymmetric GARCH models and Non-normal Densities [PDF]
This paper examines and estimate the three GARCH(1,1) models (GARCH, EGARCH and GJR-GARCH) using the daily price data. Two Asian stock indices KLCI and STI are studied using daily data over a 14-years period.
Abu Hassan, Ahmed Shamiri
core
Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning
Journal of Forecasting, Volume 43, Issue 7, Page 2904-2916, November 2024.Abstract We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state‐of‐the‐art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms ...
Theo Berger, Jana Koubová
wiley +1 more source