Results 31 to 40 of about 27,670 (236)

Exploring the Dynamic Links between GCC Sukuk and Commodity Market Volatility

International Journal of Financial Studies, 2018
This study investigates the impact of commodity price volatility (including soft commodities, precious metals, industrial metals, and energy) on the dynamics of corporate sukuk returns.
Nader Naifar
doaj   +1 more source

Discussing energy volatility and policy in the aftermath of the Russia–Ukraine conflict

Frontiers in Environmental Science, 2023
The ongoing Russo–Ukrainian War has highly affected energy markets in the EU and worldwide, with different EU- and country-level emergency policy measures being advanced to tackle high energy prices.
Adrian-Gabriel Enescu, Monica Răileanu Szeles   +1 more
doaj   +1 more source

Computing (R, S) policies with correlated demand [PDF]

, 2018
This paper considers the single-item single-stocking non-stationary stochastic lot-sizing problem under correlated demand. By operating under a nonstationary (R, S) policy, in which R denote the reorder period and S the associated order-up-to-level, we ...
Martin-Barragan, Belen, Rossi, Roberto, Tarim, S. Armagan, Xiang, Mengyuan   +3 more
core   +4 more sources

Beyond Symbolism: Examining the Impact of Sustainable Finance on Product Responsibility and the Moderating Role of Board Environmental Expertise—Evidence From UK Non‐Financial Firms

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Despite the growing emphasis on sustainable finance in today's corporate landscape, its impact on product responsibility remains underexplored, particularly the moderating role of board environmental expertise. This study addresses these gaps by examining non‐financial companies listed on the London Stock Exchange, chosen for the UK's ...
Bright Akwasi Gyamfi, Richard Arhinful, Leviticus Mensah, Hayford Asare Obeng, Simplice A. Asongu   +4 more
wiley   +1 more source

Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

Frontiers in Public Health, 2021
The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures.
Yinpeng Zhang, Panpan Zhu, Yingying Xu
doaj   +1 more source

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer, Kristofer Månsson, Pär Sjölander, Gazi Salah Uddin   +3 more
wiley   +1 more source

Volatility Modeling and Spillover: The Turkish and Russian Stock Markets

Istanbul Business Research
This study investigates the internal and external (spillover) characteristics of the volatility of the Turkish and Russian stock market indices. To this end, generalized autoregressive conditional heteroskedasticity models that are classified as short ...
Ahmet Galip Gençyürek
doaj   +1 more source

Forecasting gains by using extreme value theory with realised GARCH filter

IIMB Management Review, 2021
Early empirical evidence suggests that the realised generalised autoregressive conditional heteroskedasticity (GARCH) model provides significant forecasting gains over the standard GARCH models in volatility forecasting.
Samit Paul, Prateek Sharma
doaj   +1 more source

Option pricing under linear autoregressive dynamics, heteroskedasticity, and conditional leptokurtosis [PDF]

Journal of Empirical Finance, 2001
Daily returns of financial assets are frequently found to exhibit positive autocorrelation at lag 1. When specifying a linear AR(1) conditional mean, one may ask how this predictability affects option prices. We investigate the dependence of option prices on autoregressive dynamics under stylized facts of stock returns, i.e.
Hafner, Christian M., Herwartz, Helmut
openaire   +4 more sources

A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley   +1 more source