Results 81 to 90 of about 16,252 (305)
GARCH-Type Models on the Volatility of Indonesian Cocoa’s Spot Price Returns
Jurnal Manajemen dan Wirausaha, 2011 Cocoa plays an important role in generating Indonesian foreign exchange revenues since it is one of Indonesia’s primary commodity exports. Meanwhile, as part of plantation commodity, cocoa’s price also has volatility nature.
Saarce Elsye Hatane
doaj
Journal of Time Series AnalysisGeneralized Autoregressive Conditional Heteroscedasticity (GARCH) model and its variations have been widely adopted in the study of financial volatilities, while the extension of GARCH‐type models to high‐dimensional data is always difficult because of over‐parameterization and computational complexity. In this article, we propose a multi‐variate GARCH‐
Pan, Yue, Pan, Jiazhu
openaire +4 more sources
Marginal Likelihood for Markov-Switching and Change-Point Garch Models [PDF]
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to
Arnaud Dufays +2 more
core
Dual‐Channel Interdigitated Aptamer‐Based Sensors for Rapid Small‐Molecule Detection in Biofluids
Angewandte Chemie International Edition, EarlyView.An electrochemical aptamer sensing chip employs dual interdigitated electrodes and selective self‐assembled monolayer removal to convert small‐molecule recognition into robust “signal‐off/on” responses. This dual‐channel signal conversion minimizes background noise and accelerates probe diffusion, enabling rapid (as fast as 5–30 min), low‐volume ...
Senyao Wang +12 more
wiley +1 more source
Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation [PDF]
In the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance.
Michael McAleer, Massimiliano Caporin
core +4 more sources
Empirical performance of GARCH, GARCH-M, GJR-GARCH and log-GARCH models for returns volatility
Journal of Physics: Conference Series, 2019 Abstract Volatility plays an important role in the field of financial econometrics as one of the risk indicators. Many various models address the problem of modeling the volatilities of financial asset returns. This study provides a new empirical performance comparison of the four different GARCH-type models, namely GARCH, GARCH-M, GJR ...
D B Nugroho +5 more
openaire +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
Chemistry – A European Journal, EarlyView.This work introduces the concept of pseudoglucosinolates (psGSLs) and reports the synthesis and evaluation of nitroreductase‐responsive psGSLs. These compounds represent a complementary prodrug strategy to natural glucosinolates (GSLs) for the controlled release of isothiocyanates (ITCs), enabling bio‐responsive protein labeling, as demonstrated in ...
Claire C. Jimidar +13 more
wiley +1 more source
RisksThe Value-at-Risk (VaR) metric serves as a pivotal tool for quantifying market risk, offering an estimation of potential investment losses. Predominantly employed within financial sectors, it aids in adhering to regulatory mandates and in devising ...
Danai Likitratcharoen +1 more
doaj +1 more source
GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks. [PDF]
Comput Econ, 2023 Buczynski M, Chlebus M.
europepmc +1 more source