Results 61 to 70 of about 886 (182)
Stock Return, Volume and Volatility in the EGARCH model
I use EGARCH model to study the asymmetric impact of negative and positive shocks on stock return volatility. I find the asymmetric effects exist and the impact on volatility of a negative shock is greater than that of a positive shock. Furthermore, I examine the dynamic relationship between returns, volume and volatility of stock index by introducing ...
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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 +2 more
wiley +1 more source
Aim: The main object of this study was to present a comparison between GARCH models, i.e. the standard GARCH model, asymmetric GJR-GARCH, and logarithmic EGARCH on exchange rate (IDR/USD) volatility.
Juwita Suwondo +3 more
doaj +1 more source
International Stock Forecasting Using Ensemble Deep Graph Models and Complex Network Analysis
International stock forecasting faces challenges as global stock markets become increasingly synchronized. This study develops a prediction model for 46 global stock prices by examining the complex interconnectedness of the global stock network. We propose a multilevel fusion approach that integrates technology and knowledge for accurate international ...
Sangjin Park +2 more
wiley +1 more source
THE DYNAMICS OF THE DOW JONES SUKUK VOLATILITY: EVIDENCE FROM EGARCH MODEL [PDF]
This paper aims to test the effect of asymmetric shocks on the volatility of the Dow Jones Sukuk. To this end, we applied the EGARCH model to give a clear idea of the effect of asymmetric shocks on the volatility of the sukuk.
Nadhem SELMI +2 more
doaj
This paper proposes a wavelet‐based framework to improve parameter estimation and forecasting performance in combined ARIMA–GARCH models for nonlinear and non‐normal time series with time‐varying variance. Although standard ARIMA–GARCH models are widely used to describe conditional mean and volatility dynamics, they may fail to capture localized and ...
Najlaa Saad Ibrahim Alsharabi +3 more
wiley +1 more source
Volatility Modeling of Currency Returns: A Bayesian Multivariate GARCH‐EVT Framework
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 +4 more
wiley +1 more source
Bitcoin ve Ethereum Piyasasında Takvim Anomalilerinin İncelenmesi
Modern finans teorisinin köşe taşlarından biri olan Etkin Piyasa Hipotezi, piyasada mevcut olan tüm bilginin kullanılması suretiyle piyasanın üzerinde getiri elde edilemeyeceğini öne sürmektedir. Bununla birlikte finansal piyasalarda yapılan çalışmaların
Arzu Özmerdivanlı
doaj +1 more source
Asymmetric stable stochastic volatility models: estimation, filtering, and forecasting
This article considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation‐based methods to address key challenges in parameter estimation, the filtering of time‐varying volatility, and volatility forecasting.
Francisco Blasques +2 more
wiley +1 more source
Modeling the Interactions between Volatility and Returns using EGARCH‐M
An EGARCH‐M model, in which the logarithm of scale is driven by the score of the conditional distribution, is shown to be theoretically tractable as well as practically useful. A two‐component extension makes it possible to distinguish between the short‐ and long‐run effects of returns on volatility, and the resulting short‐ and long‐run volatility ...
Lange, Rutger-Jan, Harvey, AC
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