efficient method of momentsLinear GARCH(1,1) and GJR GARCH(1,1) processes are established as regularly varying, meaning their heavy tails follow a Power Law, under conditions that allow tdoi:10.2139/ssrn.3013353Prono, ToddSocial Science Electronic Publishing...
Bayesian Estimation of the Markov-Switching GJR(1, 1) Model with Student-t InnovationsIn this chapter, we address the problem of estimating GARCH models subject to structural changes in the parameters; namely, the Markov-switching GARCH models (henceforth MS-GARCH). In this framework, a hidden...
Financial Volatility Forecasting by Least Square Support Vector Machine Based on GARCH, EGARCH and GJR Models: Evidence from ASEAN Stock Markets, J. International Journal of Economics & Finance, 2010, 2(1): 337-367.Phichhang O., & Hengshan W. (2010). Financial Volatility Forecasting by ...
S. 2011. Modeling and Forecasting Stock Market Volatility by Gaussian Processes based on GARCH, EGARCH and GJR Models. World Congress on Engineering 2011. 1 (7), p. 1.Ou, P., Wang, H.: Modeling and forecasting stock market volatility by Gaussian processes based on GARCH, EGARCH and GJ...
摘要：在Fama-French四因子定价模型的基础上,运用Fama-French-ARMAGJR-GARCH-AEPD模型进行实证分析,结果表明此模型不仅能更好地拟合次贷危机影响下下跌的美股与复苏美股的收益率、消除自相关与ARCH效应问题,而且AEPD分布既能捕捉到残差的偏度又能获得残差的左尾和右尾分布情况。以"日"为单位,用固定长度的滚动时间...
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Moments for the most commonly used GARCH models are stated as special cases. We also derive the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments. A simulation study using these analytic ...
S.Esipov and D.Guo, Portfolio-Based Risk Pricing: Pricing Long-Term Put Options with GJR-GARCH(1,1)/Jump Diffusion Process, forthcoming paper in the Proceeding of the Seminar on Mathematical Finance at Courant Institute, World Scientific, Singapore (1998)...
GARCH(l,l)/jump-diffusion model#Parameters for the GJR-GARCH(l,l)/jump-diffusion model#Pricing S&P 500 Index Put OptionsOption parameters for one-year European putsFrom distributions to pricesVolatility smileComparison with historical market price of puts in 1988–1997Five-year European puts#...
GJR-GARCH模型本文使用我国上海期货交易所沪胶指数日收盘价数据,采用GJR-GARCH模型对我国天然橡胶期货收益率序列基本统计特征和杠杆效应进行研究。我们使用学生t分布来处理天然橡胶收益率序列的尾部特征,结果表明,我国天然橡胶收益率序列存在明显的"尖峰厚尾"分布,收益率波动具有非对称性,即存在杠杆效应,当天然橡胶收益率...