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波动率周期建模：MF2-GARCH 模型AI 解读

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. In contrast to other multiplicative component GARCH models, the MF2‐GARCH features stationary returns, and long‐term volatility forecasts are mean‐reverting. When applied to the S&P 500, the new component…

GARCH 模型中遗漏乘法型长期成分的检验AI 解读

We consider the problem of testing for an omitted multiplicative long-term component in GARCH-type models. Under the alternative, there is a two-component model with a short-term GARCH component that fluctuates around a smoothly time-varying long-term component which is driven by the dynamics of an explanatory variable. We suggest a Lagrange multiplier statistic for testing the null hypothesis that the variable has no explanatory power. We derive the asymptotic theory for…

双成分优于单成分？乘法成分GARCH-MIDAS模型的波动率预测AI 解读

Summary We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed‐data sampling (GARCH‐MIDAS) models suggested in Engle, Ghysels, and Sohn ( Review of Economics and Statistics , 2013, 95 , 776–797). In those models volatility is decomposed into a short‐term GARCH component and a long‐term component that is driven by an explanatory variable. We…

参数化 GARCH-in-Mean 模型的渐近理论AI 解读

In this paper we develop an asymptotic theory for the Quasi-Maximum Likelihood Estimator (QMLE) of the parametric GARCH-in-Mean model. The asymptotics is based on a study of the volatility as a process of the model parameters. The proof makes use of stochastic recurrence equations for this random function and uses exponential inequalities to localize the problem. Our results show why the asymptotics for this specification is quite complex although it is a rather standard…

预判长期股票市场波动率AI 解读

We investigate the relationship between long-term US stock market risks and the macroeconomic environment using a two-component GARCH-MIDAS model. Our results show that macroeconomic variables are important determinants of the secular component of stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate profits and the unemployment rate have the highest predictive ability for long-term stock market volatility. While…

双曲型 GARCH 模型的非负性条件AI 解读

In this article we derive conditions which ensure the non-negativity of the conditional variance in the Hyperbolic GARCH(p,d,q) (HYGARCH) model of Davidson (2004). The conditions are necessary and sufficient for p=1 and sufficient for p>=2 and emerge as natural extensions of the inequality constraints derived in Nelson and Cao (1992) and Tsai and Chan (2008) for the GARCH model and in Conrad and Haag (2006) for the FIGARCH model. As a by-product we obtain a representation of…

非受限 ECCC-GARCH 模型中的负向波动溢出AI 解读

This paper considers a formulation of the extended constant or time-varying conditional correlation GARCH model that allows for volatility feedback of either the positive or negative sign. In the previous literature, negative volatility spillovers were ruled out by the assumption that all the parameters of the model are nonnegative, which is a sufficient condition for ensuring the positive definiteness of the conditional covariance matrix. In order to allow for negative…