Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
Forecasting volatility under fractality, regime-switching, long memory and student-t innovations
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DOI：
10.1016/j.csda.2010.03.005
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发表时间：
2010-11
期刊：
影响因子：
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通讯作者：
T. Lux;Leonardo Morales-Arias
中科院分区：
文献类型：
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作者：
T. Lux;Leonardo Morales-Arias
引入了具有学生t创新的资产收益的马尔可夫转换多重分形模型（MSM-t），作为对资产收益的马尔可夫转换多重分形模型（MSM）的扩展。MSM-t可以通过最大似然法（ML）和广义矩法（GMM）进行估计，波动率预测可以通过贝叶斯更新（ML）或最佳线性预测（GMM）进行。蒙特卡洛模拟表明，使用GMM加上线性预测导致较小的损失相比，最佳贝叶斯预测的基础上ML估计的效率。MSM-t模型的预测能力是在一个全面的面板预测分析与三个不同的横截面的资产在国家一级（所有股票股票指数，债券指数和真实的房地产安全指数）经验评估。经验预测的MSM-T模型相比，从高斯同行和其他波动模型的广义自回归条件异方差（GARCH）家庭。在平均绝对误差（均方误差）方面，MSM-t（高斯MSM）在所考虑的各种资产类别的大多数预测范围内主导所有其他模型。此外，从MSM和（部分集成）Gestival模型获得的预测组合提供了从单一模型预测的改进。
The Markov-switching Multifractal model of asset returns with Student-t innovations (MSM-t henceforth) is introduced as an extension to the Markov-switching Multifractal model of asset returns (MSM). The MSM-t can be estimated via Maximum Likelihood (ML) and Generalized Method of Moments (GMM) and volatility forecasting can be performed via Bayesian updating (ML) or best linear forecasts (GMM). Monte Carlo simulations show that using GMM plus linear forecasts leads to minor losses in efficiency compared to optimal Bayesian forecasts based on ML estimates. The forecasting capability of the MSM-t model is evaluated empirically in a comprehensive panel forecasting analysis with three different cross-sections of assets at the country level (all-share equity indices, bond indices and real estate security indices). Empirical forecasts of the MSM-t model are compared to those obtained from its Gaussian counterparts and other volatility models of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family. In terms of mean absolute errors (mean squared errors), the MSM-t (Gaussian MSM) dominates all other models at most forecasting horizons for the various asset classes considered. Furthermore, forecast combinations obtained from the MSM and (Fractionally Integrated) GARCH models provide an improvement upon forecasts from single models.