Journal of Empirical Finance · VOLUME 42 · June 2017

使用金融和宏观经济指标预测总体股市波动率：哪个模型预测的最准确？在什么时候？为什么？

作者：Nima Nonejad (Aalborg University and CREATES, Denmark)

摘要：本文在一个全面的贝叶斯模型平均的框架下对使用金融和宏观经济指标预测总体股市波动率这个话题进行了再讨论，使用到的模型有时变（使用不同的动态度）和常数系数的自回归模型，这些模型都是使用经过代表风险溢价、杠杆、债券收益率和信用风险的外生预测指标增强后的月度实现波动率的对数形式进行研究的。因此本文同时解决了参数非稳定性和模型不确定性这两个必然会影响波动率预测的问题。把模型应用到1926年到2010年的标普500指数月度波动率数据，作者发现相比其他方法，使用时变回归系数的贝叶斯模型平均方法可以提供更好的密度预测，并且在点估计方面也会有一定改进。

关键词：贝叶斯模型平均、预测、模型不确定性、参数不稳定性、实现波动率

Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?

Nima Nonejad(Aalborg University and CREATES, Denmark)

ABSTRACT

This paper revisits the topic of forecasting aggregate stock market volatility using financial and macroeconomic predictors in a comprehensive Bayesian model averaging framework. Candidate models include time-varying (with various degrees of dynamics) and constant-coefficient autoregressions based on the logarithm of monthly realized volatility augmented with exogenous predictors capturing risk premia, leverage, bond rates and proxies for credit risk. Thus, we simultaneously address parameter instability and model uncertainty that unavoidably impact volatility predictions. Applied to monthly S&P 500 volatility from 1926 to 2010, we find that Bayesian model averaging with time-varying regression coefficients provides very competitive density and modest improvements in point forecasts compared to rival approaches.

Keywords: Bayesian model averaging; Forecasting; Model uncertainty; Parameter instability; Realized volatility

原文链接：http://www.sciencedirect.com/science/article/pii/S0927539817300269

翻译：殷曼琳