波动性(金融)
计量经济学
经济
地缘政治学
ARCH模型
股票市场
马尔可夫链
库存(枪支)
金融经济学
统计
数学
地理
政治
考古
法学
政治学
背景(考古学)
作者
Mawuli Segnon,Rangan Gupta,Bernd Wilfling
标识
DOI:10.1016/j.ijforecast.2022.11.007
摘要
We investigate the role of geopolitical risks in forecasting stock market volatility at monthly horizons within a robust autoregressive Markov-switching GARCH mixed-data-sampling (AR-MSGARCH-MIDAS) framework. Our approach accounts for structural breaks through regime switching and allows us to disentangle short- and long-run volatility components. We conduct an empirical out-of-sample forecasting analysis using (i) daily Dow Jones Industrial Average returns, and (ii) monthly sampled geopolitical risks and macroeconomic variables over a time span of 122 years. We find that the impact of geopolitical risks as explanatory variables for stock market volatility forecasts at monthly horizons hinges crucially on the specific prediction model chosen by the forecaster. After capturing the non-stationarities in the data via an MSGARCH framework, we do not find significant forecast accuracy improvements through the inclusion of geopolitical risk indices.
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