Abstract

This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting volatility of stock markets. In this regard, using the Markov-switching multifractal (MSM) model, we find that investors’ uncertainty can substantially increase the accuracy of the forecasts of stock market volatility according to the forecast encompassing test. We further provide evidence that the MSM outperforms the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model.

中文翻译：

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投资者的不确定性和预测股市波动

摘要

本文研究了结合投资者的不确定性，如情绪的条件波动所捕捉到的，是否有助于预测股市的波动。对此，利用马尔可夫切换多重分形（MSM）模型，我们发现投资者的不确定性可以大大提高根据预测包含检验预测股市波动的准确性。我们进一步提供了 MSM 优于动态条件相关广义自回归条件异方差 (DCC-GARCH) 模型的证据。