Quantile forecast combinations in realised volatility prediction

Abstract

This paper tests whether it is possible to improve point, quantile and density forecasts of realised volatility by conditioning on a set of predictive variables. We employ quantile autoregressive models augmented with macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior point, quantile and density predictive performance relative to the univariate models and the autoregressive benchmark.

Implied volatility directional forecasting: a machine learning approach

“…The set of models for a fixed value of k is referred to as a complete subset and the authors propose to use equal-weighted combinations of the forecasts from all models within these subsets indexed by k. See, also, Meligkotsidou et al (2019) and Meligkotsidou et al (2021), who developed quantile forecast combination schemes for realized volatility prediction and a complete subset quantile regression approach for equity premium prediction.…”

Financial volatility modeling with option-implied information and important macro-factors

“…Corradi et al (2013) explore the economic impact on monthly returns, volatilities, and volatility risk-premia. Finally, Conrad and Loch (2015) test quarterly macro-regressors of daily conditional variance and Meligkotsidou et al (2019)…”

“…Besides the negative effect of activity indices, they demonstrate the positive unemployment impact on volatility and the negative consumer sentiment/confidence influence. Finally, Meligkotsidou et al (2019) choose monthly inflation to capture the macroeconomic stance and default spreads alongside several bond yield rates for the financial cycle effect on volatility forecasting.…”

Forecasting under model uncertainty: Non‐homogeneous hidden Markov models with Pòlya‐Gamma data augmentation

“…Financial volatility has been extensively studied in the literature due to its crucial role in various financial fields, such as asset pricing, risk management, investment and asset allocation among others, see Gospodinov et al [2006]. Several studies have considered predicting realized stock volatility using various financial and/or economic predictors (see for example, Mittnik et al [2015], Meligkotsidou et al [2019], Christiansen et al [2012], Paye [2012]).…”