nep-for New Economics Papers
on Forecasting
Issue of 2024‒04‒22
two papers chosen by
Rob J Hyndman, Monash University
  1. Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach By Afees A. Salisu; Ahamuefula E.Oghonna; Rangan Gupta; Oguzhan Cepni
  2. Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty? By Matteo Bonato; Oguzhan Cepni; Rangan Gupta; Christian Pierdzioch
  1. By: Afees A. Salisu (Centre for Econometrics and Applied Research, Ibadan, Nigeria; Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Ahamuefula E.Oghonna (Centre for Econometrics and Applied Research, Ibadan, Nigeria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Ostim Technical University, Ankara, Turkiye)
    Abstract: In this paper, we employ the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of state-level stock returns in the United States (US) based on monthly metrics of oil price uncertainty (OPU), and relatively broader energy market-related uncertainty index (EUI). We find that over the daily period of (February) 1994 to (September) 2022 and various forecast horizons, in 37 out of the 50 states, the GARCH-MIDAS model with EUI can outperform the benchmark, i.e., the GARCH-MIDAS-realized volatility (RV), which in turn, holds for at most 18 cases under OPU. The statistical evidence is further strengthened when we are able to detect higher utlilty gains delivered for 42 states by the GARCH-MIDAS-EUI in comparison to the GARCH-MIDAS-RV. Our findings have important implications for investors and policymakers.
    Keywords: Monthly Oil Price and Energy Market Uncertainties, Daily State-Level Stock Returns Volatility, GARCH-MIDAS, Forecasting
    JEL: C32 C53 G10 Q02
    Date: 2024–03
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:202409&r=for
  2. By: Matteo Bonato (Department of Economics and Econometrics, University of Johannesburg, Auckland Park, South Africa; IPAG Business School, 184 Boulevard Saint-Germain, 75006 Paris, France.); Oguzhan Cepni (Department of Economics, Copenhagen Business School, Denmark; Ostim Technical University, Ankara, Turkiye); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)
    Abstract: We compare the contribution of various popular economic policy uncertainty (EPU) measures with that of widely-studied realized moments (realized leverage, realized skewness, realized kurtosis, realized good and bad volatilities, realized jumps, and realized up and down tail risks) to the performance of out-of-sample forecasts of stock market volatility of the United States (US) over the sample period from 2011 to 2023. To this end, we construct optimal forecasting models by combining the popular heterogeneous autoregressive realized volatility (HAR-RV) model with optimal stepwise predictor selection algorithms and shrinkage estimators (lasso, elastic net, and ridge regression), where we control for macroeconomic factors and sentiment as well. We find that realized moments improve out-of-sample forecasting performance relative to the baseline HAR-RV model. EPU measures do not add to forecasting performance beyond realized moments, and even deteriorate forecasting performance as the length of the forecast horizon increases. The punchline is that realized moments rather than EPU measures matter for forecasting stock market volatility.
    Keywords: Stock market, Volatility, Forecasting, Moments, Economic policy uncertainty
    JEL: C22 C53 G10 G17 D80
    Date: 2024–03
    URL: http://d.repec.org/n?u=RePEc:pre:wpaper:202408&r=for
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