Investor Happiness and Predictability of the Realized Volatility of Oil Price

Matteo Bonato,Konstantinos Gkillas,Rangan Gupta,Christian Pierdzioch

doi:10.3390/su12104309

Abstract

We use the the heterogeneous autoregressive realized volatility (HAR-RV) model to analyze both in sample and out-of-sample whether a measure of investor happiness predicts the daily realized volatility of oil-price returns, where we use high-frequency intraday data to measure realized volatility. Full-sample estimates reveal that realized volatility is significantly negatively linked to investor happiness at a short forecast horizon. Similarly, out-of-sample results indicate that investor happiness significantly improves the accuracy of forecasts of realized volatility at a short forecast horizon. Results for a medium and a long forecast horizon are insignificant. We argue that our results shed light on the role played by speculation in oil products and the potential function of oil-related products as a hedge against risks in traditional financial assets.

Highlights

The oil market’s recent financialization has led to increased participation of hedge funds, pension funds and insurance companies, in the market, rendering oil a profitable alternative investment in the portfolio decisions of financial institutions [1,2,3,4,5] (Bahloul et al, 2018, Bonato 2019)
We employ intraday data obtained from West Texas Intermediate (WTI) oil futures traded in NYMEX over a 24 h trading day to calculate daily measures of realized oil-price volatility as well as realized skewness and kurtosis
Our in-sample results for the full sample of data demonstrate that, when we use the heterogeneous autoregressive realized volatility (HAR-RV) model to capture the implications of the heterogeneous market hypothesis for the dynamics of realized volatility, the estimated coefficient of investor happiness, H A, has a negative sign and is highly significant for h = 1, while the coefficient becomes insignificant and turns positive for h = 5 and h = 22

Summary

Introduction

The oil market’s recent financialization has led to increased participation of hedge funds, pension funds and insurance companies, in the market, rendering oil a profitable alternative investment in the portfolio decisions of financial institutions [1,2,3,4,5] (Bahloul et al, 2018, Bonato 2019). Accurate estimates of oil-price volatility are of vital importance to oil traders. This is a concern from the policy perspective, as oil-price volatility has been shown to negatively impact economic activity as well since it captures macroeconomic uncertainty [6,7] (Elder and Serletis 2010, van Eyden et al, 2019). Oil-price fluctuations have many consequences for most non-energy producing companies by increasing the cost of doing business. A better econometric understanding of oil-price volatility is vital for its effective management and could lead to a competitive advantage by reducing operating costs and business risk. In the short term, it is economically important to proceed with a systematic characterization of the types of events that cause oil-price volatility to fluctuate over time. The impact of oil-price shocks and oil-price volatility has received great attention in the earlier literature ([9,10,11](see Jiang et al, 2018, Zao et al, 2019, Gkillas et al, 2020, among others)

Methods

Results

Conclusion