Forecasting realized volatility of agricultural commodities

Abstract

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous AutoRegressive (HAR) model. We obtain tick-by-tick data for five widely traded agricultural commodities (Corn, Rough Rice, Soybeans, Sugar, and Wheat) from the CME/ICE. Real out-of-sample forecasts are produced for 1-up to 66-days ahead. Our in-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump components and incorporate leverage effects offer better fitting in the predictive regressions. However, we convincingly demonstrate that such HAR extensions do not offer any superior predictive ability in the out-of-sample results, since none of these extensions produce significantly better forecasts compared to the simple HAR model. Our results remain robust even when we evaluate them in a Value-at-Risk framework. Thus, there is no benefit by adding more complexity, related to volatility decomposition or relative transformations of volatility, in the forecasting models.

Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility

“…Understating asset market volatility is central for academicians, financial practitioners, and policymakers. Several studies forecast realized volatility for different asset classes including stock prices (Liang et al, 2020; Luo & Chen, 2020), exchange rates (Andersen et al, 2003), commodity prices (Degiannakis et al, 2020), precious metals (Lyócsa & Molnár, 2016), nonferrous metals (Todorova et al, 2014), as well as individual asset market volatility like oil market (Degiannakis & Filis, 2017; Ma et al, 2020) and corn futures market (Wu et al, 2015). Bucci (2017), provides a good review of the literature on forecasting realized volatility.…”

“…Answering these questions will insights into hedging risk for investor and policy design. Studying out‐of‐sample forecasting ability is also important because a strong in‐sample relationship, as established in the literature, does not always imply a similar relationship in the out‐of‐sample context (Degiannakis et al, 2020; Kishor & Marfatia, 2018).…”

On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks

“…In order to compare the models based on their forecasting performance, we define a relatively long out-of-sample window such that the Model Confidence Set (MCS) of Hansen et al (2011) Following recent literature in forecasting (Corsi, 2009, Corsi & Renò, 2012, Sévi, 2014, Degiannakis et al, 2020, Luo et al, 2019, forecasting realized volatilities over different horizons is carried out iteratively or on aggregated level. We obtain one-day ahead or short term (h = 1), one-week ahead or medium term (h = 5), and one-month ahead or long-term (h = 22) predictions for the predefined out-of-sample period.…”

“…At least two widely used approaches are established in literature. One approach aims to forecast the accumulated or average realized volatility over h days as carried out in Corsi (2009), Degiannakis & Filis (2017), Degiannakis et al (2020), andTodorova (2015) for example. The other approach is to obtain point forecasts for the h-day ahead realized volatility by iterating one-day ahead forecasts and utilizing these forecasts as pseudo-observations to obtain the next forecast (Marcellino et al, 2006).…”

Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach

“…In particular, models that account for the particular dynamics of the volatility of agricultural commodity prices appear to robustly improve probabilistic forecasts of price changes, as shown in Ramirez and Fadiga (2003). The importance of modelling changes in the variance of agricultural commodity prices to improve predictive power has led to research efforts aimed at optimizing the specification of their volatility dynamics (see, e.g., the recent contribution by Degiannakis et al, 2020).…”