The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence

• Author: Yaojie Zhang,Xiaodong Lai,M. I. M. Wahab,Feng Ma
• Published date: 01 August 2019
• Date: 01 August 2019
• DOI: http://doi.org/10.1002/for.2569

RESEARCH ARTICLE

The role of jumps in the agricultural futures market on

forecasting stock market volatility: New evidence

Feng Ma

1

| Yaojie Zhang

3

| M. I. M. Wahab

2

| Xiaodong Lai

1

1

School of Economics and Management,

Southwest Jiaotong University, Chengdu,

China

2

Department of Mechanical and

Industrial Engineering, Ryerson

University, Toronto, Ontario, Canada

3

School of Economics and Management,

Nanjing University of Science and

Technology, Nanjing, China

Correspondence

M. I. M. Wahab, Department of

Mechanical and Industrial Engineering,

Ryerson University, 350 Victoria Street,

Toronto, Ontario M5B 2K3, Canada.

Email: wahab@ryerson.ca

Funding information

the Fundamental Research Funds for the

Central Universities, Grant/Award Num-

bers: 2682018WXTD05 and

2682017WCX01; the Humanities and

Social Science Fund of the Ministry of

Education, Grant/Award Numbers:

17XJCZH002 and 17YJC790105; the Nat-

ural Science Foundation of China, Grant/

Award Numbers: 71701170 and 71671145

Abstract

In this study, we explore the effect of cojumps within the agricultural futures

market, and cojumps between the agricultural futures market and the stock

market, on stock volatility forecasting. Also, we take into account large and

small components of cojumps. We have several noteworthy findings. First,

large jumps may lead to more substantial fluctuations and are more powerful

than small jumps. The effect of cojumps and their decompositions on future

volatility are mixed. Second, a model including large and small cojumps

between the agricultural futures market and the stock market can achieve a

higher forecasting accuracy, implying that large and small cojumps contain

more useful predictive information than cojumps themselves. Third, our con-

clusions are robust based on various robustness tests such as the realized ker-

nel, expanding forecasts, different forecasting windows, different jump tests,

and different threshold values.

KEYWORDS

agricultural futures, cojumps, forecasting evaluation, jumps, volatility forecasting

1|INTRODUCTION

In recent years, with the availability of high‐frequency

(intraday) data, the modeling and forecasting of financial

market volatility have taken a new direction (Ma, Wei, Liu,

& Huang, 2018; Patton & Sheppard, 2015). Previous studies

(e.g., Koopman, Jungbacker, & Hol, 2005; Martens & Zein,

2004; Pu, Chen, & Ma, 2016; Wei, 2012) empirically find that

the traditional models, such as generalized autoregressive

conditional heteroskedasticity (GARCH) and its extended

models, have a lower predictive power compared to predic-

tion models using high‐frequency data. Hence high‐

frequency data contain a wealth of information that can help

market participants make quicker decisions compared to

low‐frequency data such as daily or monthly data.

An influential study by Andersen and Bollerslev

(1998) constructs volatility using high‐frequency data

called realized variance (RV), which is robust to market

microstructure effects from both a theoretical and an

empirical point of view. The main advantage of RV is that

it is directly observable. Hence it enables researchers to

measure and understand its dynamics. It is worth noting

that volatility is a crucial input of derivative pricing,

hedging, portfolio selection, and risk management (see,

e.g., Andersen, Bollerslev, & Diebold, 2007; Bollerslev,

Hood, Huss, & Pedersen, 2017). Therefore, a large body

of literature describes and forecasts the RV of stock mar-

kets (see, e.g., Choi & Shin, 2018; Corsi, 2009; Engle &

Gallo, 2006; Ghysels, Santa‐Clara, & Valkanov, 2006;

Hansen, Huang, & Shek, 2012; Patton & Sheppard,

Received: 23 May 2018 Revised: 11 September 2018 Accepted: 3 January 2019

DOI: 10.1002/for.2569

400 © 2019 John Wiley & Sons, Ltd. Journal of Forecasting. 2019;38:400–414.wileyonlinelibrary.com/journal/for