Seasonal volatility in agricultural markets: modelling and empirical investigations

This paper deals with the issue of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, we employ a maturity-dependent damping term to account for the Samuelson e...

Full description

Saved in:
Bibliographic Details
Published in:Annals of operations research 2024-03, Vol.334 (1-3), p.7-58
Main Authors: Schneider, L., Tavin, B.
Format: Article
Language:English
Subjects:
Agricultural commodities
Algorithms
Arbitrage
Business administration
Business and Management
Characteristic functions
Combinatorics
Cotton
Damping
Economics and Finance
Empirical analysis
Energy industry
Futures
Humanities and Social Sciences
Inventory
Kalman filters
Modelling
Operations research
Operations Research/Decision Theory
Original Research
Seasonal variations
Securities prices
Soybeans
State space models
Theory of Computation
Volatility
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper deals with the issue of modelling the volatility of futures prices in agricultural markets. We develop a multi-factor model in which the stochastic volatility dynamics incorporate a seasonal component. In addition, we employ a maturity-dependent damping term to account for the Samuelson effect. We give the conditions under which the volatility dynamics are well defined and obtain the joint characteristic function of a pair of futures prices. We then derive the state-space representation of our model in order to use the Kalman filter algorithm for estimation and prediction. The empirical analysis is carried out using daily futures data from 2007 to 2019 for corn, cotton, soybeans, sugar and wheat. In-sample, the seasonal models clearly outperform the nested non-seasonal models in all five markets. Out-of-sample, we predict volatility peaks with high accuracy for four of these five commodities.
ISSN:0254-5330
1572-9338
1572-9338
DOI:10.1007/s10479-021-04241-7