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Estimation of VaR with jump process: application in corn and soybean markets

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  • Minglian Lin
  • Indranil SenGupta
  • William Wilson

Abstract

Value at Risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a specific time period. The objective of this paper is to construct a model and estimate the VaR for a diversified portfolio consisting of multiple cash commodity positions driven by standard Brownian motions and jump processes. Subsequently, a thorough analytical estimation of the VaR is conducted for the proposed model. The results are then applied to two distinct commodities -- corn and soybean -- enabling a comprehensive comparison of the VaR values in the presence and absence of jumps.

Suggested Citation

  • Minglian Lin & Indranil SenGupta & William Wilson, 2023. "Estimation of VaR with jump process: application in corn and soybean markets," Papers 2311.00832, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2311.00832
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    References listed on IDEAS

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    1. Humayra Shoshi & Erik Hanson & William Nganje & Indranil SenGupta, 2021. "Stochastic Analysis and Neural Network-Based Yield Prediction with Precision Agriculture," JRFM, MDPI, vol. 14(9), pages 1-17, August.
    2. Shige Peng & Shuzhen Yang & Jianfeng Yao, 2023. "Improving Value-at-Risk Prediction Under Model Uncertainty," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 228-259.
    3. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
    4. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    5. Minglian Lin & Indranil SenGupta, 2021. "Analysis of optimal portfolio on finite and small time horizons for a stochastic volatility market model," Papers 2104.06293, arXiv.org.
    6. Christoffersen, Peter & Hahn, Jinyong & Inoue, Atsushi, 2001. "Testing and comparing Value-at-Risk measures," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 325-342, July.
    7. Fahim Afzal & Pan Haiying & Farman Afzal & Asif Mahmood & Amir Ikram, 2021. "Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    8. Kamrud, Gwen & Wilson, William W. & Bullock, David W., 2023. "Logistics competition between the U.S. and Brazil for soybean shipments to China: An optimized Monte Carlo simulation approach," Journal of Commodity Markets, Elsevier, vol. 31(C).
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