Author(s)

Pin Shen ^1,2, Xiaolu Hu ^1,2, Lina Jiang ²

Affiliation(s)

¹ School of Accounting, Guangzhou College of Commerce, Guangzhou, 511363, China
² Faculty of Finance, City University of Macau, Macau, 999078, China

Corresponding Author

Lina Jiang

ABSTRACT

This paper estimates the Value at Risk (VaR) of Shanghai INE crude oil futures using GARCH models based on 1403 daily data points. The study finds that the INE crude oil futures return series (RETURN) is stationery and exhibits left-skew, leptokurtosis (peaked), and heavy tails, along with weak autocorrelation. Volatility demonstrates a "leverage effect," where bad news generates greater volatility than equivalent good news. At a 95% confidence level, the VaR is 4.634% of the asset's market value, indicating significant risk. Furthermore, the EGARCH-T model is shown to accurately estimate this risk. This research provides a valuable reference for investors in assessing risk and formulating strategies.

KEYWORDS

GARCH Model; VaR; Crude Oil Futures; Risk Measurement

CITE THIS PAPER

Pin Shen, Xiaolu Hu, Lina Jiang, GARCH-Based VaR Estimation for the INE Crude Oil Futures Market. Financial Engineering and Risk Management (2025) Vol. 8: 205-217. DOI: http://dx.doi.org/10.23977/ferm.2025.080224.

REFERENCES

[1] Alexander, C., & Dimitriu, A. (2005). Indexing and statistical arbitrage. Journal of Portfolio Management, 18(1), 50–64.
[2] Alexander, G. J., & Baptista, A. M. (2002). Economic implications of using a mean-VaR model for portfolio selection: A comparison with mean-variance analysis. Journal of Economic Dynamics and Control, 26(7–8), 1159–1193.
[3] Aloui, C., & Mabrouk, S. (2010). Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy, 38(5), 2326–2339.
[4] Chen, K. (2019). Research on statistical arbitrage strategies of stock index futures based on GARCH family models (Master’s thesis). Guangdong University of Finance and Economics.
[5] Chen, Q. (2019). An empirical analysis of statistical arbitrage strategies based on cointegration theory. Western Finance, (1), 37–41.
[6] Ding, Y., & Cao, J. (2017). Design of stock pairs trading strategies based on GARCH models: Evidence from the infrastructure industry. Modern Business, (21), 87–89.
[7] Fan, Y. (2000). An exploratory study of the VaR method and its application in stock market risk analysis. Chinese Journal of Management Science, 8(3), 27–33.
[8] Fan, Y., et al. (2008). Estimating value at risk of crude oil price and its spillover effect using the GED-GARCH approach. Energy Economics, 30(6), 3156–3171.
[9] Feng, C., Wu, J., & Jiang, F. (2004). Calculating value at risk in the oil market using a semiparametric method. Journal of Hubei University (Natural Science Edition), 26(3), 213–217.
[10] Fernando, C., & Werner, K. (2020). Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets. International Journal of Finance & Economics, 26(3), 17–23.
[11] Gong, C., & Chen, Y. (2022). An empirical analysis of factors influencing regional economic growth in Liaoning based on a VAR model. Journal of Liaodong University (Social Sciences Edition), 24(5), 55–62.
[12] Han, Z. (2019). Application of the AR-GARCH model in securities arbitrage. Times Finance, 23(35), 67–68.
[13] He, S., Sun, W., & Xu, W. (2010). Analysis and calculation of value at risk for international crude oil prices. Statistics and Decision, (18), 144–146.
[14] He, S., Zhang, Y., & Zhang, W. (2013). Empirical research on cointegration-based intertemporal arbitrage in stock index futures using GARCH models. Mathematics in Practice and Theory, 43(20), 274–279.
[15] Ma, C., Li, H., Zhou, E., Yang, X., Xu, S., & Zhang, Y. (2001). Value-at-risk method and its empirical analysis. Chinese Journal of Management Science, (5), 16–23.
[16] Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.
[17] Nicolas, H. (2019). Large data sets and machine learning: Applications to statistical arbitrage. European Journal of Operational Research, 18(1), 278–281.
[18] Pan, H., & Zhang, J. (2006). Measuring extreme risk in the oil market using VaR. Operations Research and Management, 15(5).
[19] Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425–442.
[20] Tian, X., Liu, H., & Li, Y. (2003). Value-at-Risk calculation under generalized error distribution (GED) in Shanghai and Shenzhen stock markets. Journal of Management Engineering, 17(1), 4.
[21] Vo, M. T. (2009). Regime-switching stochastic volatility: Evidence from the crude oil market. Energy Economics, 31(5), 779–788.
[22] Wang, S. S., Young, V. R., & Panjer, H. H. (1997). Axiomatic characterization of insurance prices. Insurance: Mathematics and Economics, 21(2), 173–183.
[23] Wang, Z., & Chen, H. (2019). An empirical study of CPI and PPI based on a VAR model. Enterprise Science and Technology & Development, (2), 76–77.
[24] Wei, Y., Wang, Y. D., & Huang, D. S. (2010). Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics, 32(6), 1477–1484.
[25] Yu, W., Fan, Y., & Wei, Y. (2007). Value-at-risk of crude oil price based on extreme value theory. Systems Engineering—Theory & Practice, (8), 12–20.
[26] Zhou, X., Li, Q., & Zhang, B. (2012). Measuring risk of world crude oil prices based on an EGARCH-EVT-t Copula model. Journal of Beijing Institute of Technology (Social Sciences Edition), (4), 10–16.

Sponsors, Associates, and Links

---

• Information Systems and Economics
  
  
• Accounting, Auditing and Finance
  
  
• Industrial Engineering and Innovation Management
  
  
• Tourism Management and Technology Economy
  
  
• Journal of Computational and Financial Econometrics
  
  
• Accounting and Corporate Management
  
  
• Social Security and Administration Management
  
  
• Population, Resources & Environmental Economics
  
  
• Statistics & Quantitative Economics
  
  
• Agricultural & Forestry Economics and Management
  
  
• Social Medicine and Health Management
  
  
• Land Resource Management
  
  
• Information, Library and Archival Science
  
  
• Journal of Human Resource Development
  
  
• Manufacturing and Service Operations Management
  
  
• Operational Research and Cybernetics