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VaR methods for the dynamic impawn rate of steel in inventory financing under autocorrelative return

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  • Juan, He
  • Xianglin, Jiang
  • Jian, Wang
  • Daoli, Zhu
  • Lei, Zhen

Abstract

This paper proposes the way of setting the dynamic impawn rate by dividing the impawn periods into different risk windows. In an efficient financial market, the return is hypothetically independent, while in a pledged inventory market where spot transactions predominate, the return is auto-correlative. Therefore, the key to setting the impawn rate is to predict the long-term risk. In this experiment, using the database of spot steel, we established a model with the formula AR (1)-GARCH (1,1)-GED, forecasting the VaR of steel during the different risk windows in the impawn period through a method of out-of-sample, and got the impawn rate according with the risk exposure of banks. The results of our experiment indicated that the introduction of coefficient K into the model can significantly improve bank risk coverage and reduce its efficiency loss. Besides, the impawn rate obtained by the model correlates positively with the lowest price in the future risk windows.

Suggested Citation

  • Juan, He & Xianglin, Jiang & Jian, Wang & Daoli, Zhu & Lei, Zhen, 2012. "VaR methods for the dynamic impawn rate of steel in inventory financing under autocorrelative return," European Journal of Operational Research, Elsevier, vol. 223(1), pages 106-115.
    • Handle: RePEc:eee:ejores:v:223:y:2012:i:1:p:106-115
    DOI: 10.1016/j.ejor.2012.06.005
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    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    3. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. René Caldentey & Martin B. Haugh, 2009. "Supply Contracts with Financial Hedging," Operations Research, INFORMS, vol. 57(1), pages 47-65, February.
    6. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    7. Esa Jokivuolle & Samu Peura, 2003. "Incorporating Collateral Value Uncertainty in Loss Given Default Estimates and Loan‐to‐value Ratios," European Financial Management, European Financial Management Association, vol. 9(3), pages 299-314, September.
    8. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    9. Lee, Chang Hwan & Rhee, Byong-Duk, 2011. "Trade credit for supply chain coordination," European Journal of Operational Research, Elsevier, vol. 214(1), pages 136-146, October.
    10. Chen, Xiangfeng & Cai, Gangshu (George), 2011. "Joint logistics and financial services by a 3PL firm," European Journal of Operational Research, Elsevier, vol. 214(3), pages 579-587, November.
    11. John A. Buzacott & Rachel Q. Zhang, 2004. "Inventory Management with Asset-Based Financing," Management Science, INFORMS, vol. 50(9), pages 1274-1292, September.
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    Cited by:

    1. Jiao Wang & Lima Zhao & Arnd Huchzermeier, 2021. "Operations‐Finance Interface in Risk Management: Research Evolution and Opportunities," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 355-389, February.
    2. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2022. "Managing inventory financing in a volatile market: A novel data-driven copula model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    3. Hu, Haiqing & Chen, Di & Sui, Bo & Zhang, Lang & Wang, Yinyin, 2020. "Price volatility spillovers between supply chain and innovation of financial pledges in China," Economic Modelling, Elsevier, vol. 89(C), pages 397-413.
    4. Zhi, Bangdong & Wang, Xiaojun & Xu, Fangming, 2020. "Impawn rate optimisation in inventory financing: A canonical vine copula-based approach," International Journal of Production Economics, Elsevier, vol. 227(C).

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