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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
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 223 (2012)
    Issue (Month): 1 ()
    Pages: 106-115

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    Handle: RePEc:eee:ejores:v:223:y:2012:i:1:p:106-115

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Finance; AR (1)-GARCH (1; 1)-GED; Long-term VaR forecasting; Dynamic impawn rate; Inventory financing;

    References

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    1. 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.
    2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    3. 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.
    4. John A. Buzacott & Rachel Q. Zhang, 2004. "Inventory Management with Asset-Based Financing," Management Science, INFORMS, vol. 50(9), pages 1274-1292, September.
    5. 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.).
    6. 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 542-47, August.
    7. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
    8. 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.
    9. 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.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, Elsevier.
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