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Forecasting plausible scenarios and losses in interest rate targeting using mathematical optimization

Author

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  • Katsuhiro Tanaka

    (Keio University, Minato City, Tokyo 108-8345, Japan)

Abstract

This study proposes a mathematical optimization model for simultaneously forecasting plausible market scenarios and portfolio losses. Interest rates, volatilities and correlation coefficients can be modeled by the DCC-GARCH. A constraint condition is set by the Mahalanobis distance for deciding an acceptable range of change in interest rates. An objective function is set as a hypothetical market portfolio loss from delta, gamma and vega. The mathematical optimization model becomes a nonlinear programming problem for which it is difficult to find appropriate solutions. Therefore, the study introduces an original heuristic approach for preventing the signs of solutions from unintentionally becoming inverse. The study finds that, compared to a stressful scenario in Japan, the forecasting scenarios and losses are plausible.

Suggested Citation

  • Katsuhiro Tanaka, 2019. "Forecasting plausible scenarios and losses in interest rate targeting using mathematical optimization," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(03), pages 1-24, September.
    • Handle: RePEc:wsi:ijfexx:v:06:y:2019:i:03:n:s2424786319500257
    DOI: 10.1142/S2424786319500257
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    References listed on IDEAS

    as
    1. Ellen H. Fukuda & Masao Fukushima, 2016. "The Use of Squared Slack Variables in Nonlinear Second-Order Cone Programming," Journal of Optimization Theory and Applications, Springer, vol. 170(2), pages 394-418, August.
    2. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    3. Breuer, Thomas & Jandačka, Martin & Mencía, Javier & Summer, Martin, 2012. "A systematic approach to multi-period stress testing of portfolio credit risk," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 332-340.
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