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Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

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Author Info

  • David Jamieson Bolder
  • Tiago Rubin

Abstract

The stochastic simulation model suggested by Bolder (2003) for the analysis of the federal government's debt-management strategy provides a wide variety of useful information. It does not, however, assist in determining an optimal debt-management strategy for the government in its current form. Including optimization in the debt-strategy model would be useful, since it could substantially broaden the range of policy questions that can be addressed. Finding such an optimal strategy is nonetheless complicated by two challenges. First, performing optimization with traditional techniques in a simulation setting is computationally intractable. Second, it is necessary to define precisely what one means by an "optimal" debt strategy. The authors detail a possible approach for addressing these two challenges. They address the first challenge by approximating the numerically computed objective function using a function-approximation technique. They consider the use of ordinary least squares, kernel regression, multivariate adaptive regression splines, and projection-pursuit regressions as approximation algorithms. The second challenge is addressed by proposing a wide range of possible government objective functions and examining them in the context of an illustrative example. The authors' view is that the approach permits debt and fiscal managers to address a number of policy questions that could not be fully addressed with the current stochastic simulation engine.

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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 07-13.

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Length: 92 pages
Date of creation: 2007
Date of revision:
Handle: RePEc:bca:bocawp:07-13

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Related research

Keywords: Debt management; Econometric and statistical methods; Fiscal policy; Financial markets;

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References

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  1. Bolder, David & Streliski, David, 1999. "Yield Curve Modelling at the Bank of Canada," Technical Reports 84, Bank of Canada.
  2. David Jamieson Bolder, 2001. "Affine Term-Structure Models: Theory and Implementation," Working Papers 01-15, Bank of Canada.
  3. David Jamieson Bolder, 2006. "Modelling Term-Structure Dynamics for Risk Management: A Practitioner's Perspective," Working Papers 06-48, Bank of Canada.
  4. Peter Sephton, 2005. "Forecasting inflation using the term structure and MARS," Applied Economics Letters, Taylor & Francis Journals, vol. 12(4), pages 199-202.
  5. Peter Sephton, 2001. "Forecasting recessions: can we do better on MARS?," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 39-49.
  6. David J. Bolder & Grahame Johnson & Adam Metzler, 2004. "An Empirical Analysis of the Canadian Term Structure of Zero-Coupon Interest Rates," Working Papers 04-48, Bank of Canada.
  7. David Jamieson Bolder, 2003. "A Stochastic Simulation Framework for the Government of Canada's Debt Strategy," Working Papers 03-10, Bank of Canada.
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Citations

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Cited by:
  1. David Jamieson Bolder & Simon Deeley, 2011. "The Canadian Debt-Strategy Model: An Overview of the Principal Elements," Discussion Papers 11-3, Bank of Canada.
  2. Johannes Holler, 2013. "Funding Strategies of Sovereign Debt Management: A Risk Focus," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 51–74.
  3. K. Batu Tunay, 2010. "Banking Crises and Early Warning Systems: A Model Suggestion for Turkish Banking Sector," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 4(1), pages 9-46.
  4. David Jamieson Bolder & Yuliya Romanyuk, 2008. "Combining Canadian Interest-Rate Forecasts," Working Papers 08-34, Bank of Canada.
  5. Udaibir S. Das & Yinqiu Lu & Michael G Papaioannou & Iva Petrova, 2012. "Sovereign Risk and Asset and Liability Management," IMF Working Papers 12/241, International Monetary Fund.

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