A stochastic programming model for the optimal issuance of government bonds

Abstract

Sovereign states issue fixed and floating securities to fund their public debt. The value of such portfolios strongly depends on the fluctuations of the term structure of interest rates. This is a typical example of planning under uncertainty, where decisions have to be taken on the base of the key stochastic economic factors underneath the model.We propose a multistage stochastic programming model to select portfolios of bonds, where the aim of the decision maker is to minimize the cost of the decision process. At the same time, we bound the conditional Value-at-Risk, a measure of risk which accounts for the losses of the tail distribution. We build an efficient frontier to trade-off the optimal cost versus the conditional Value-at-Risk and analyze the results obtained.

Computational approaches and data analytics in financial services: A literature review

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A multistage stochastic programming framework for cardinality constrained portfolio optimization

“…Their study considers interest rates and equity returns as uncertain parameters, and uses no-arbitrage interest rate models and moment matching for scenario generation. Consiglio and Staino [7] propose a multistage stochastic programming model to select portfolios of bonds, minimizing the cost of the issuance of government bonds. Rocha and Kuhn [26] propose a multistage stochastic mean-variance optimization model for the management of a portfolio of electricity derivative contracts, whose role is to hedge the financial risk imposed by deregulation of electricity markets.…”

Turkish Treasury Simulation Model for Debt Strategy Analysis

“…Bolder and Rubin (2007) and Bolder and Deeley (2011) attempt to combine simulation and optimization approaches with the aim of approximating the debt management objective function through simulations by using function approximation algorithms and then to optimize this approximation. Balibek and Köksalan (2010) and Consiglio and Stanio (2010) suggest employment of stochastic programming models to formulate an issuance strategy. However, to the best of our knowledge, optimization models have not yet been put into practical use due to challenges with regard to their complex structure, which impedes interpretation and maintenance.…”