A stochastic programming model for the optimal issuance of government bonds

Staino, A

Risultato della ricerca: Article

12 Citazioni (Scopus)

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 analyse the results obtained.
Lingua originaleEnglish
pagine (da-a)159-172
Numero di pagine13
RivistaAnnals of Operations Research
Volume193
Stato di pubblicazionePublished - 2012

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Government bonds
Conditional value at risk
Stochastic programming
Trade-offs
Public debt
Economic factors
Decision under uncertainty
Term structure
Costs
Efficient frontier
Planning
Fluctuations
Floating
Measure of risk
Multistage stochastic programming
Decision process
Decision maker

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Management Science and Operations Research

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A stochastic programming model for the optimal issuance of government bonds. / Staino, A.

In: Annals of Operations Research, Vol. 193, 2012, pag. 159-172.

Risultato della ricerca: Article

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AU - Consiglio, Andrea

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AB - 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 analyse the results obtained.

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