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A classification of structured bond portfolio modeling techniques

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TLDR
The various approaches to modeling the assethability management problem are examined to help practitioners decide which methodology is appropriate for their specific problem, given the unique structure of their liabilities and the types of assets in their portfolio.
Abstract
0 ver the past decade, managing interest rate risk has become-a pivotal component of the portfolio management procedures of financial institutions. The volatility of interest rates in the early 1980s emphasized the importance of insulating portfolio values from unanticipated swings in interest rates. A flurry of activity contributed considerable refinements to existing assethability management techniques, including immunization techniques such as dedication and duration matching. This article examines the various approaches to modeling the assethability management problem. Our goal is to help practitioners decide which methodology is appropriate for their specific problem, given the unique structure of their liabilities and the types of assets in their portfolio. We consider a hypothetical financial institution with liabilities that must be met over time. To minimize financial risk, the institution intends to invest cash in assets that "match" the liabilities. The asset/ liability management problem is to identify a portfolio of assets (or to identify a feasible asset portfolio rebalancing strategy) that achieves this "match" so as to eliminate the interest rate risk from the combined assetniability portfolio. We classify assetniability problems as either deterministic or stochastic. Deterministic assethability problems are those for which 1) the liability cash flow stream is known with certainty in advance, and 2) the cash flows for all assets that may potentially be included in the portfolio are known with certainty, and do not depend in any way on factors such as the level ofinterest rates, exchange rates, or the general level of the economy. These two criteria are quite restrictive, excluding most realistic assethability management problems. All other problems are classified as stochastic problems; either the liability cash flows are uncertain, the asset cash flows are uncertain, or, in most instances, both. Much of the research in this area during the past decade has focused on analyzing the stochastic assetniability management problem. We also classify asset/liability management techniques according to the hedging criteria applied dedication techniques and duration-matching techniques. Dedication or cash-matching techniques focus on matching the liability cash flows over time. Duration-matching techniques focus on hedging the interest rate exposure in the liabilities by constructing an asset portfolio that matches the interest rate sensitivity of the liabilities. Table 1 categorizes assetniability management problems by problem type (deterministic or stochastic) and by hedging methodology (dedication or duration matching). Within each category, some references are provided to the literature. We discuss

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Journal ArticleDOI

Robust Optimization of Large-Scale Systems

TL;DR: This paper characterize the desirable properties of a solution to models, when the problem data are described by a set of scenarios for their value, instead of using point estimates, and develops a general model formulation, called robust optimization RO, that explicitly incorporates the conflicting objectives of solution and model robustness.
Journal ArticleDOI

Stochastic network programming for financial planning problems

TL;DR: In this paper, several financial planning problems are posed as dynamic generalized network models with stochastic parameters, including asset allocation for portfolio selection, international cash management, and programmed-trading arbitrage.
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Stochastic dedication: designing fixed income portfolios using massively parallel Benders decomposition

TL;DR: In this paper, a stochastic programming procedure is proposed for managing asset/liability portfolios with interest rate contingent claims, using scenario generation to combine deterministic dedication techniques with stochastically duration matching methods, and providing the portfolio manager with a risk/return Pareto optimal frontier from which a portfolio may be selected based on individual risk attitudes.
Journal ArticleDOI

Dynamic models for fixed-income portfolio management under uncertainty

TL;DR: In this paper, the authors developed multi-period dynamic models for fixed-income portfolio management under uncertainty, using multi-stage stochastic programming with recourse, and evaluated their performance vis-a-vis single-period models.
Journal ArticleDOI

A model for portfolio management with mortgage-backed securities

TL;DR: A stochastic programming model for the management of large portfolios of mortgage-backed securities (abbreviated: MBS) is presented, whereby portfolio decisions made here-and-now are influenced by uncertain information about the future.
References
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Journal ArticleDOI

A Theory of the Term Structure of Interest Rates.

TL;DR: In this paper, the authors use an intertemporal general equilibrium asset pricing model to study the term structure of interest rates and find that anticipations, risk aversion, investment alternatives, and preferences about the timing of consumption all play a role in determining bond prices.
Posted Content

Bond pricing and the term structure of interest rates: a new methodology for contingent claims valuation

TL;DR: In this article, a unifying theory for valuing contingent claims under a stochastic term structure of interest rates is presented, based on the equivalent martingale measure technique.
Journal ArticleDOI

L-shaped linear programs with applications to optimal control and stochastic programming.

TL;DR: An algorithm for L-shaped linear programs which arise naturally in optimal control problems with state constraints and stochastic linear programs (which can be represented in this form with an infinite number of linear constraints) is given.
Journal ArticleDOI

Term Structure Movements and Pricing Interest Rate Contingent Claims

TL;DR: In this paper, an arbitrage-free interest rate movements model (AR model) is proposed to price interest rate contingent claims relative to the observed complete term structure of interest rates.
Journal ArticleDOI

A continuous time approach to the pricing of bonds

TL;DR: The authors developed an arbitrage model of the term structure of interest rates based on the assumptions that the whole term structure at any point in time may be expressed as a function of the yields on the longest and shortest maturity default free instruments and that these two yields follow a Gauss-Wiener process.
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