Coping with the Risk of Interest-Rate Fluctuations: Returns to Bondholders from Naive and Optimal Strategies

Fisher, Lawrence; Weil, Roman L.

doi:10.1086/295402

Cited by 272 publications

(127 citation statements)

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“…Blume, Keim, and Patel (1991) directly calculate betas with the S&P 500 for different periods using Scholes and Williams' (1977) and OLS-regressions of returns on government bonds and on low-grade bonds with at least ten years to maturity. They find beta factors for the government bonds ranging between 0.16 and 0.83 and betas for the low-grade bonds of 18 See Fisher and Weil (1971), Boquist, Racette, and Schlarbaum (1975), Lanstein andSharpe (1978), p. 657, Livingston (1978) and Cox, Ingersoll, and Ross (1979). 19 See Altman (1989), p. 913, Asquith, Mullins, and Wolff (1989), p. 928, and Blume, Keim, and Patel (1989), published (1991.…”

Section: Deriving Debt Betasmentioning

confidence: 99%

The Opportunity Cost of Capital of US Buyouts

Groh

Gottschalg

2008

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Section: Deriving Debt Betasmentioning

confidence: 99%

The Opportunity Cost of Capital of US Buyouts

Groh

Gottschalg

2008

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“…The classical approach to interest rate immunization of an insurer's liabilities is Redington's theory of immunization which is based on a deterministic shock to a flat yield curve [1]. Fisher and Weil [2] extended the analysis to a non-flat yield curve. Extensions of interest rate immunization to multiple liabilities and non-constant shocks as well as the application of linear programming techniques to select immunized bond portfolios are presented in Shiu [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Immunization and Hedging of Post Retirement Income Annuity Products

Liu

Sherris

2017

Risks

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Designing post retirement benefits requires access to appropriate investment instruments to manage the interest rate and longevity risks. Post retirement benefits are increasingly taken as a form of income benefit, either as a pension or an annuity. Pension funds and life insurers offer annuities generating long term liabilities linked to longevity. Risk management of life annuity portfolios for interest rate risks is well developed but the incorporation of longevity risk has received limited attention. We develop an immunization approach and a delta-gamma based hedging approach to manage the risks of adverse portfolio surplus using stochastic models for mortality and interest rates. We compare and assess the immunization and hedge effectiveness of fixed-income coupon bonds, annuity bonds, as well as longevity bonds, using simulations of the portfolio surplus for an annuity portfolio and a range of risk measures including value-at-risk. We show how fixed-income annuity bonds can more effectively match cash flows and provide additional hedge effectiveness over coupon bonds. Longevity bonds, including deferred longevity bonds, reduce risk significantly compared to coupon and annuity bonds, reflecting the long duration of the typical life annuity and the exposure to longevity risk. Longevity bonds are shown to be effective in immunizing surplus over short and long horizons. Delta gamma hedging is generally only effective over short horizons. The results of the paper have implications for how providers of post retirement income benefit streams can manage risks in demanding conditions where innovation in investment markets can support new products and increase the product range.

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“…He used multi-period returning planning to model desirability in discrete time and maximizing the mathematical expectancy of this function and showed that optimum portfolios could gain a certain percentage of the capital by optimum investment in each period (regardless of the initial capital). Fisher and Weil (1971) offered a model based on Radyngton model (Shiu, 1990) by eliminating the assumption of yield curve flatness. Merton (1969) extended Samuelson model for continuous periods.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of fuzzy logic and genetic algorithms models for portfolio optimization

Soureh¹,

Amanollahi²

2017

10.5267/j.msl

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Selection of optimum methods which have appropriate speed and precision for planning and decision-making has always been a challenge for investors and managers. One the most important concerns for them is investment planning and optimization for acquisition of desirable wealth under controlled risk with the best return. This paper proposes a model based on Markowitz theorem by considering the aforementioned limitations in order to help effective decisionsmaking for portfolio selection. Then, the model is investigated by fuzzy logic and genetic algorithms, for the optimization of the portfolio in selected active companies listed in Tehran Stock Exchange over the period 2012-2016 and the results of the above models are discussed. The results show that the two studied models had functional differences in portfolio optimization, its tools and the possibility of supplementing each other and their selection.

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Coping with the Risk of Interest-Rate Fluctuations: Returns to Bondholders from Naive and Optimal Strategies

Cited by 272 publications

References 0 publications

The Opportunity Cost of Capital of US Buyouts

The Opportunity Cost of Capital of US Buyouts

Immunization and Hedging of Post Retirement Income Annuity Products

Comparative evaluation of fuzzy logic and genetic algorithms models for portfolio optimization

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