When Can Life-Cycle Investors Benefit from Time-Varying Bond Risk Premia?

Koijen, Ralph S. J.; Nijman, Theo; Werker, Bas J. M.

doi:10.2139/ssrn.795925

Cited by 42 publications

(74 citation statements)

References 48 publications

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“…We have also implemented the method by Koijen et al (2007Koijen et al ( , 2010) on a discrete-time version of our benchmark problem. Their method involves the construction of an endogenous grid of the wealth-income ratio (after consumption), which is the only state variable for our problem because of homogeneity of the utility function.…”

Section: Stock-income Correlation %mentioning

confidence: 99%

“…The optimal strategies derived by our method and their method are very close, whereas the strategy derived by the MCA approach deviates somewhat for high values of the wealth-income ratio. For models with additional state variables, the complexity and computation time of the Koijen et al (2007Koijen et al ( , 2010 method grow considerably because it then involves simulation-based regressions for the approximation of conditional expectations. Furthermore, we emphasize again that our method relies on closed-form strategies unlike the alternative methods.…”

Section: Stock-income Correlation %mentioning

confidence: 99%

“…An alternative Monte Carlo approach to solving portfolio problems builds on the American option pricing method of Carriere (1996) and Longstaff and Schwartz (2001). This approach was developed by Brandt et al (2005), Bouchard et al (2004), van Binsbergen andBrandt (2007), and Koijen et al (2007Koijen et al ( , 2010, among others. For comparison with our SAMS approach, we implement the method of Koijen et al (2007Koijen et al ( , 2010 because this was formulated directly for a lifetime consumption-investment problem.…”

Section: Introductionmentioning

confidence: 99%

“…This approach was developed by Brandt et al (2005), Bouchard et al (2004), van Binsbergen andBrandt (2007), and Koijen et al (2007Koijen et al ( , 2010, among others. For comparison with our SAMS approach, we implement the method of Koijen et al (2007Koijen et al ( , 2010 because this was formulated directly for a lifetime consumption-investment problem. In contrast to our approach, this method does not deliver explicit solutions for the optimal consumption-investment choice.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Solving Constrained Consumption–Investment Problems by Simulation of Artificial Market Strategies

2013

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U tility-maximizing consumption and investment strategies in closed form are unknown for realistic settings involving portfolio constraints, incomplete markets, and potentially a high number of state variables. Standard numerical methods are hard to implement in such cases. We propose a numerical procedure that combines the abstract idea of artificial, unconstrained complete markets, well-known closed-form solutions in affine or quadratic return models, straightforward Monte Carlo simulation, and a standard iterative optimization routine. Our method provides an upper bound on the wealth-equivalent loss compared to the unknown optimal strategy, and it facilitates our understanding of the economic forces at play by building on closed-form expressions for the strategies considered. We illustrate and test our method on the life-cycle problem of an individual who receives unspanned labor income and cannot borrow or short sell. The upper loss bound is small, and our method performs well in comparison with two existing methods.

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Section: Stock-income Correlation %mentioning

confidence: 99%

Section: Stock-income Correlation %mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving Constrained Consumption–Investment Problems by Simulation of Artificial Market Strategies

2013

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“…Sangvinatsos and Wachter (2005) show that unconstrained long-term investors (as considered in section 3) can realize large gains by exploiting these time varia-tion s in bond premia. Koijen et al (2006b) consider the case of borrowing constrained life-cycle investors explored in section 4. They find that short-sell constraints reduce the potential gains associated with bond timing strategies because these gains can be obtained only at the expense of reduced equity exposure unless very long maturity bonds would be traded.…”

Section: Interest Rate Riskmentioning

confidence: 99%

Saving and Investing Over the Life Cycle and the Role of Collective Pension Funds

et al. 2007

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SummaryThis paper surveys the academic literature on optimal saving and investment over an individual's life cycle. We start out with a simple benchmark model with separable and smooth preferences, one aggregate risk factor and riskless wage income. Within this simple setting, optimal saving and investment behavior are explored from the perspective of individuals. Subsequently, we investigate various constraints to optimal individual decision making. We discuss how collective pension schemes may help to relieve some of the market incompleteness that arises from these constraints while at the same time introducing new types of constraints. Finally, various extensions to the benchmark setting are analyzed: a more elaborate modelling of human capital, additional risk factors, and other types of preferences.

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Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method

Reus

Prado

2021

Comput Econ

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This work presents a novel application of the Stochastic Dual Dynamic Problem (SDDP) to large-scale asset allocation. We construct a model that delivers allocation policies based on how the portfolio performs with respect to user-defined (synthetic) indexes, and implement it in a SDDP open-source package. Based on US economic cycles and ETF data, we generate Markovian regime-dependent returns to solve an instance of multiple assets and 28 time periods. Results show our solution outperforms its benchmark, in both profitability and tracking error. Objectives and ContributionsMultistage stochastic programming (MSP) is a well-known framework for modeling large-scale problems under uncertainty, and has been widely used in several fields, including asset allocation problems. 1 MSP solutions have the quality of enabling planning not only for today's positions, but also for future portfolio rebalancing, depending on the evolution of market events and prices. That advantage avoids increases in turnover (which leads to higher transaction costs) and augment the & Lorenzo Reus

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When Can Life-Cycle Investors Benefit from Time-Varying Bond Risk Premia?

Cited by 42 publications

References 48 publications

Solving Constrained Consumption–Investment Problems by Simulation of Artificial Market Strategies

Solving Constrained Consumption–Investment Problems by Simulation of Artificial Market Strategies

Saving and Investing Over the Life Cycle and the Role of Collective Pension Funds

Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method

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