Modelling optimal decisions for financial planning in retirement using stochastic control theory

Andréasson, Johan Gustav

Modelling optimal decisions for financial planning in retirement using stochastic control theory

Andréasson, Johan Gustav

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Publication Type:: Thesis
Issue Date:: 2017

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Field	Value	Language
dc.contributor.author	Andréasson, Johan Gustav
dc.date.accessioned	2018-04-06T02:20:33Z
dc.date.available	2018-04-06T02:20:33Z
dc.date.issued	2017
dc.identifier.uri	http://hdl.handle.net/10453/123238
dc.description	University of Technology Sydney. Faculty of Science.	en_AU
dc.description.abstract	In this thesis, we develop an expected utility model for retirement behaviour in the decumulation phase of Australian retirees with sequential family status subject to consumption, housing, investment, bequest, and a government-provided means-tested Age Pension. We account for mortality risk and risky investment assets, and we introduce a “health” proxy to capture the decreasing level of consumption for older retirees. The model is calibrated using the maximum likelihood method with empirical data on consumption and housing from the Australian Bureau of Statistic’s 2009-2010 ‘Household Expenditure Survey’ and ‘Survey of Income and Housing’. The calibrated model fits the characteristics of the data well to explain the behaviour of Australian retirees, and is then used to examine the optimal decisions given recent Age Pension policies and different family settings. Specifically, we examine optimal decisions for housing at retirement, and the optimal consumption and risky asset allocation depending on age and wealth for the Age Pension policies 2015-2017. As the piecewise linearity in the Age Pension function requires the stochastic control problem to be solved numerically, we utilise the Least Squares Monte Carlo method to extend the problem with additional dimensions and control variables. This method is difficult to use with utility functions, as it can lead to a bad fit or bias from transforming variables. We suggest methods to account for this bias, and show that the Least Squares Monte Carlo is then accurate when applied to expected utility stochastic control problems. We then extend the optimal decisions to include annuitisation, as well as the option to scale housing in retirement or to access home equity through a reverse mortgage, and examine optimal decisions with respect to the Age Pension in retirement.	en_AU
dc.format	Thesis (PhD)
dc.language.iso	en_AU	en_AU
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/123238/7/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Modelling optimal decisions for financial planning in retirement using stochastic control theory	en_AU
dc.type	Thesis	en_AU
utslib.copyright.status	open_access

Abstract:

In this thesis, we develop an expected utility model for retirement behaviour in the decumulation phase of Australian retirees with sequential family status subject to consumption, housing, investment, bequest, and a government-provided means-tested Age Pension. We account for mortality risk and risky investment assets, and we introduce a “health” proxy to capture the decreasing level of consumption for older retirees. The model is calibrated using the maximum likelihood method with empirical data on consumption and housing from the Australian Bureau of Statistic’s 2009-2010 ‘Household Expenditure Survey’ and ‘Survey of Income and Housing’. The calibrated model fits the characteristics of the data well to explain the behaviour of Australian retirees, and is then used to examine the optimal decisions given recent Age Pension policies and different family settings. Specifically, we examine optimal decisions for housing at retirement, and the optimal consumption and risky asset allocation depending on age and wealth for the Age Pension policies 2015-2017. As the piecewise linearity in the Age Pension function requires the stochastic control problem to be solved numerically, we utilise the Least Squares Monte Carlo method to extend the problem with additional dimensions and control variables. This method is difficult to use with utility functions, as it can lead to a bad fit or bias from transforming variables. We suggest methods to account for this bias, and show that the Least Squares Monte Carlo is then accurate when applied to expected utility stochastic control problems. We then extend the optimal decisions to include annuitisation, as well as the option to scale housing in retirement or to access home equity through a reverse mortgage, and examine optimal decisions with respect to the Age Pension in retirement.

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http://hdl.handle.net/10453/123238