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Multivariate Density Modeling for Retirement Finance

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  • Christopher J. Rook

Abstract

Prior to the financial crisis mortgage securitization models increased in sophistication as did products built to insure against losses. Layers of complexity formed upon a foundation that could not support it and as the foundation crumbled the housing market followed. That foundation was the Gaussian copula which failed to correctly model failure-time correlations of derivative securities in duress. In retirement, surveys suggest the greatest fear is running out of money and as retirement decumulation models become increasingly sophisticated, large financial firms and robo-advisors may guarantee their success. Similar to an investment bank failure the event of retirement ruin is driven by outliers and correlations in times of stress. It would be desirable to have a foundation able to support the increased complexity before it forms however the industry currently relies upon similar Gaussian (or lognormal) dependence structures. We propose a multivariate density model having fixed marginals that is tractable and fits data which are skewed, heavy-tailed, multimodal, i.e., of arbitrary complexity allowing for a rich correlation structure. It is also ideal for stress-testing a retirement plan by fitting historical data seeded with black swan events. A preliminary section reviews all concepts before they are used and fully documented C/C++ source code is attached making the research self-contained. Lastly, we take the opportunity to challenge existing retirement finance dogma and also review some recent criticisms of retirement ruin probabilities and their suggested replacement metrics.

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  • Christopher J. Rook, 2017. "Multivariate Density Modeling for Retirement Finance," Papers 1709.04070, arXiv.org.
    • Handle: RePEc:arx:papers:1709.04070
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    References listed on IDEAS

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    1. Christopher J. Rook, 2015. "Optimal Equity Glidepaths in Retirement," Papers 1506.08400, arXiv.org.
    2. Jan Fidrmuc & Peter Huber, 2007. "Introduction," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 34(4), pages 281-286, September.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    4. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
    5. Christopher J. Rook & Mitchell Kerman, 2015. "Approximating the Sum of Correlated Lognormals: An Implementation," Papers 1508.07582, arXiv.org.
    6. Moshe A. Milevsky, 2016. "It’s Time to Retire Ruin (Probabilities)," Financial Analysts Journal, Taylor & Francis Journals, vol. 72(2), pages 8-12, March.
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