SSRN Author: Peter ForsythPeter Forsyth SSRN Content
https://privwww.ssrn.com/author=2719316
https://privwww.ssrn.com/rss/en-usTue, 03 Nov 2020 01:20:26 GMTeditor@ssrn.com (Editor)Tue, 03 Nov 2020 01:20:26 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Two Stage Decumulation Strategies for DC Plan Investors: 'A Goal Properly Set Is Halfway Reached'Optimal stochastic control methods are used to examine decumulation strategies for a defined contribution (DC) plan retiree. An initial investment horizon of fifteen years is considered, since the retiree will attain this age with high probability. The objective function reward measure is the expected sum of the withdrawals. The objective function tail risk measure is the expected linear shortfall with respect to a desired lower bound for wealth at fifteen years. The lower bound wealth level is the amount which is required to fund a lifelong annuity fifteen years after retirement, which generates the required minimum cash flows. This ameliorates longevity risk. The controls are the withdrawal amount each year, and the asset allocation strategy. Maximum and minimum withdrawal amounts are specified. Specifying a short initial decumulation horizon, results in the optimal strategy achieving: (i) median withdrawals at the maximum rate within 2-3 years of retirement (ii) terminal wealth ...
https://privwww.ssrn.com/abstract=3719597
https://privwww.ssrn.com/1957697.htmlMon, 02 Nov 2020 15:02:15 GMTNew: A Stochastic Control Approach to Defined Contribution Plan Decumulation: 'The Nastiest, Hardest Problem in Finance'We pose the decumulation strategy for a Defined Contribution (DC) pension plan as a problem in optimal stochastic control. The controls are the withdrawal amounts and the asset allocation strategy. We impose maximum and minimum constraints on the withdrawal amounts, and impose no-shorting no-leverage constraints on the asset allocation strategy. Our objective function measures reward as the expected total withdrawals over the decumulation horizon, and risk is measured by Expected Shortfall (ES) at the end of the decumulation period. We solve the stochastic control problem numerically, based on a parametric model of market stochastic processes. We find that, compared to a fixed constant withdrawal strategy, with minimum withdrawal set to the constant withdrawal amount, the optimal strategy has a significantly higher expected average withdrawal, at the cost of a very small increase in ES risk. Tests on bootstrapped resampled historical market data indicate that this strategy is ...
https://privwww.ssrn.com/abstract=3674232
https://privwww.ssrn.com/1937552.htmlWed, 02 Sep 2020 09:53:45 GMTNew: Optimal Asset Allocation for Outperforming a Stochastic Benchmark TargetWe propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. Following the optimal adaptive strategy, investors can make allocation decisions simply depending on the current state of the portfolio. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal ...
https://privwww.ssrn.com/abstract=3619332
https://privwww.ssrn.com/1914823.htmlMon, 29 Jun 2020 16:22:32 GMTNew: Optimal Dynamic Asset Allocation for DC Plan Accumulation/Decumulation: Ambition-CVARWe consider the late accumulation stage, followed by the full decumulation stage, of an investor in a defined contribution (DC) pension plan. The investor's portfolio consists of a stock index and a bond index. As a measure of risk, we use conditional value at risk (CVAR) at the end of the decumulation stage. This is a measure of the risk of depleting the DC plan, which is primarily driven by sequence of return risk and asset allocation during the decumulation stage. As a measure of reward, we use Ambition, which we define to be the probability that the terminal wealth exceeds a specified level. We develop a method for computing the optimal dynamic asset allocation strategy which generates points on the efficient Ambition-CVAR frontier. By examining the Ambition-CVAR efficient frontier, we can determine points that are Median-CVAR optimal. We carry out numerical tests comparing the Median-CVAR optimal strategy to a benchmark constant proportion strategy. For a fixed median ...
https://privwww.ssrn.com/abstract=3495182
https://privwww.ssrn.com/1850398.htmlMon, 16 Dec 2019 12:53:14 GMTNew: Optimal Asset Allocation for DC Pension Decumulation with a Variable Spending RuleWe determine the optimal asset allocation to bonds and stocks using an Annually Recalculated Virtual Annuity (ARVA) spending rule for DC pension plan decumulation. Our objective function minimizes downside withdrawal variability for a given fixed value of total expected withdrawals. The optimal asset allocation is found using optimal stochastic control methods. We formulate the strategy as a solution to a Hamilton Jacobi Bellman (HJB) Partial Integro Differential Equation (PIDE). We impose realistic constraints on the controls (no shorting, no leverage, discrete rebalancing), and solve the HJB PIDEs numerically. Compared to a fixed weight strategy which has the same expected total withdrawals, the optimal strategy has a much smaller average allocation to stocks, and tends to de-risk rapidly over time. This conclusion holds in the case of a parametric model based on historical data, and also in a bootstrapped market based on the historical data.
https://privwww.ssrn.com/abstract=3494501
https://privwww.ssrn.com/1849885.htmlFri, 13 Dec 2019 16:18:47 GMT