Combined Accumulation- and Decumulation-Plans with Risk-Controlled Capital Protection

Similar documents
THE OPTIMAL ASSET ALLOCATION PROBLEMFOR AN INVESTOR THROUGH UTILITY MAXIMIZATION

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

OPTIMAL PORTFOLIO CONTROL WITH TRADING STRATEGIES OF FINITE

MODELLING OPTIMAL HEDGE RATIO IN THE PRESENCE OF FUNDING RISK

Determining a Realistic Withdrawal Amount and Asset Allocation in Retirement

Dynamic Portfolio Choice II

Optimal Withdrawal Strategy for Retirement Income Portfolios

Retirement. Optimal Asset Allocation in Retirement: A Downside Risk Perspective. JUne W. Van Harlow, Ph.D., CFA Director of Research ABSTRACT

Are Managed-Payout Funds Better than Annuities?

Optimal Search for Parameters in Monte Carlo Simulation for Derivative Pricing

LIFE ANNUITY INSURANCE VERSUS SELF-ANNUITIZATION: AN ANALYSIS FROM THE PERSPECTIVE OF THE FAMILY

MEASURING PORTFOLIO RISKS USING CONDITIONAL COPULA-AR-GARCH MODEL

Value at Risk, Expected Shortfall, and Marginal Risk Contribution, in: Szego, G. (ed.): Risk Measures for the 21st Century, p , Wiley 2004.

Financial Giffen Goods: Examples and Counterexamples

COMBINING FAIR PRICING AND CAPITAL REQUIREMENTS

Hedging with Life and General Insurance Products

Calibrating to Market Data Getting the Model into Shape

ASC Topic 718 Accounting Valuation Report. Company ABC, Inc.

Analysis of truncated data with application to the operational risk estimation

Mathematics of Finance Final Preparation December 19. To be thoroughly prepared for the final exam, you should

Overnight Index Rate: Model, calibration and simulation

Portfolio Optimization using Conditional Sharpe Ratio

Valuation of Asian Option. Qi An Jingjing Guo

Market Risk Analysis Volume I

Annuity Decisions with Systematic Longevity Risk. Ralph Stevens

Optimal decumulation into annuity after retirement: a stochastic control approach

Optimal portfolio choice with health-contingent income products: The value of life care annuities

Calculating VaR. There are several approaches for calculating the Value at Risk figure. The most popular are the

Beyond Modern Portfolio Theory to Modern Investment Technology. Contingent Claims Analysis and Life-Cycle Finance. December 27, 2007.

On the Environmental Kuznets Curve: A Real Options Approach

Alpha, Beta, and Now Gamma

Statistical Methods in Financial Risk Management

Optimizing Modular Expansions in an Industrial Setting Using Real Options

Market Risk: FROM VALUE AT RISK TO STRESS TESTING. Agenda. Agenda (Cont.) Traditional Measures of Market Risk

Multiple Objective Asset Allocation for Retirees Using Simulation

Analytical approximations for a general pension problem

Lifetime Portfolio Selection: A Simple Derivation

EE266 Homework 5 Solutions

Asset Pricing Models with Underlying Time-varying Lévy Processes

risk minimization April 30, 2007

The Capital Asset Pricing Model as a corollary of the Black Scholes model

Nearly optimal asset allocations in retirement

LIFECYCLE INVESTING : DOES IT MAKE SENSE

Coping with Sequence Risk: How Variable Withdrawal and Annuitization Improve Retirement Outcomes

Stochastic Programming in Gas Storage and Gas Portfolio Management. ÖGOR-Workshop, September 23rd, 2010 Dr. Georg Ostermaier

induced by the Solvency II project

2. AREA: BANKING, FINANCE & INSURANCE. 2.1 Chair of Business Administration, Risk Theory, Portfolio Management and Insurance

EURASIAN JOURNAL OF BUSINESS AND MANAGEMENT

GOAL PROGRAMMING TECHNIQUES FOR BANK ASSET LIABILITY MANAGEMENT

Interest Rate Curves Calibration with Monte-Carlo Simulatio

Comparison of Estimation For Conditional Value at Risk

Collective Defined Contribution Plan Contest Model Overview

INTERTEMPORAL ASSET ALLOCATION: THEORY

Energy Systems under Uncertainty: Modeling and Computations

Inferences on Correlation Coefficients of Bivariate Log-normal Distributions

Sharpe Ratio over investment Horizon

Optimized Least-squares Monte Carlo (OLSM) for Measuring Counterparty Credit Exposure of American-style Options

Article from. ARCH Proceedings

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2011, Mr. Ruey S. Tsay. Solutions to Final Exam.

A Study on Optimal Limit Order Strategy using Multi-Period Stochastic Programming considering Nonexecution Risk

Risk analysis of annuity conversion options in a stochastic mortality environment

DAKOTA FURNITURE COMPANY

A STOCHASTIC APPROACH TO RISK MODELING FOR SOLVENCY II

Ordinary Mixed Life Insurance and Mortality-Linked Insurance Contracts

Growth-indexed bonds and Debt distribution: Theoretical benefits and Practical limits

The Impact of Stochastic Volatility and Policyholder Behaviour on Guaranteed Lifetime Withdrawal Benefits

Valuing Early Stage Investments with Market Related Timing Risk

UPDATED IAA EDUCATION SYLLABUS

Monte Carlo Simulations

arxiv: v1 [math.oc] 28 Jan 2019

Payout-Phase of Mandatory Pension Accounts

1 Consumption and saving under uncertainty

Valuation of performance-dependent options in a Black- Scholes framework

Retirement Lockboxes. William F. Sharpe Stanford University. CFA Society of San Francisco January 31, 2008

A DYNAMIC CONTROL STRATEGY FOR PENSION PLANS IN A STOCHASTIC FRAMEWORK

Monte-Carlo Estimations of the Downside Risk of Derivative Portfolios

Equity correlations implied by index options: estimation and model uncertainty analysis

Proxy Function Fitting: Some Implementation Topics

Yale ICF Working Paper No First Draft: February 21, 1992 This Draft: June 29, Safety First Portfolio Insurance

CSCI 1951-G Optimization Methods in Finance Part 00: Course Logistics Introduction to Finance Optimization Problems

ROBUST OPTIMIZATION OF MULTI-PERIOD PRODUCTION PLANNING UNDER DEMAND UNCERTAINTY. A. Ben-Tal, B. Golany and M. Rozenblit

Advanced Financial Economics Homework 2 Due on April 14th before class

Portfolio optimization problem with default risk

Balancing Income and Bequest Goals in a DB/DC Hybrid Pension Plan

MAKING YOUR NEST EGG LAST A LIFETIME

INTRODUCTION TO THE ECONOMICS AND MATHEMATICS OF FINANCIAL MARKETS. Jakša Cvitanić and Fernando Zapatero

Solving real-life portfolio problem using stochastic programming and Monte-Carlo techniques

Optimal support for renewable deployment: A case study in German photovoltaic

2.1 Mathematical Basis: Risk-Neutral Pricing

Financial Mathematics III Theory summary

ScienceDirect. Some Applications in Economy for Utility Functions Involving Risk Theory

Results for option pricing

Optimal Dam Management

Math 416/516: Stochastic Simulation

Pension Funds Performance Evaluation: a Utility Based Approach

Sustainable Spending for Retirement

Pricing Exotic Options Under a Higher-order Hidden Markov Model

Credit Risk and Underlying Asset Risk *

Brownian Motion and the Black-Scholes Option Pricing Formula

Deterministic Income under a Stochastic Interest Rate

Transcription:

Combined Accumulation- and Decumulation-Plans with Risk-Controlled Capital Protection Peter Albrecht and Carsten Weber University of Mannheim, Chair for Risk Theory, Portfolio Management and Insurance D - 68131 Mannheim (Schloss), Germany Telephone: 49 621 181 1680 Facsimile: 49 621 181 1681 E-mail: risk@bwl.uni-mannheim.de Abstract We base our analysis on an investor, usually a retiree, endowed with a certain amount of wealth W, who considers both his own consumption needs (fixed periodic withdrawals) and the requirement of his heirs (defined bequest). For this purpose he pursues the following investment strategy. The part F is invested in a set of investment funds with the target to achieve an accumulated wealth at the end of a certain time horizon of at least the original amount of wealth W (or the fraction ( 1 h)w ), measured in real terms. As certain investment risks are implied, we allow for the probability of falling short of the target and implement it into our model as a risk control parameter. The remaining part MM of the original wealth is invested in money market funds in order to avoid additional investment risks and deliver fixed periodic withdrawals until the end of the respective time horizon. The optimal investment strategy is the investment fund allocation that satisfies the probability of shortfall and minimizes F, while maximizing the fixed periodic withdrawals. We outline this investment problem in a mathematical model and illustrate the solution for a reasonable choice of empirical parameters. Keywords: Financial Engineering, Value-at-Risk, Capital Protected Accumulation Plans

2 1. Investment problem An investor of a certain age, usually a retiree of about 60 years, possesses a certain amount of wealth W, e.g. 100.000, which he invests according to the following requirements: A minimal part F is invested in a set of investment funds, or asset categories, with the target to achieve an accumulated wealth at the end of a certain time horizon, e.g. 20 years of at least the original amount of wealth W or the fraction ( 1 h)w bequest)., measured in real terms (capital protection in real terms for a defined The remaining part MM of the original wealth is invested in money market funds, out of which an annual annuity due is withdrawn until the end of the respective time horizon (annuization for individual consumption needs). ity due. In order to maximise the annuity due, the investor has to choose an investment stratoriginal amount of wealth W part of wealth F part of wealth MM fund investment money market investment capital protection in real terms annual annuity due in real terms Figure 1: Illustration of the investment problem It is evident, that the amount of F determines the amount of MM and therefore also the annu-

3 egy, minimizing the amount of F, while meeting the above- mentioned investment requirements. In the following, we develop a general solution for this respective investment problem. For clarity reasons, we present our model in detail in the appendix against a theoretical background. 2. Methodology 2.1 Condition of risk-controlled capital protection in real terms In case of fund investment under risk, the reach of the respective investment target is not only determined by the average investment return, but also by the volatility of the fund. Therefore, it is necessary to specify a condition, that incorporates the capital protection for a defined bequest under risk. Capital protection for fund investment under risk can not be guaranteed with full certainty, but only to a distinct degree of certainty, being represented by a probability. Thus, we propose the following criterion of risk control based on the shortfall probability. This condition of risk-controlled capital protection in real terms is orally defined as: At the end of a previously fixed time horizon, the desired fraction of the original amount of wealth ( 1 h)w may fall short merely in a maximum of out of 100 investment outcomes. The parameter is a confidence coefficient, that has to be individually defined by the investor, e.g. = 1%,5%,10%. This means, that the desired fraction of wealth is failed in no more than 1%, 5% or 10% of all possible investment scenarios. In this way, the shortfall probability of the desired fraction of capital protection can be controlled. The determination of the Valueat-Risk of the distribution of wealth at the end of the time horizon constitutes the focus of our methodology. For a mathematical formalization, the reader is referred to the appendix.

4 Table 2 summarizes the general procedure of our formalization in order to generate the minimal amount of F and the corresponding investment fund allocation as well as fundamental factors that influence the investment problem. confidence coefficient time horizon condition of riskcontrolled capital protection risk-controlled fund investment risk control calculation of the Value-at-Risk selection stochastic process of fund investment average investment return, volatility and correlation of funds investment fund allocation optimization optimal riskcontrolled fund investment F Figure 2: Formalization of the procedure For further concretion of the general procedure, we limit our analysis to the case of three different investment funds or asset categories. 2.2 Case of three investment funds For the simultaneous development of three investment funds, e.g. a representative stock, bond

5 or property fund, we assume a trivariate geometric Brownian motion. Since the distribution of F at the end of the time horizon can not be determined in an analytical way, the Value-at-Risk is not analytically definable either and therefore has to be generated in a Monte Carlo- Simulation. In consequence, the determination of the minimal amount of F and the corresponding optimal investment fund allocation can not be achieved analytically either. Like in Albrecht/Maurer (2002), we use the standard approach of restraining the possible investment fund allocations to a representative number and vary the investment weights of each fund by steps of 5%, which results in 231 investment fund allocations. stock funds 0% 0% 0% 5% 5% 10% 10% 95% 95% 100% bond funds 0% 5% 100% 0% 95% 0% 90% 0% 5% 0% property funds 100% 95% 0% 95% 0% 90% 0% 5% 0% 0% Table 1: Representative investment fund allocations 3. Results The following results refer to the case of three investment funds and are based on the parameters for continuous investment returns in real terms. average investment returns of stock fund 8%, 5% resp. average investment return of bond fund 4% average investment return of property fund 3.3% volatility of stock fund 25% volatility of bond fund 6% volatility of property fund 2% correlation between stock and bond funds 0.2 correlation between stock and funds -0.1

6 correlation between bond and property funds 0.6 sales charge of stock fund 5% sales charge of bond fund 3% sales charge of property fund 5% Table 2: Specification of parameters We use the empirical results for the German market of Maurer/Schlag (2002) and Sebastian (2003) for our simulations, however, we projected the average returns to be slightly lower and the volatilities to be slightly higher in our prospective model calculations. With regard to the stock fund we take two alternative scenarios into account. On the one hand, we consider an average investment return of 8% in real terms, which often represents the standard estimate of average stock returns for very long time horizons as used in Pye (2000). On the other hand, the conservative projection of 5% in real terms serves to obtain information on the sensitivity of the results. Tables 3 and 4 contain the minimum amounts of F, the corresponding optimal investment fund allocations and the annual annuity dues based on a continuous real money market return of 1.5% according to the respective time horizons as well as the confidence coefficients. From now on we deal with the transformation of the confidence coefficients into degrees of certainty ( 1 ). The results refer to average real stock returns of 8% and 5% and assume full capital protection of ( 1 h) = 1. As outlined in the appendix, the desired fraction of capital protection does not affect the determination of the optimal investment fund allocation, but merely the amount of F and the annual annuity due.

7 time horizon in years 5 10 15 20 25 ( 5% 0% 95% ) ( 5% 0% 95% ) ( 10% 5% 85% ) ( 10% 5% 85% ) ( 15% 15% 70% ) degree of certainty 95% 94.851,07 81.533,17 69.232,59 58.189,23 48.499,59 1.060,91 1.973,80 2.273,47 2.401,72 2.451,92 ( 5% 0% 95% ) ( 10% 5% 85% ) ( 15% 20% 65% ) ( 20% 30% 50% ) ( 25% 40% 35% ) 90% 93.189,78 79.201,37 66.248,61 54.455,79 43.912,82 1.403,21 2.223,04 2.493,96 2.616,18 2.670,29 Table 3: Results for an average real stock return of 8% time horizon in years 5 10 15 20 25 ( 5% 0% 95% ) ( 5% 0% 95% ) ( 5% 0% 95% ) ( 5% 5% 90% ) ( 5% 5% 90% ) degree of certainty 95% 95.552,87 82.774,21 71.127,18 60.889,03 51.978,41 916,31 1.841,16 2.133,47 2.246,63 2.286,29 ( 5% 0% 95% ) ( 5% 5% 90% ) ( 5% 5% 90% ) ( 5% 10% 85% ) ( 10% 20% 70% ) 90% 93.888,81 80.701,59 68.909,11 58.607,26 49.446,32 1.259,18 2.062,69 2.297,37 2.377,70 2.406,84 Table 4: results for an average real stock return of 5% All in all, the following plausible dependencies can be observed: The longer is the time horizon, the larger is the share invested in stock and bond funds. The longer is the time horizon, the smaller is the amount of F, that has to be invested in the risky investment funds and the larger is the amount of MM disposable for the annuity due.

8 The higher is the degree of certainty, the lower is the share of stock and bond funds and the larger is the share of property funds as the least risky type of investment fund. Using lower average real stock returns leads to consistently larger amounts of F to be invested in risky investment funds and in general to a lower share of stock funds for the optimal investment fund allocation. References ALBRECHT, P. and MAURER, R. (2002), Self-Annuitization, Consumption Shortfall in Retirement and Asset Allocation: The Annuity Benchmark. In Journal of Pension Economics and Finance 1, pp.269 288. MAURER, R. and SCHLAG, C. (2001), Investmentfonds-Ansparpläne: Erwartetes Versorgungsniveau und Shortfallrisiken. In Der Langfristige Kredit 12/2001, pp.440 445. PYE, G.B. (2000), Sustainable Investment Withdrawals. In Journal of Portfolio Management, Summer 2000, pp.73 83. SEBASTIAN, S. (2003), Inflationsrisiken von Aktien-, Renten- und Immobilieninvestments, Bad Soden/Taunus.

9 Appendix: Fundamental Methodology In the general case of N investment funds or asset categories, the development of the value of each fund during n years is determined by n 1+ Vk ( n) : = exp U k ( t), k = 1,..., N. (1) t= 1 Assuming a multivariate geometric Brownian motion, the vectors of continuous real returns ( U ( t),, U ( t),, U ( t) ) L with t = 1, L, n are i.i.d. as 1 k L N ( U,, U, L, U ) ~ N( m, ) L. (2) 1 k N The vector of real log-returns has a multivariate normal distribution. Given x ( x 1, L,, L, ) = x k x N with 0 x k 1 and x k =1 represents the vector of shares invested in each of the N investment funds or asset categories and 100a k % with k = 1, L, n the respective sales charges, we obtain the following wealth in real terms per invested unit after a time horizon of n years N 1+ Vk ( n) 1+ Vn ( x ) = xk. (3) k= 1 1+ a k The condition of risk-controlled capital protection in real terms is defined as ( F[ 1+ V ( )] ( 1 h ) W) = P n x (4) with 0 < ( 1 h ) 1 being the desired degree of capital protection. (For example, ( 1 h) = 0. 9 demonstrates a capital protection in real terms of 90%.) Given Q ( x ) represents the -quantile of the random number ( x ) 1+, we obtain (1 h)w F = F( x ) =. (5) Q ( x ) Then it is necessary to determine the investment fund allocation x*, that yields V n F( x ) min!.

10 Since equation (5) applies, F( x ) is at its minimum, when Q ( x ) is at its maximum. Thus, regardless of the desired degree of capital protection in real terms, from a formal perspective, we merely have to find the investment fund allocation x*, that yields Q( x ) max!. (6) According to equation (5), Q ( x ) determines the minimum amount of F for a given confidence coefficient. Capital protection is only feasible, if the amount of F can be financed by the original wealth W, implying F W. Because of equation (5), the necessary condition to be fulfilled, is ( 1 h) Q( x *). (7) Therefore, our analysis can be conducted independent from the amount of original wealth W and based on one unit of wealth instead. For this purpose, it is sufficient to analyze the quantile Q ( x ) and fix the desired degree of capital protection in real terms. The average development of the value of the optimal fund investment F( x* ) ultimately is while ( ) m k m =, = ( ) N x k * n(m + 1 2 k vk ) [ ] 2 E F( x *) = F e, (8) 1+ a 2 Σ v kj and k v kk k= 1 v =. k Finally, we describe the amount of MM in real terms invested in money market funds, that yields the annual annuity due after a time horizon of n years (1 h) W [Q (1 h)] W F = W = W. (9) Q Q Given i represents the deterministic annual continuous real money market rate, the annual real annuity due is determined by Q R = (1 h) W q Q n 1 q 1 q n 1 (10)

11 with i q = e :. Evidently, the annual real annuity due is positive, if Q > ( 1 h).