Constant Relative Risk Aversion Research Articles

MULTI-ASSET WORST-CASE OPTIMAL PORTFOLIOS

We generalize the worst-case portfolio approach of Korn & Wilmott (2002) to a multi-asset setting. The nonuniqueness of indifference strategies results in a much more complicated portfolio optimization problem as in the single risky asset framework. To determine the worst-case optimal portfolio processes we develop two new approaches, a Lagrangian multiplier approach in the log-utility case and a combined constrained HJB equation and indifference strategy approach for dealing with power-utility functions. Various examples illustrate remarkable effects and differences compared to the single risky asset setting, in particular the possibility for using some stocks for crash hedging and thereby allowing stock investment possibilities that are not present in the single-stock case.

An optimal consumption and investment problem with stochastic hyperbolic discounting

In this paper, we analyze the optimal consumption and investment problem of an agent by incorporating the stochastic hyperbolic preferences with constant relative risk aversion utility. Using the dynamic programming method, we deal with the optimization problem in a continuous-time model. And we provide the closed-form solutions of the optimization problem.

A behavioral microeconomic foundation for car-following models

The objective of this paper is to develop a micro-economic modeling approach for car-following behaviors that may capture different risk-taking tendencies when dealing with different traffic conditions. The proposed framework allows for the consideration of perception subjectivity and judgement errors that may lead to unsafe acceleration driving maneuvers with the possibility of real-end collisions. The modeling approach relies on a generalized utility-based formulation with three specific types of subjective utility functions (SUFs): Prospect Utility (PT) subjective utility function, Constant Relative Risk Aversion (CRRA) subjective utility function, and an Exponential Constant Relative Risk Aversion (ECRA) subjective utility function. The formulation is assessed in terms of its homogeneous macroscopic properties (thus leading to a triangular fundamental diagram) and its non-homogeneous microscopic properties (thus leading to realistic following behavior facing different traffic scenarios).Once tested in terms of feasibility, the modeling approach is calibrated against real-life trajectory data. A Genetic Algorithm (GA) method is adopted to minimize a spacing-mixed-error term while considering inter-driving heterogeneity. The three models (i.e. PT, CRRA and ECRA) produce acceptable error values with the PT model showing the best fit, followed by the ECRA model, followed by the CRRA model. Even though the CRRA and the PT models show similar intensity in the acceleration response to the behavior of a lead vehicle with more disturbance (stochasticity/randomness) seen with the CRRA SUF, the PT and the ECRA models show more realistic wave formation despite their difference in terms of individual acceleration distribution functions. The ECRA model results in an amplified sensitivity to the behavior of the lead vehicle with the acceleration probability distribution function skewed to the left (i.e. towards decelerating rather than accelerating).The calibration exercise is followed by a simulation exercise. The three suggested models produce a homogeneous congestion phase, but a clear transient single/multiple wave formation is seen with the PT and the ECRA models. These latter models are able to reproduce all the congestion regimes observed on real-world surface transportation networks. The ECRA is characterized by a decreased capacity and increased traffic disturbances with additional shockwave formation. Finally, the different models allow the possibility of perception or judgement errors with the explicit incorporation of a collision probability and a collision weight in the suggested formulation approach.

Stocks for the log-run and constant relative risk aversion preferences

Almost Stochastic Dominance implies that stocks are preferred over bonds in the long-run by all preferences with bounded marginal utility. Unfortunately, this analysis excludes the very central Constant Relative Risk Aversion (CRRA) preferences, which have unbounded marginal utility. We derive a simple closed-form solution for the portfolio-choice problem of CRRA investors which, with the empirical parameters, implies that stocks are preferred over bonds for all CRRA investors with relative risk aversion smaller than 3.9. As a by-product, the analysis offers an alternative derivation of the continuous-time CAPM, that does not rely on dynamic programming, and allows for ambiguous investment horizons.

Optimal investment strategy for a DC pension plan with mispricing under the Heston model

In this article, we consider the optimal investment problem for a defined contribution (DC) pension plan with mispricing. We assume that the pension funds are allowed to invest in a risk-free asset, a market index, and a risky asset with mispricing, i.e. the prices are inconsistent in different financial markets. Assuming that the price process of the risky asset follows the Heston model, the manager of the pension fund aims to maximize the expected utility for the power utility function of terminal wealth. By applying stochastic control theory, we establish the corresponding Hamilton-Jacobi-Bellman (HJB) equation. And the optimal investment strategy is obtained for the power utility function explicitly. Finally, numerical examples are provided to analyze effects of parameters on the optimal strategy.

Optimal investment and consumption with stochastic factor and delay

We analyse an optimal portfolio and consumption problem with stochastic factor and delay over a finite time horizon. The financial market includes a risk-free asset, a risky asset and a stochastic factor. The price process of the risky asset is modelled as a stochastic differential delay equation whose coefficients vary according to the stochastic factor; the drift also depends on its historical performance. Employing the stochastic dynamic programming approach, we establish the associated Hamilton–Jacobi–Bellman equation. Then we solve the optimal investment and consumption strategies for the power utility function. We also consider a special case in which the price process of the stochastic factor degenerates into a Cox–Ingersoll–Ross model. Finally, the effects of the delay variable on the optimal strategies are discussed and some numerical examples are presented to illustrate the results. doi:10.1017/S1446181119000014

Modern tontine with bequest: Innovation in pooled annuity products

We introduce a new pension product that offers retirees the opportunity for a lifelong income and a bequest for their estate. Based on a tontine mechanism, the product divides pension savings between a tontine account and a bequest account. The tontine account is given up to a tontine pool upon death while the bequest account value is paid to the retiree’s estate. The values of these two accounts are continuously re-balanced to the same proportion, which is the key feature of our new product.Our main research question about the new product is what proportion of pension savings should a retiree allocate to the tontine account. Under a power utility function, we show that more risk averse retirees allocate a fairly stable proportion of their pension savings to the tontine account, regardless of the strength of their bequest motive. The proportion declines as the retiree becomes less risk averse for a while. However, for the least risk averse retirees, a high proportion of their pension savings is optimally allocated to the tontine account. This surprising result is explained by the least risk averse retirees seeking the potentially high value of the bequest account at very old ages.

Liquidity Risk and the Dynamics of Arbitrage Capital

ABSTRACTWe develop a continuous‐time model of liquidity provision in which hedgers can trade multiple risky assets with arbitrageurs. Arbitrageurs have constant relative risk‐aversion (CRRA) utility, while hedgers' asset demand is independent of wealth. An increase in hedgers' risk aversion can make arbitrageurs endogenously more risk‐averse. Because arbitrageurs generate endogenous risk, an increase in their wealth or a reduction in their CRRA coefficient can raise risk premia despite Sharpe ratios declining. Arbitrageur wealth is a priced risk factor because assets held by arbitrageurs offer high expected returns but suffer the most when wealth drops. Aggregate illiquidity, which declines in wealth, captures that factor.

Consumption, investment and healthcare with aging

This paper solves the problem of optimal dynamic consumption, investment, and healthcare spending with isoelastic utility, when natural mortality grows exponentially to reflect Gompertz' law and investment opportunities are constant. Healthcare slows the natural growth of mortality, indirectly increasing utility from consumption through longer lifetimes. Optimal consumption and healthcare imply an endogenous mortality law that is asymptotically exponential in the old-age limit, with lower growth rate than natural mortality. Healthcare spending steadily increases with age, both in absolute terms and relative to total spending. The optimal stochastic control problem reduces to a nonlinear ordinary differential equation with a unique solution, which has an explicit expression in the old-age limit. The main results are obtained through a novel version of Perron's method.

Risk Aversion Among Australian Households

This paper explores risk aversion among Australian households using panel data from the Household Income and Labour Dynamics in Australia (HILDA) survey. Using households’ share of risky assets, we test whether relative risk aversion is constant in wealth. After accounting for measurement error, we cannot reject the constant relative risk aversion (CRRA) assumption. Using an Euler equation that adjusts for measurement error in consumption data, we estimate the coefficient of relative risk aversion in the CRRA utility function. Point estimates from our preferred non-linear models suggest a moderate degree of risk aversion for the typical Australian household, with values ranging from 1.2 to 1.4. These findings can provide guidance for calibrating household preferences in macroeconomic models of the Australian economy.

Importance sampling in stochastic optimization: An application to intertemporal portfolio choice

In this paper, we propose an approach to construct an analytical approximation of the zero-variance importance sampling distribution. We show specifically how this can be designed for the classic intertemporal portfolio choice problem with proportional transaction costs and constant relative risk aversion preferences. We compare the method to standard variance reduction techniques in single-period optimization and multi-stage stochastic programming formulations of the problem. The numerical experiments show that the method produces significant improvements in solution quality. In the single-period setting, the number of scenarios can be reduced by a factor of 400 with maintained solution quality compared to the best standard method; Latin hypercube sampling. Using importance sampling in multi-stage formulations, the gaps between lower and upper bound estimates are reduced by a factor of 26-500 with maintained scenario tree size. On a higher level, we consider analytical approximations of the zero-variance importance sampling distribution to be a promising method to improve solution quality in stochastic optimization.

Optimal Control of DC Pension Plan Management under Two Incentive Schemes

Since the late 1990s, a performance fee arrangement has been approved as a managerial incentive in direction contribution (DC) pension plan management to motivate managers. However, the fact that managers may take undue risk for the larger performance fees and thus reduce members’ utility has been a subject of debate. As such, this study investigates the optimal risk-taking policies of DC pension fund managers under both the single management fee scheme and a mixed scheme with a lower management fee, as well as an additional performance fee. The analytical solutions are derived by using the duality method and concavification techniques in a singular optimization problem. The results show the complex risk-taking structures of fund managers and recognize the win-win situation of implementing performance-based incentives in DC pension plan management. Under the setting of geometric Brownian motion asset price dynamics and constant relative risk aversion utility, the optimal risk investment proportion shows a peak-valley pattern under the mixed scheme. Further, the manager gambles for gain when fund wealth is low and time to maturity is short. As opposed to the existing literature, this study found that the risk-taking policy is more conservative when fund wealth is relatively large. Furthermore, the utilities of the manager and members could both be improved by appropriately choosing the performance fee rate.

A Sensitivity Analysis of the Long-Term Expected Utility of Optimal Portfolios

This paper discusses the sensitivity of the long-term expected utility of optimal portfolios for an investor with constant relative risk aversion. Under an incomplete market given by a factor model, we consider the utility maximization problem with long-time horizon. The main purpose is to find the long-term sensitivity, that is, the extent how much the optimal expected utility is affected in the long run for small changes of the underlying factor model. The factor model induces a specific eigenpair of an operator, and this eigenpair does not only characterize the long-term behavior of the optimal expected utility but also provides an explicit representation of the expected utility on a finite time horizon. We conclude that this eigenpair therefore determines the long-term sensitivity. As examples, explicit results for several market models such as the Kim-Omberg model for stochastic excess returns and the Heston stochastic volatility model are presented.

Insider information and its relation with the arbitrage condition and the utility maximization problem.

Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of insider information. We assume that, since the initial time, the information flow is altered by adding the knowledge of an additional random variable including future information. In this context we study the utility maximization problem under the logarithmic and the Constant Relative Risk Aversion (CRRA) utilities, with and without the restriction of no temporary-bankruptcy. In particular, we show that the value of the insider information may be bounded while the arbitrage condition holds and we prove that the insider information does not always imply arbitrage for the insider by providing an explicit example.

The Value of Knowing the Market Price of Risk

This paper presents an optimal allocation problem in a financial market with one risk-free and one risky asset, when the market is driven by a stochastic market price of risk. We solve the problem in continuous time, for an investor with a Constant Relative Risk Aversion (CRRA) utility, under two scenarios: when the market price of risk is observable (the full information case), and when it is not (the partial information case). The corresponding market models are complete in the partial information case and incomplete in the other case, hence the two scenarios exhibit rather different features. We study how the access to more accurate information on the market price of risk affects the optimal strategies and we determine the maximal price that the investor would be willing to pay to get such information. In particular, we examine two cases of additional information, when an exact observation of the market price of risk is available either at time 0 only (the initial information case), or during the whole investment period (the dynamic information case).

On the Modified Optimal Investment Strategy for Annuity Contracts under the Constant Elasticity of Variance (CEV) Model

In this work, the optimal pension wealth investment strategy during the decumulation phase, in a defined contribution (DC) pension scheme is constructed. The pension plan member is allowed to invest in a risk free and a risky asset, under the constant elasticity of variance (CEV) model. The explicit solution of the constant relative risk aversion (CRRA) and constant absolute risk aversion (CARA) utility functions are obtained, using Legendre transform, dual theory, and change of variable methods. It is established herein that the elastic parameter, β, say, must not necessarily be equal to one (β ≠ 1). A theorem is constructed and proved on the wealth investment strategy. Observations and significant results are made and obtained, respectively in the comparison of our various utility functions and some previous results in literature.

Risk Aversion Among Australian Households

This paper explores risk aversion among Australian households using panel data from the Household Income and Labour Dynamics in Australia (HILDA) survey. Using households’ share of risky assets, we test whether relative risk aversion is constant in wealth. After accounting for measurement error, we cannot reject the constant relative risk aversion (CRRA) assumption. Using an Euler equation that adjusts for measurement error in consumption data, we estimate the coefficient of relative risk aversion in the CRRA utility function. Point estimates from our preferred non-linear models suggest a moderate degree of risk aversion for the typical Australian household, with values ranging from 1.2 to 1.4. These findings can provide guidance for calibrating household preferences in macroeconomic models of the Australian economy.

Optimal Portfolio Choice in a Jump-Diffusion Model with Self-Exciting

We solve the optimal portfolio choice problem for an investor who can trade a risk-free asset and a risky asset. The investor faces both Brownian and jump risks and the jump is modeled by a Hawkes process so that occurrence of a jump in the risky asset price triggers more sequent jumps. We obtain the optimal portfolio by maximizing expectation of a constant relative risk aversion (CRRA) utility function of terminal wealth. The existence and uniqueness of a classical solution to the associated partial differential equation are proved, and the corresponding verification theorem is provided as well. Based on the theoretical results, we develop a numerical monotonic iteration algorithm and present an illustrative numerical example.

Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method

In this paper, we propose a novel investment strategy for portfolio optimization problems. The proposed strategy maximizes the expected portfolio value bounded within a targeted range, composed of a conservative lower target representing a need for capital protection and a desired upper target representing an investment goal. This strategy favorably shapes the entire probability distribution of returns, as it simultaneously seeks a desired expected return, cuts off downside risk and implicitly caps volatility and higher moments. To illustrate the effectiveness of this investment strategy, we study a multiperiod portfolio optimization problem with transaction costs and develop a two-stage regression approach that improves the classical least squares Monte Carlo (LSMC) algorithm when dealing with difficult payoffs, such as highly concave, abruptly changing or discontinuous functions. Our numerical results show substantial improvements over the classical LSMC algorithm for both the constant relative risk-aversion (CRRA) utility approach and the proposed skewed target range strategy (STRS). Our numerical results illustrate the ability of the STRS to contain the portfolio value within the targeted range. When compared with the CRRA utility approach, the STRS achieves a similar mean-variance efficient frontier while delivering a better downside risk-return trade-off.

Uncovering the Distribution of Option Implied Risk Aversion

This paper explores the dynamics of risk aversion of a representative agent with an iso-elastic utility function. In contrast to most of the existing literature, we estimate the coefficient of relative risk aversion from option prices. To do this, we transform the risk-neutral density function obtained from a cross-section of option prices to an objective distribution function that reflects individuals’ risk aversion through a CRRA utility function. The dynamics of the relative risk-aversion coefficient are obtained by repeating the same estimation procedure over rolling windows. This procedure uncovers strong variation in risk aversion over time. We also propose a simulation procedure to construct confidence intervals for the risk-aversion coefficient in each period. We assess the robustness of these confidence intervals under different assumptions on the data generating process of stock prices. The results imply a strong influence of volatility on the variation of risk aversion. In an empirical application, we compare the forecasting performance of our approach based on our risk-aversion estimates against the method proposed in [1]. Overall, we find that our simulation based approach obtains better forecasting results than bootstrap methods.