Research Article  Open Access
Time Consistent Strategies for MeanVariance AssetLiability Management Problems
Abstract
This paper studies the optimal time consistent investment strategies in multiperiod assetliability management problems under meanvariance criterion. By applying time consistent model of Chen et al. (2013) and employing dynamic programming technique, we derive twotime consistent policies for assetliability management problems in a market with and without a riskless asset, respectively. We show that the presence of liability does affect the optimal strategy. More specifically, liability leads a parallel shift of optimal timeconsistent investment policy. Moreover, for an arbitrarily risk averse investor (under the variance criterion) with liability, the timediversification effects could be ignored in a market with a riskless asset; however, it should be considered in a market without any riskless asset.
1. Introduction
By using variance as a risk measure, Markowitz [1] proposed the classic meanvariance portfolio selection model, which has become the theoretical foundation of modern finance theory and has been extended in several directions. One of the main extensions for portfolio selection is to study the optimal policy in a multiperiod setting. For example, Li and Ng [2] and Zhou and Li [3] employed an embedding technique to derive analytical solutions to multiperiod and continuoustime meanvariance models, respectively. In most of the studies in the multiperiod environment, there has been a common assumption that an investor has no longterm liability. However, in reality, many investment institutions (e.g., pension funds, insurance company, and banks) have paid great attention to their portfolios while taking into account their liabilities. Further, it has been shown by Kell and Muller [4] and Sharpe and Tint [5] that liability does affect the optimal policy. More specifically, in a singleperiod setting, liability leads a parallel shift of meanvariance optimal investment policy and affects the meanvariance efficient frontier. Due to both theoretical interest and practical importance of assetliability management, the research on meanvariance assetliability management has attracted recent attentions. For example, among others, Leippold et al. [6] considered the multiperiod assetliability management problem where the liability is exogenous and fixed and derived an analytical optimal policy and an efficient frontier. Further, they extended their research to the case where the liability is endogenous and controllable in [7]. Chiu and Li [8] and Xie et al. [9] studied continuoustime meanvariance assetliability management problems, respectively. Furthermore, Xie [10] studied meanvariance model with stochastic liability in a Markovian regime switching financial market, and Zeng and Li [11] investigated assetliability management problem in a jump diffusion market.
In most of the literature, the popular approaches of dealing with dynamic meanvariance assetliability management problem are embedding technique which was developed by Li and Ng [2] and dual method. However, since the iteratedexpectation property does not hold for the variance operator, the optimal assetliability management policy (called precommitment strategy) derived by both the approaches does not satisfy Bellman's optimality principle and is time inconsistent. The main reason is that, the precommitment strategy, for time interval , computed at time will not necessarily coincide with the strategy which computed at time . As a result, at time , the strategy computed at time will not be implemented by the investor, unless there exists some commitment mechanism. Strotz [12] first formalized time inconsistency and pointed out that the conflict could be solved by a timeconsistent strategy. Very recently, much more scholars have paid their attentions on constructing a timeconsistent meanvariance portfolio choice. Among others, Basak and Chabakauri [13] provided a fully analytical characterization of the optimal timeconsistent meanvariance portfolio within a general incomplete market economy. Wang and Forsyth [14] developed a numerical scheme for determining the optimal asset allocation strategy for timeconsistent, continuous time, meanvariance optimization. By allowing the tradeoff between the mean and the variance of the terminal wealth varying over time, Cui et al. [15] proposed a weak time consistency to compare with Bellman's optimality principle and derived an optimal meanvariance portfolio strategy. In all the literature mentioned above, the studies mainly referred to Bellman's optimality principle. However, the requirement (for short, REQ) that local optimum is also globally optimum is not necessarily satisfied, which is an essential requirement in solving the relevant optimal portfolio problem by the dynamic programming technique. In order to make up this shortfall, Chen et al. [16], by using a time consistent dynamic risk measure, proposed a separable dynamic meanvariance model and showed that the relevant optimal investment policy satisfies not only the Bellman's optimality principle but also the REQ.
Although assetliability management is an important issue in modern finance theory, the timeconsistent assetliability management problem has not attracted enough attention. Recently, Li et al. [17] reported the timeconsistent assetliability management problem in the continuoustime setting. They employed Basak and Chabakauri's [13] model to study the continuoustime assetliability management problem. They derived the timeconsistent optimal strategy and showed that the timeconsistent efficient frontier with liability is below that without liability. However, the derived timeconsistent policy does not satisfy the REQ. As the aforementioned importance of the REQ, in this paper, we employ the model of Chen et al. [16] to analysis the multiperiod assetliability management problem. We derive timeconsistent optimal investment policies in a market with and without a riskless asset, respectively. After comparing the optimal timeconsistent policies with myopic strategies, we show that, for an arbitrarily risk averse investor, if there is a riskless asset in the market, the timediversification effects arising from multiperiod optimization can be ignored, otherwise, the effects should be considered.
This paper proceeds as follows. In the next section, we formulate a timeconsistent assetliability management model. In Section 3, we derive the timeconsistent optimal policy for a market without riskless asset. Section 4 derives the timeconsistent optimal policy for a market with both riskless and risky assets. Section 5 performs numerical examples to illustrate our results. The paper is concluded in Section 6.
2. Model Formulation
Throughout this paper, we assume that is a fixed and finite time horizon and trading only takes place at time . Let be a probability space, and let a field be the available information at time .
Consider a security market consisting of one riskless asset and risky assets. The return of the riskless asset at the th investment period is assumed to be . The return of the th risky asset at the th investment period is denoted by , and the relevant random return vector is denoted by which is measurable.
Consider an investor with an initial endowment and a liability . We assume that the liability cannot be controlled and denote by the accumulative liability at time . Let be the return of the liability at the th investment period which is measurable. It is clear that . Assume that , , are statistically independent. Denote the expected return vector by and the variancecovariance matrix by which is assumed to be positive definite throughout this paper. It is clear that is also positive definite. Denote by the covariance vector. The investor begins his/her investment at time and invests the cash amount in the th risky asset at the beginning of the th investment period, where . is called an investment decision at the th investment period and is an investment policy during the entire investment horizon. We denote the wealth and the surplus of the investor at time by and , respectively.
A singleperiod conditional risk mapping is defined as where and are the singleperiod conditional expectation of and its conditional variance, respectively. Tradeoff parameter as a constant, which is defined on , is a weight which presents the relative importance of expected profit compared to the risk. Note that, if , then the investor is arbitrary risk averse who only focus on the risk; if , then the investor is risk neutral who only concerns maximizing their expected profit; if , the investor is risk averse who considers both expected profit and the risk in his decision.
In multiperiod portfolio selection, two main optimal investment policies are the myopic and timeconsistent strategies. Myopic Strategy is a strategy whereby at each time the investor determines their optimal investment decision assuming the instantaneous moments of assets returns will remain fixed at their current values for the remainder of the investment horizon. More specifically, for any , the myopic strategy is a solution to the following problem: where is a set of all permit policies at time . Intuitively, a myopic investor only cares about the mean and variance of the surplus at the current period.
To measure the total risk of an investor among multiperiods (after time ), we employ a separable expected conditional mapping which is defined as (see Chen et al. [16]) which reflects all the risk in the future. Following this assumption, a separable dynamic meanvariance problem is defined as where is a set of all permit policies. The optimal policy of problem (4), which satisfies both Bellman's optimality principle and requirement REQ, is called TimeConsistent Strategy. Note that both Bellman's optimality principle and requirement REQ could be proved by following the methodology of Chen et al. [16]. Thus, problem (4) can be recursively solved by the dynamic programming technique. Applying the iteratedexpectation property of the expected operator, that is, for , we have (for more details see Chen et al. [16]). Then, problem (4) is equivalent to It follows from Bellman's optimality principle and (6) that (4) is equivalent to find an optimal strategy to satisfy the following problem:
We solve this problem in the following sections. In order to discuss the impact of riskless asset, the market is considered in two cases: with and without riskless asset. We demonstrate these results in Sections 3 and 4, respectively.
3. Time Consistent Optimal Strategy without Riskless Asset
Consider a market consisting of only risky assets and assume that the wealth process is in a selffinancing fashion. We list the notations of this section in Table 1. The wealth process could be described as follows: where . In this setting, problem (6) can be written as follows:

Since is positive definite, we have . Further, it follows from the nonnegative definiteness of that is also positive definite and . By using mathematical induction, we conclude that, for any , is also positive definite and .
By applying Bellman's optimality principle, the timeconsistent optimal investment policy of problem (9) is given in the following theorem.
Theorem 1. The timeconsistent optimal investment policy of problem (9) is given by
Proof. When , for given wealth and liability at the beginning of the th period, problem (9) can be expressed as follows:
Substituting the binding constraints into the objective function, we have
which is a linearquadratic program. By using the Lagrange multiplier technique and letting be the Lagrange multiplier, the Lagrange function is defined as
By using the firstorder necessary optimality condition, we have
From (14), we can easily have
which implies that the Lagrange multiplier is
Substituting into (16), we have
Further, by substituting into the objective function of problem (12) (see Appendix A for more details), we have
When , for given wealth and liability at the beginning of the th period, the corresponding optimal investment problem is given as follows:
It follows from (20) that
Substituting and the binding constraints into the objective function of problem (21), we have
where
By setting be the Lagrange multiplier, the Lagrange function for problem (23) is
where
From the firstorder necessary optimality condition, we have
From (27), we have
which implies
Substituting into (29), we get
Taking into account, we have
Substituting into (23) gives (see Appendix B for more details)
Next, by using mathematical induction, we show that both (10) and
hold. Suppose that (10) and (35) hold for time . At the beginning of th period, for given wealth and liability , the corresponding optimal investment problem is
It follows from (35) that
Substituting and the binding constraints into the objective function of problem (36), we have
where
Letting be the Lagrange multiplier, the Lagrange function for problem (38) is given by
It follows from the firstorder necessary optimality condition that
Thus, we have
which implies
It follows from (42) that
Substituting into the objective function of problem (38) (see Appendix C for more details), we have
This completes the proof.
Remark 2. If an investor does not have any liability, that is, for any , then the optimal timeconsistent investment strategy can be simplified as follows: which is exactly the same as that in [16]. This implies that the result of Chen et al. [16] is a special case of Theorem 1. Therefore, Theorem 1 generalizes their result.
Corollary 3. If the returns of liability and risky assets are uncorrelated, that is, for any , then the optimal investment policy for problem (9) is
Proof. Since , it is easy to verify that , and . Substituting them into (10) gives This completes the proof.
Remark 4. After comparing Corollary 3 and Remark 2, it is quite clear that, if the return of liability is uncorrelated with that of risky asset, then the liability does not affect the timeconsistent optimal policy in a market without riskless asset.
Remark 5. If the return of liability is correlated to those of risky assets, then the occurrence of liability leads to a parallel shift of the optimal investment policy and the shift is which depends on the current value of liability, , and the covariance between the returns of liability and risky assets, .
Next, we compare the timeconsistent strategy with the myopic strategy in a market without riskless asset. In such a market, problem (2) can be expressed as follows: By using the same method in the proof of Theorem 1 for time , the myopic strategy is given by where , , . It is clear that the difference between two strategies enters into all of the three parts. More specifically, the following feature holds; if the investor is arbitrarily risk averse, that is, , then both the time consistent optimal strategy and myopic strategy reduce to respectively. After comparing these two strategies, we find that, if an investor is arbitrarily risk averse, then he/she should concern about the timediversification effects arising from multiperiod optimization.
4. Time Consistent Optimal Strategy with Riskless Asset
In this section, we consider a market which is consisting of one riskless asset and risky assets and assume that the wealth process is also in a selffinancing fashion. We list the notations of this section in Table 2. The wealth process can be described as follows: where . In this setting, problem (6) can be written as follows:

By applying Bellman's optimality principle, the timeconsistent optimal investment policy of problem (55) is given in the following theorem.
Theorem 6. The optimal investment strategy of problem (55) is given by where and .
Proof. When , for given wealth and liability at the beginning of the th period, problem (55) reduces to
Substituting the binding constraints into the objective function, we have
where
It is clear that problem (58) is an unconstrained convex program problem. By using the firstorder necessary optimality condition, we have
which implies
Substituting into the objective function of problem (58) gives (see Appendix D for more details)
where .
When , for given wealth and liability at the beginning of the th period, the corresponding optimal investment problem can be expressed as follows:
From (62), we can easily have
Substituting and the binding constraints into the objective function of problem (63), we have
where
The firstorder necessary optimality condition implies
Thus,
Substituting into the objective function of problem (65) (see Appendix E for more details), we have
where .
Next, by using mathematical induction, we show that both (56) and
hold, where with . Suppose that (56) and (70) are true for time . At the beginning of the th period, for given wealth and liability , the corresponding optimal investment problem is
It follows from (70) that
Substituting and the binding constraints into the objective function of problem (71), we have
where
The firstorder necessary optimality condition gives