Liability Management Model Research Articles

МОДЕЛІ УПРАВЛІННЯ ФІНАНСОВОЮ ЕКОНОМІЧНОЮ БЕЗПЕКОЮ ПІДПРИЄМСТВА

Management of financial and economic security is a key aspect for the stable development and competitiveness of enterprises in the market. Modern challenges, such as globalization and rapid technological change, require the adaptation of traditional management models to dynamic business conditions. The article discusses integrated approaches, risk management, strategic planning, asset and liability management, compliance management and other models that allow for comprehensive identification, assessment and management of financial risks. These models result in strengthening financial stability and ensuring adaptability to changes in the business environment. Keywords: management, financial security, risk management, strategic planning, compliance management.

Multistage stochastic dominance: an application to pension fund management

AbstractA pension fund manager typically decides the allocation of the pension fund assets taking into account a long-term sustainability goal. Many asset and liability management models, in the form of multistage stochastic programming problem, have been proposed to help the pension fund manager to define the optimal allocation given a multi-objective function. The recent literature proposes univariate stochastic dominance constraints to guarantee that the optimal strategy is able to stochastically dominate a benchmark portfolio. In this work we extend previous results (i) considering alternative types of multivariate stochastic dominance that appear more suitable in a multistage framework, (ii) proposing a way to measure the economic cost of introducing stochastic dominance constraints, (iii) proposing a sort of augmented stochastic dominance through a safety margin. Numerical results show the difference between the alternative ways to interpret and apply the multivariate stochastic dominance. These results are evaluated thanks to the proposed economic cost of the stochastic dominance constraints and either in presence or not of a safety margin.

Long-term stability of a life insurer’s balance sheet

In this paper, we devise a stochastic asset–liability management (ALM) model for a life insurance company and analyze its influence on the balance sheet within a low-interest rate environment. In particular, a flexible procedure for the generation of insurers’ compressed contract portfolios that respects the given biometric structure is presented, extending the existing literature on stochastic ALM modeling. The introduced balance sheet model is in line with the principles of double-entry bookkeeping as required in accounting. We further focus on the incorporation of new business, i.e. the addition of newly concluded contracts and thus of insured in each period. Efficient simulations are obtained by integrating new policies into existing cohorts according to contract-related criteria. We provide new results on the consistency of the balance sheet equations. In extensive simulation studies for different scenarios regarding the business form of today’s life insurers, we utilize these to analyze the long-term behavior and the stability of the components of the balance sheet for different asset–liability approaches. Finally, we investigate the robustness of two prominent investment strategies against crashes in the capital markets, which lead to extreme liquidity shocks and thus threaten the insurer’s financial health.

Development of a new asset liability Management model with liquidity and inflation risks based on the Lower Partial Moment

Inflation and liquidity risks are two significant risks with which pension fund managers must contend. This study develops a new multi-period Asset Liability Management model to address these challenges. The model objective function minimizes both risks simultaneously. The Lower Partial Moment measure is used to minimize investment portfolio risk and a CVaR constraint is employed to avoid loss. A hybrid ARIMA-GARCH model is developed to generate scenarios regarding a case study from Iran. To do so, four different investment cases out of seven stocks from the stock market, one bond, three foreign currencies (Dollar, Euro, and Yen) and gold are considered. Findings affirm model efficacy and the model suggests that a portfolio of all four assets is the best investment strategy.

A stochastic Asset Liability Management model for life insurance companies

We build a stochastic Asset Liability Management (ALM) model for a life insurance company. Therefore, we deal with both an asset portfolio, made up of bonds, equity and cash, and a liability portfolio, comprising with-profit life insurance policies. We define a mortality model and a surrender model, as well as a new production model. First, with the purpose of ensuring the solvency of the company and the achievement of a competitive return, in the interest of both shareholders and policyholders, the insurer’s portfolio is periodically rebalanced according to the solution of a nonlinearly constrained optimization problem that aims to match asset and liability durations, subject to the attainment of a target return. In addition, several real-world constraints are imposed. When computing the company balance sheet projections, we consider not only future maturity and death payments, but also future surrender payments and all the cash flows due to new production, in order to obtain estimates that are as reliable as possible. The estimation of the timing and of the numbers of future surrenders and of future new policyholders requires the approximation of conditional expectations: To this end, we employ the least squares Monte Carlo technique. Secondly, for each bonds asset class and for equity asset class we propose a sectorial optimization problem with the aim of maximizing the expected value of a chosen utility function, subject to the results obtained from the first stage of portfolio rebalancing. Finally, we analyze a case study.

Scenario Generation for Asset and Liability Management Models Applied to a Saudi Arabian Pension Fund

In Asset and Liability Management (ALM) models, there are parameters whose values are not known with certainty at decision time, such as future asset returns, liability and contribution values. Simulation models generate possible “scenarios” for these parameters, which are used as inputs in the optimisation models and help thus in making decisions. These decisions can be evaluated in the sample, on the same scenarios that were used for making the decision, and out-of-sample, on a different, usually much larger, scenario set. With asset return simulation, the major difficulty lies in the multivariate nature of the data. We propose to capture this via the historical copula, making thus no distributional assumptions. We suggest the use of univariate sample generation which allows for different asset returns to be modelled by different distributions. The liabilities and contributions values have as a main source of uncertainty the population numbers; we propose to model this by adapting a model used in biology (BIDE). We use the resulting scenario generator in four different ALM optimisation models, using a dataset from the largest Saudi Arabian pension fund and the Saudi Arabian market index.

Pension fund management with investment certificates and stochastic dominance

This paper considers an extension of the common asset universe of a pension fund to investment certificates. Investment certificates are a class of structured products particularly interesting for their special payoff structures and they are acquiring relevancy in the worldwide markets. In fact, some subclasses of certificates offer loss protection and show high liquidity and, thus, they can be very appreciated by pension fund managers. We consider the problem of a pension fund manager who has to implement an Asset and Liability Management model trying to achieve a long-term sustainability. Therefore, we formulate a multi-stage stochastic programming problem adopting a discrete scenario tree and a multi-objective function. We propose a technique to price highly structured products such as investment certificates on a discrete scenario tree. Finally, we solve the investment problem considering some investment certificate types both in term of payoff structure and protection level, and we test whether they are preferred or not to standard hedging contract such as put options. Moreover, we test the inclusion of first-order and second-order stochastic dominance constraints on multiple stages with respect to a benchmark portfolio. Numerical results show that the portfolio composition reacts to the inclusion of the stochastic dominance constraints, and that the optimal portfolio is efficiently able to reach several targets such as liquidity, returns, sponsor’s extraordinary contribution and funding gap.

A decision-dependent randomness stochastic program for asset–liability management model with a pricing decision

In this study, we present a stochastic programming asset–liability management model which deals with decision-dependent randomness. The model focuses on a pricing problem and the subsequent asset–liability management problem describing the typical life of a consumer loan. Such problems are frequently tackled by many companies, including multinationals. When doing so, they must consider numerous factors. These factors include the possibility of their customer rejecting the loan, the possibility of the customer defaulting on the loan and the possibility of prepayment. The randomness associated with these factors have a clear relationship with the offered interest rate of the loan which is the company’s decision and thus, induces decision-dependent randomness. Another important factor, which plays a major role for liabilities, is the price of money in the market. This is determined by the market interest rates. We captured their evolution in the form of a scenario tree. In summary, we formulated a non-linear, multi-stage stochastic program with decision-dependent randomness, which spanned the lifetime of a typical consumer loan. Its solution showed us the optimal decisions that the company should make. In addition, we performed a sensitivity analysis demonstrating the results of the model for various parameter settings that described different types of customers. Finally, we discuss the losses caused if companies do not act in the optimal way.

Asset liability management for Tanzania pension funds by stochastic programming

We present a long-termmodel of asset liability management for Tanzania pension funds. The pension system is pay-as-you-go where contributions are used to pay current benefits. The pension plan is a final salary defined benefit. Two kinds of pension benefits, a commuted (at retirement) and a monthly (old age) pension are considered. A decisive factor for a long-term asset liability management is that, Tanzania pension funds face an increase of their members’ life expectancy, which will cause the retirees to contributors dependence ratio to increase. We present a stochastic programming approach which allocates assets with the best return to raise the asset value closer to the level of liabilities. The model is based on work by Kouwenberg in 2001, with features from Tanzania pension system. In contrast to most asset liability management models for pension funds by stochastic programming, liabilities are modeled by using number of years of life expectancy for monthly benefit. Scenario trees are generated by using Monte Carlo simulation. Numerical results suggest that, in order to improve the long-term sustainability of the Tanzania pension fund system, it is necessary to make reforms concerning the contribution rate, investment guidelines and formulate target funding ratios to characterize the pension funds’ solvency situation. Keywords: Pay-as-you-go pension fund, asset liability management, stochastic programming, scenario trees. AMS 2010 Mathematics Subject Classification: 62P05, 90C15

Pension fund management with hedging derivatives, stochastic dominance and nodal contamination

The main goal of a pension fund manager is sustainability. We propose an Asset and Liability Management model structured as a multi-stage stochastic programming problem adopting a discrete scenario tree and a multi-objective function. Among other constraints, we consider the second-order stochastic dominance with respect to a benchmark portfolio. To protect the pension fund from shocks, we test the inclusion of hedge financial contracts in the form of put options and, moreover, we stress the portfolio introducing a new scenario tree contamination technique, namely the nodal contamination. Numerical results show that we can efficiently manage the pension fund satisfying several targets such as liquidity, returns, sponsor’s extraordinary contribution and funding gap. Moreover, we test the sensitivity with respect to put option strikes and to the stochastic dominance constraints. Finally, we demonstrate the effect of the scenario tree contamination.

Asset liability management model with decision support system for life insurance companies: Computational results

Big Data Analytics is an important and flexible tool available for data analysis and informed decision making. In this paper, we look at the use of Big Data Analytics in asset liability management and asset allocation in uncertain economic situations using stochastic linear programming (SLP). In particular, this paper is an extension of our earlier work and we contribute to the existing literature by conducting experiments on the stochastic model through DSS. In particular, for this SLP based DSS, we address issues like the optimal number of scenarios required for good results, and the impact of the change in the number of scenarios on the stability of the model. The paper also addresses the impact of the change in the number of scenarios on the policy holders’ as well as shareholders’ reserves. In particular, we show the relevance of employing a larger number of scenarios and also present the experimental design developed to test the relevance of this model. We also show that a stochastic model employing fewer scenarios produced marked improvements in both return side measures as well as risk side measures compared to a mean value model or a partial mean value model.

ALM models based on second order stochastic dominance

We propose asset and liability management models in which the risk of underfunding is modelled based on the concept of stochastic dominance. Investment decisions are taken such that the distribution of the funding ratio, that is, the ratio of asset to liabilities, is non-dominated with respect to second order stochastic dominance. In addition, the funding ratio distribution is close in an optimal sense to a user-specified target distribution. Interesting results are obtained when the target distribution is degenerate; in this case, we can obtain equivalent risk minimisation models, with risk defined as expected shortfall or as worst case loss. As an application, we consider the financial planning problem of a defined benefit pension fund in Saudi Arabia.

Generalization of the Annuity Factor

The well-known Annuity Factor, restricted to constant payments only, can be generalized for time dependent payments. A Generalized Annuity Factor (GAF) broadens the application potential considerably as is shown exemplarily for the valuation of loans and pension obligations. For the first time for such linear and nonlinear payments over time, compressed closed-form formulae for important financial key numbers such as present value, duration, convexity or value at risk can be derived. Moreover, easy computation makes General Annuity Factors a useful valuation tool especially in the field of finance and accounting. As General Annuity Factors can be implemented as User Defined Functions in a spreadsheet program, calculations can also be done in smaller firms or public services. Because of its computational efficiency the new instrument is also suitable for far-sighted economical models such as Asset Liability Management models (ALM) or life-cycle valuation models concerning products or investments.

New Asset Liability Management Model with Decision Support System for Life Insurance Companies: Interface Design Issues for Database and Mathematical Models

We introduce a new asset liability management (ALM) model based on decision support system (DSS) for a life insurance firm. The DSS is based on multi-stage stochastic linear programming (SLP) with recourse for strategic planning. The model can be used with minimal knowledge of management sciences. The model maximises the expected value of total reserve (policy holders' reserve and shareholders' reserve) at the end of the time period of planning with constraints both on the asset and the liability side of the firm's balance sheet. We discuss issues related to DSS interface design, one to one correspondences between the SLP model and the database and the difficulty in multi stage DSS compared to two stage DSS. We also compare and contrast the similarities and differences with our earlier work on SLP based DSS for process industries.

New asset liability management model with decision support system for life insurance companies: interface design issues for database and mathematical models

New asset liability management model with decision support system for life insurance companies: interface design issues for database and mathematical models

GPU parallel implementation for asset-liability management in insurance companies

In this work we present a stochastic asset liability management (ALM) model for a life insurance company together with its numerical simulation, based in a Monte Carlo balance sheet projection, and we carry out its efficient parallel computation using graphics processing units (GPUs) hardware. The liabilities of the company consist of a portfolio comprising with-profit life insurance policies, that evolve according to the policyholder saving account, surrender and biometric models. On the asset side, we mainly consider bonds, equity and cash, so that appropriate stochastic models are considered for their evolution. We consider some innovations with respect to literature in the modeling of the surrenders of the policyholders. Another important innovative aspect comes from the implementation of ALM in the new high performance computing architectures provided by GPUs technology. Numerical results illustrate the high speed up of the calculus by using GPUs and the coherence of the computations (asset evolution, default probabilities and so on).

The case for convex risk measures and scenario-dependent correlation matrices to replace VaR, C-VaR and covariance simulations for safer risk control of portfolios

Value at risk (VaR) is the most popular risk measure and is enshrined in various regulations. It postulates that portfolio losses are less than some prescribed amount most of the time. Therefore a loss of $ 10 million is the same as a loss of $ 5 billion. C-VaR tries to correct this by linearly penalizing the loss so a loss of $ 20 million is twice as damaging as that of $ 10 million with the same probability. This is an improvement but is not enough of a penalty to force investment portfolios to be structured to avoid these losses. The author has used convex risk measures since 1974 in various asset–liability management (ALM) models such as the Russell Yasuda Kasai and the Vienna InnoALM. They penalize losses at a much greater rate than linear rate so that double or triple losses are more than two or three times as undesirable. Also scenario-dependent correlation matrices are very important in model applications because ordinary average correlations tend to work when you do not need them and fail by givi...

A robust optimization approach to asset-liability management under time-varying investment opportunities

This paper presents an asset liability management model based on robust optimization techniques. The model explicitly takes into consideration the time-varying aspect of investment opportunities. The emphasis of the proposed approach is on computational tractability and practical appeal. Computational studies with real market data study the performance of robust-optimization-based strategies, and compare it to the performance of the classical stochastic programming approach.

Dynamic Optimal Pension Fund Portfolios when Risk Preferences Are Heterogeneous among Pension Participants

AbstractIn this paper, we consider the problem of an optimal pension fund portfolio given the heterogeneous risk preferences of pension fund participants. The relative risk aversion of a pension fund tends to be a decreasing function of the level of aggregate wealth. We find that the dynamic optimal portfolio is simply characterized as the weighted sum of the optimal portfolio for each participant. Our model helps successfully establish the microfoundation of asset liability management models. A numerical example using recent Japanese data indicates the significant total welfare losses of adopting a suboptimal portfolio strategy and an inefficient risk‐sharing rule.

Asset liability management for individual households

AbstractPersonal finance is a challenging topic which can benefit from a scientific approach to individual financial planning. This paper presents an individual asset liability management (iALM) model for life cycle planning which uses the methodology of dynamic stochastic optimisation and incorporates ideas from both classical and behavioural finance. Its implementation is in the form of a decision support tool for use by financial advisers or wealth managers. The investment universe is given by a set of indices for major asset classes and their returns are simulated forward over the lifetime of a household. On the liability side the foreseen cash flows of incomes and outgoings are simulated and punctuated by life events such as illness and death. The household's utility function is constructed for each time period over a range of monetary values in terms of household financial goals and preferences. Taxes and pension savings are treated using the tax shielded saving accounts specific to a national jurisdiction in terms of constraints in the optimisation sub-models. The paper goes on to present an analysis ofiALM model recommendations for a representative UK household, together with an evaluation of the sensitivity of the financial plan generated to changes in market environments such as the 2007–9 crisis. The promise of this new technology is to bring modern decision support tools to individual investors in order to facilitate custom designed consumption, savings and investment policies.