Time-consistent asset–liability management with imperfect information

<p style='text-indent:20px;'>In this paper, we study the robust optimal asset- problems for an ambiguity-averse investor, who does not have perfect information in the drift terms of the risky asset and liability processes. Two different kinds of objectives are considered: <inline-formula><tex-math id="M1">\begin{document}$ (i) $\end{document}</tex-math></inline-formula> Maximizing the minimal expected utility of the terminal wealth; <inline-formula><tex-math id="M2">\begin{document}$ (ii) $\end{document}</tex-math></inline-formula> Minimizing the maximal cumulative deviation. The ambiguity in both problems is described by a set of equivalent measures to the reference model. By the stochastic dynamic programming approach and Hamilton-Jacobi-Bellman (HJB) equation, we derive closed-form expressions for the value function and corresponding robust optimal investment strategy in each problem. Furthermore, some special cases are provided to investigate the effect of model uncertainty on the optimal investment strategy. Finally, the economic implication and parameter sensitivity are analyzed by some numerical examples. We also compare the robust optimal investment strategies in two different problems.</p>

With regard to asset liability management (ALM), the future evolution of the interest rate market plays a major role. This is natural, as with a deterministic evolution of the interest rate (in fact, only in a deterministic setting we could speak of the interest rate!), interest rate risk would not play a role in ALM. However, as the main objective of ALM is to relate payments occurring at dif-ferent times in a suitable way, especially when the time horizon is large, assum-ing a deterministic interest rate evolution is absolutely inappropriate. Thus, finding a good model for the evolution of the term structure of interest rates is one of the main obstacles to overcome for successful ALM.

Portfolio Selection in Asset Liability Management Using Probabilistic Utility Functions

Advances in market risk management have had a huge impact on asset liability management in recent years, enabling the most advanced institutions to analyze their balance sheet risks in a much more realistic and dynamic way. In future, as risk management develops still further and computing power increases, ALM could find itself with a new, broader function to perform.

Chapter 13 - Stochastic programming models for strategic and tactical asset allocation—A study from Norwegian life insurance

We present a long-term model 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

Asset Liability Management (ALM) can be overseen using financial ratios derived from financial statements. These statements provide a comprehensive picture of a company's status and necessitate analysis to evaluate performance. This research aims to analyze financial ratios to describe the financial condition, measure business development over time, and evaluate the achievement of the company's objectives. An optimization analysis of financial ratios is performed using the Weighted Goal Programming (WGP) model, which addresses multiple objectives by applying weights based on their priorities. The Best-Worst Method (BWM) was used to determine the priority weights of deviation variables from each financial ratio target. Financial ratios were selected based on their impact on profit using factor analysis. The constructed WGP model aims to minimize deviations in Return on Assets, Operating Ratios, Operating Income Ratio, Total Assets Turnover, and Current Ratio. Computational calculations to solve the WGP model are performed using Python, with pseudocode provided. A case study on a company in the garment and textile sector was conducted and found that the Operating Ratio, Return on Assets, Operating Income Ratio, and Current Ratio still need improvement by developing strategies to achieve the targets. Sensitivity analysis was also employed to assess the resilience of the model in response to alterations in data.

The Macaulay duration matched strategy is a key tool in bond portfolio immunization. It is well known that if term structures are not flat or changes are not parallel, then the Macaulay duration matched portfolio can not guarantee adequate immunization. In the paper the approximate duration is proposed to measure the bond price sensitivity to changes of interest rates of non-flat term structures. Its performance in immunization is compared with those of Macaulay and key rate durations using the US Treasury strips and bond data. Approximate duration turns out to be a possible contender in asset liability management: it does not assume any particular structures or patterns of changes of interest rates, it does not need short selling of bonds, and it is easy to set up and rebalance the optimal portfolio with linear programming.

073071 (E50) - Asset liability management and strategies of profit sharing

Stochastic Interest Rate Modeling: An Empirical Performance Analysis of the L vy Forward Price Model

Worst-case Conditional Value at Risk for asset liability management: A framework for general loss functions

Mean–variance asset–liability management with asset correlation risk and insurance liabilities

Insurance companies are facing major challenges that point to the need for control process and risk management. Risk management in insurance has a direct impact on solvency, economic security, and overall financial stability of insurance companies. It is very important for insurance companies to adequately calculate risks to which they are exposed. Asset liability management (ALM), as an integrated approach to financial management, requires simultaneous decision-making about categories and values of assets and liabilities in order to establish the optimum volume and the ratio of assets and liabilities, with the understanding of complexity of the financial market in which financial institutions operate. ALM focuses on a significant number of risks, whereby the emphasis in this paper will be on interest rate risk which indicates potential losses that may reflect in a lower interest margin, a lower value of assets or both, in terms of changes in interest rates. In the above context, the aim of this paper is to show how to protect from interest rate changes and how these changes influence the insurance market in Montenegro, both from the theoretical and the practical point of view. The authors consider this to be an interesting and very important topic, especially because the life insurance market in Montenegro is underdeveloped and subject to fluctuations. Also, taking into account the fact that Montenegro is a country that has been making serious efforts to join the EU, it is expected that insurance companies in Montenegro will strengthen their financial position in the market even using the ALM traditional techniques, which is shown in this paper.

The two-additive-factor Gaussian model G2[Formula: see text] is a famous stochastic model for the instantaneous short rate. It has functional qualities required in various practical purposes, as in Asset Liability Management and in Trading of interest rate derivatives. Though closed formulas for the prices of various main interest-rate instruments are known and used under the G2[Formula: see text] model, it seems that references for the corresponding sensitivities are not clearly presented over the financial literature. To fill this gap is one of our purposes in the present work. We derive here analytic expressions for the sensitivities of zero-coupon bond, coupon-bearing bonds, portfolio of coupon bearing bonds. The sensitivities under consideration here are those with respect to the shocks linked to the unobservable two-uncertainty shock risk/opportunity factors underlying the G2[Formula: see text] model. As a such, the hedging of a position sensitive to the interest rate by means of a portfolio (in accordance with the market participants practice) becomes easily transparent as just resulting from the balance between the various involved sensitivities.

A Practical Robust Long Term Yield Curve Model