Asset Liability Management Research Articles

EFFECTIVENESS OF ASSET LIABILITY MANAGEMENT IN HI-TECH POLE AT PUDUCHERRY

Asset and Liability Management (ALM) is the art of properly managing sustainable financial resources. Infrastructure assets are managed through asset liability management in order to reduce the overall cost of ownership and operation while consistently providing the service levels that customers want. It combines engineering principles with good business practices and economic logic, and it provides the means to enable a more flexible and organized way of making decisions that are required to meet stakeholder and public expectations. In order to accomplish business performance based on its scale and complexity, the organization or firm must be able to manage its assets and liabilities optimally through the determination of a dependable plan. The primary goal of this analysis is to pinpoint the specific ratios that have an impact on the concern's assets and liabilities. The analysis makes it evident that the business hopes to provide a more effective service soon.

Analisis Penerapan Konsep Asset And Libality Management dengan Sistem Perbankan Syariah di Indonesia


 In banking operations, Asset Liability Management (ALMA) is the main focus in managing commercial banks. As a tool for analyzing and managing banking operations, it is something that is paid attention to by banking practitioners and academics. This research uses a type of library research. Bibliography research is research carried out using a bibliography in the form of books, memos and information from previous research. On the other hand, the method used is storytelling and analysis. This research results that the application of Asset and Liability Management in Islamic Banking is implemented in a special unit called ALCO (Asset and Liability Management Committee). This committee is responsible for planning, organizing and supervising Islamic bank investments.
 Keywords: Asset Liability Management, Sharia Banking, Systems and Concept

Mean-variance reinsurance and asset liability management with common shock via non-Markovian stochastic factors

This paper explores a reinsurance and asset liability management problem in a factor model with dependent risks, considering economic factors as non-Markovian processes impacting the appreciation rate, volatility, and value of liabilities. It encompasses various stochastic volatility (SV) models like Hull-White, Heston, 3/2, and 4/2 models as special cases. The model addresses dependent risks through a common shock in claim number processes. The insurer's aim is to minimize variance in terminal net wealth while achieving a certain expected terminal net wealth, balancing reinsurance, new business, and asset allocation. The paper employs linear-quadratic (LQ) control and backward stochastic differential equation (BSDE) theory to derive both the efficient strategy and the efficient frontier. To illustrate our results, numerical examples are provided in two special cases, the 3/2 and 4/2 SV models.

Effect of Macroeconomic Factors on the relationship between Asset Liability Management and Profitability of Commercial Banks in Kenya

Effect of Macroeconomic Factors on the relationship between Asset Liability Management and Profitability of Commercial Banks in Kenya

Analisis Penerapan Konsep Asset and Libality Management dengan Sistem Perbankan Syariah di Indonesia

In banking operations, Asset Liability Management (ALMA) is the main focus in managing commercial banks. As a tool for analyzing and managing banking operations, it is something that is paid attention to by banking practitioners and academics. This research uses a type of library research. Bibliography research is research carried out using a bibliography in the form of books, memos and information from previous research. On the other hand, the method used is storytelling and analysis. This research results that the application of Asset and Liability Management in Islamic Banking is implemented in a special unit called ALCO (Asset and Liability Management Committee). This committee is responsible for planning, organizing and supervising Islamic bank investments

Assets & Liability Management Analysis of IDBI Bank

One of the most important tools for risk management in banks is asset liability management. To improve their performance, the banks must implement appropriate Asset Liability Management procedures. Furthermore, asset liability management has purposes beyond mere risk mitigation. The protection that asset liability management provides creates additional chances to increase net worth. Ratio analysis was used to examine the assets and liabilities of IBDI bank. The IDBI Bank asset liability management will be examined during the sample period of 2013–2022. It offers the structure required to identify, quantify, track, adjust, and manage these risks.

Analysis of the risk transmission mechanism of monetary policy in the monetary and financial service sectors from the perspective of asset-liability management.

The Silicon Valley Bank crisis that occurred in March 2023 highlights the importance of studying the risk transmission mechanism underlying monetary policy. To explore this mechanism in the monetary and financial services sector from the perspective of asset-liability management, the relationships between asset- and liability-side risks and the corresponding action direction of those risks were empirically studied based on the risk-taking channel theory of monetary policy using publicly available information on 176 Chinese monetary and financial services companies covering the period of 2000 to 2022. The analysis shows that asset-side risks are transmitted through monetary policy as liability-side risks in the monetary and financial service sectors. If the liability side passively takes on these risks, the action direction of monetary policy to asset-side risks is determined; when the liability side actively takes on these risks, monetary policy exerts opposite effects on both types of risks. This conclusion somewhat deepens our understanding of the risk transmission mechanism of monetary policy, which is helpful in better formulating and implementing monetary policy.

Application of deep reinforcement learning in asset liability management

Asset Liability Management (ALM) is an essential risk management technique in Quantitative Finance and Actuarial Science. It aims to maximise a risk-taker's ability to fulfil future liabilities. ALM is especially critical in environments of elevated interest rate changes, as has been experienced globally between 2021 and 2023. Traditional ALM implementation is still heavily dependent on the judgement of professionals such as Quants, Actuaries or Investment Managers. This over-reliance on human input critically limits ALM performance due to restricted automation, human irrationality and restricted scope for multi-objective optimisation. This paper addressed these limitations by applying Deep Reinforcement Learning (DRL), which optimises through trial, and error and continuous feedback from the environment. We defined the Reinforcement Learning (RL) components for the ALM application: the RL decision-making Agent, Environment, Actions, States and Reward Functions. The results demonstrated that DRL ALM can achieve duration-matching outcomes within 1% of the theoretical ALM at a 95% confidence level. Furthermore, compared to a benchmark weekly rebalancing traditional ALM regime, DRL ALM achieved superior outcomes of net portfolios which are, on average, 3 times less sensitive to interest rate changes. DRL also allows for increased automation, flexibility, and multi-objective optimisation in ALM, reducing the negative impact of human limitations and improving risk management outcomes. The findings and principles presented in this study apply to various institutional risk-takers, including insurers, banks, pension funds, and asset managers. Overall, DRL ALM provides a promising Artificial Intelligence (AI) avenue for improving risk management outcomes compared to the traditional approaches.

The Impact of IFRS 17 Adoption on Insurance Industry and Asset-Liability Management

IFRS 17完整改變保險合約負債之衡量，其中IFRS17.B72(a)要求保險合約負債之折現均需採用現時利率，同時IFRS 17.88也允許個體基於資產負債管理，將利率影響數拆分至其他綜合損益。故可預期保險負債之會計處理將深受利率影響，且須基於資產負債管理進行會計處理。接軌IFRS 17後，IFRS 17.C29也要求個體重新指定金融資產分類，可知IFRS 17之適用不僅影響負債端也影響資產端。本文將就接軌IFRS 17對資產與負債之影響進行討論，並延伸討論避險會計適用之可能性。

Acceptance of large corporate receivables as collateral – the practice of the Magyar Nemzeti Bank in view of the principles of collateral management, with a specific focus on legal risks

In the spring of 2020, the Magyar Nemzeti Bank (MNB) expanded the pool of assets eligible as collateral for central bank lending by accepting loans granted to large corporates customers as collateral, thus widening the lending channel between the MNB and credit institutions and providing credit institutions with a wider leeway in ALM (Asset-Liability Management). In this paper we analyse the MNB’s framework of conditions for the eligibility of large corporate receivables in respect to the principles of collateral management including, in particular, the management of legal risks. The inclusion of such transactions as collateral is challenging from both a legal and an operational perspective, since eligibility for use as collateral requires the development of a framework that fully ensures the enforceability of large corporate receivables accepted as collateral, and compliance with all other relevant collateral management principles. It is concluded from the analysis that a set of strict conditions and the operational framework supporting it can ensure the management of legal risks, but can also be noted that there may be trade-offs between simple asset-liability and liquidity management, and compliance with the complex system of corporate receivables conditions.

Robust optimal asset-liability management with mispricing and stochastic factor market dynamics

This paper investigates a robust optimal asset-liability management problem under an expected utility maximization criterion. More specifically, the manager is concerned about the potential model uncertainty and aims to seek the robust optimal investment strategies. We incorporate an uncontrollable random liability described by a generalized drifted Brownian motion. Also, the manager has access to an incomplete financial market consisting of a risk-free asset, a market index with potentially path-dependent, time-varying risk premium and volatility, and a pair of mispriced stocks. The market dynamics are assumed to rely on an affine-form, square-root factor process and the price error is modeled by a co-integrated system. We adopt a backward stochastic differential equation approach hinging on the martingale optimality principle to solve this non-Markovian robust control problem. Closed-form expressions for the robust optimal investment strategies, the probability perturbation process under the well-defined worst-case scenario and the corresponding value function are derived. The admissibility of the robust optimal controls is verified under some technical conditions. Finally, we perform some numerical examples to illustrate the effects of model parameters on the robust investment strategies and draw some economic interpretations from these results.

Bank crisis resolution in India

PurposeThe academic literature has examined why bank runs happen based on the work of 2022 Nobel Prize-winning economists Diamond and Dybvig. They have found the source of banking/financial crisis in terms of mismatch between liabilities (deposits being short term and savers wanting to short-term access to their money) and assets (long term and illiquid). The Lakshmi Vilas Bank (LVB) crisis intensified when it came under Prompt Corrective Action (PCA) of the Reserve Bank of India (RBI). This situation provides the opportunity to study whether the elements embodied in the theoretical models like Diamond and Dybvig hold true for LVB crisis. This study aims to examine the reasons for the demise of LVB in India using DuPont financial model, peer group analysis and time series structural break in crucial financial parameters.Design/methodology/approachThe study examines the reason for insolvency of LVB using financial ratios, financial models (DuPont), financial distress model (Z-score) and asset-liability management. The study also adopts univariate structural break models using quarterly financial data covering the key financial measures used in the RBI’s PCA framework.FindingsLVB crisis is like Diamond–Dybvig model, in the sense, savers requiring short-term access to their money (liquidity for their deposits) on the information of high non-performing assets, which further deteriorates the illiquid nature of loan portfolio (assets) of banks. The study finds its profit margin (net interest margin and non-interest margin) and managerial efficiency had started deteriorating since 2018. The study finds that LVB’s main weakness lies in its limited credit appraisal ability, its monitoring and weak internal controls. Lending to sensitive sectors (like real estate, capital markets and commodities) and exposure to large business groups also contributed to its weakness. The study also finds huge, elevated asset-liability mismatch, especially in the short-term maturity buckets. Using univariate econometric time series model, the study also confirms financial weakness being evident much earlier than the time when resolution was undertaken by the RBI through PCA.Research limitations/implicationsThe study has implications for analysing and monitoring financial distress of banks. The study also has implications for devising banking regulation and supervision.Originality/valueThe study brings in a perspective of the banking regulations using the application of PCA framework on a listed private sector bank. The authors combine an accounting ratio model and combine risk measures that could identify the incipient risks in a bank. The authors believe this will help in refinement of banking regulations and better monitoring mechanisms.

Studi Literatur: Pengaruh Asset And Liabilities Management Terhadap Perbankan

This study describes Asset Liability Management of the Company through surveyed journals in Indonesia from 2017 to 2022. The purpose of this study is to add and develop research or articles on Asset and Liability Management (ALMA) published in Indonesian journals. The samples obtained were 25 articles. This research uses a systematic literature study method, with a PoP (Publish or Perish) application. From this literature study, we conclude that the operation of the Asset and Liability Management (ALMA) function can optimize the structure of a bank's balance sheet in a competitive financial market environment through bank liquidity management policies while maintaining quota and liquidity status. These include primary and secondary reserves, gap management by managing and controlling the gap between assets and liabilities to maximize net profit margin (NIM), exchange rate management by maximizing exchange rate differential returns, investment management and returns through investment decisions in a bank's fortopolio.

Asset liability management and performance of deposit money banks in Nigeria and Ghana

This study examines the effect of ALM on the performance Return on Assets (ROA) and Return on Equity (ROE) of DMBs in Nigeria and Ghana. The study used ex-post facto research design. The population comprised 18 DMBs licensed in Nigeria and 22 DMBs licensed in Ghana as at 31st December 2021. Purposive sampling technique was adopted to select a sample of 10 DMBs each with relevant data from Nigeria and Ghana. Validated data were obtained from the annual reports of the banks for the period 2007-2021. The reliability of data was premised on the statutory audit and approval of the Financial Statements by the regulatory agencies in Nigeria and Ghana. Data were analyzed using descriptive and inferential statistics. The findings showed that while ALM had insignificant effect on ROA of DMBs in Nigeria (Adj.R2 = 0.297, W(3, 143) = 3.62, p > 0.05) and Ghana (Adj.R2 = 0.372, W(3, 143) = 3.13, p > 0.05), ALM with control variables exerted significant effect on ROA of DMBs in Nigeria (Adj.R2 = 0.296, W(6, 143) = 29.70, p < 0.05) and Ghana (Adj.R2 = 0.394, W(6, 143) = 33.41, p < 0.05). ALM exerted significant effect on ROE of DMBs in Nigeria (Adj.R2 = 0.327, W(3, 146) = 4.17, p < 0.05) and Ghana (Adj.R2 = 0.351, W(3, 146) = 3.68, p < 0.05). ALM with control variables had significant effect on ROE of DMBs in Nigeria (Adj.R2 = 0.293, W(6, 143) = 6.38, p < 0.05) but an insignificant effect on ROE in Ghana (Adj.R2 = 0.337, W(6, 143) = 1.94, p > 0.05).The study concludes that ALM affected performance of DMBs in Nigeria and Ghana. The study recommended that the management of DMBs in both countries should continuously adopt ALM to improve performance. Keywords: Asset liability management, Deposit money banks, Loan deposit ratio, Net interest margin, Return on assets, and Return on equity

Optimal Investment Policy for Pension Annuitants in Malaysia under Economic Exposure

This research has conducted a profound evaluation into the annuity market in Malaysia from both the business and individual perspective. To a great extent, analysis is made on how the annuity insurers could better maintain the solvency level of annuity fund which is exposed under significant economic risks by obtaining an optimal fund allocation to maximise investment returns through the development of Asset-Liability Management (ALM). By adapting the concept from Wilkie model, six fundamental models including inflation rate, real interest rate, KLCI stock return, KLCI stock dividend yield, 10-year MGS return as well as Malaysian property return were formulated accordingly to the Box-Jenkins modelling approach. It concludes that the optimal weightages combination suggests that the annuity insurers should allocate 94% of the single premium such as RM100,000 which collected from the annuitant to bond market, whereas the other 5% of the fund should be invested in stock but only 1% of the premium should be allocated into the property investment.

Robust optimal asset-liability management under square-root factor processes and model ambiguity: a BSDE approach

This article studies robust optimal asset-liability management problems for an ambiguity-averse manager in a possibly non-Markovian environment with stochastic investment opportunities. The manager has access to one risk-free asset and one risky asset in a financial market. The market price of risk relies on a stochastic factor process satisfying an affine-form, square-root, Markovian model, whereas the risky asset’s return rate and volatility are potentially given by general non-Markovian, unbounded stochastic processes. This financial framework includes, but is not limited to, the constant elasticity of variance (CEV) model, the family of 4/2 stochastic volatility models, and some path-dependent non-Markovian models, as exceptional cases. As opposed to most of the papers using the Hamilton-Jacobi-Bellman-Issacs (HJBI) equation to deal with model ambiguity in the Markovian cases, we address the non-Markovian case by proposing a backward stochastic differential equation (BSDE) approach. By solving the associated BSDEs explicitly, we derive, in closed form, the robust optimal controls and robust optimal value functions for power and exponential utility, respectively. In addition, analytical solutions to some particular cases of our model are provided. Finally, the effects of model ambiguity and market parameters on the robust optimal investment strategies are illustrated under the CEV model and 4/2 model with numerical examples.

Optimization of Asset and Liability Management of Banks with Minimum Possible Changes

Asset-Liability Management (ALM) of banks is defined as simultaneous planning of all bank assets and liabilities under different conditions and its purpose is to maximize profits and minimize the risks in banks by optimizing the parameters in the balance sheet. Most of the studies `and proposed models in the ALM field are based on an objective function that maximizes bank profit. It is not easy to apply changes in these models in order to reach the optimal values of the parameters in the balance sheet. In this article, an attempt has been made to propose a linear model using constraints to achieve optimal values of balance sheet parameters using ALM objectives and considering balance sheet, system and regulatory constraints. It has also been tried to design the model according to the most possible mode and with the least changes and to minimize the size of the balance sheet. The analysis of the model presented in this article has been conducted using the parameters of the balance sheet and income statement of one of the famous Iranian banks. The results obtained from the proposed model show that the values of cash and receivables from banks and other credit institutions have decreased by 30% and increased by 200%, respectively, compared to the actual values of these parameters. Also, Total Income, Operating Income and Non-Operating Income have grown by 30% compared to the actual values of these parameters. Also, the values of a number of parameters are estimated to be zero after optimization. According to the results, it is obvious that the performance of bank managers, especially in the management of bank assets, is significantly different from the optimal values of the balance sheet, and the results obtained from the proposed model can help the management of banks as much as possible.

The fall of DHFL: hidden issues that everyone ignored

Learning outcomes The learning outcomes are as follows: to be able to evaluate early warning signs/red flags through financial statement analysis; to be able to analyse company’s credit or debt servicing using a thorough process of fundamental analysis; to be able to analyse and decode the financial health of an organization through different financial tools applicable according to the industry such as default probability and financial ratios; and to be able to synthesize credit rating framework and role of credit rating agencies in the bond market. Case overview/synopsis In late January 2019, the allegation by an online investigative portal about the misuse of the Dewan Housing Finance Corporation Ltd. (DHFL) money by its promoter for buying asset abroad was the start of the fall of the non-banking finance company giant. This was followed by a series of downgrade by credit rating agencies on its debt and eventual default on its interest payment on 4 June 2019 which upset multiple portfolio investors and the regulators. Investors became sceptical about the regulator’s policy and inefficiencies of credit rating agencies in predicting the default along with asset management houses which were expected to guard investors’ interest. One investor, Shikhar Pachori, decided to scrutinize all hidden information on DHFL to investigate if DHFL crisis arises because of unknown factors which was not in control of management or if it a clear negligence on the part of all involved parties. The case tries to emphasize the aspect of Asset-Liability Management and process of credit analysis while looking for red flags which aids in identifying any stress in company’s financial or any potential default by company. Complexity academic level This case can be used in the advance level of post-graduate finance course or MBA program for elective/specialization courses such as Financial Statement Analysis, Financial Institutions and Market and Fixed Income. Supplementary materials Teaching notes are available for educators only. Subject code CSS 1: Accounting and Finance

Current Situation and Challenges in Indian Banking Sector

Abstract: The Indian banking sector is facing several challenges due to the rapidly changing financial environment. The key issues include the high level of Non-Performing Assets (NPAs), increasing competition from fintech companies and digital payment platforms, difficulty in meeting capital adequacy requirements, concerns about governance and risk management, and challenges in Asset Liability Management (ALM). Addressing these challenges is critical for the Indian banking sector to remain competitive and continue playing a crucial role in the country’s economy. One of the largest in the world and essential to the nation's economy is the Indian banking sector. However, it is currently facing several challenges due to the rapidly changing financial environment. Here are some of the key issues and challenges faced by the Indian banking sector: Non-Performing Assets (NPAs): The high level of NPAs, or bad loans, is one of the main problems Indian banks face. The gross NPA ratio of Indian banks, according to the Reserve Bank of India (RBI), was 7.5% in March 2021. The profitability and capital sufficiency of banks have been under intense strain as a result of this is digital disruption.The traditional banking sector has been impacted by the growth of fintech businesses and digital payment platforms. These new competitors, who can provide customers with speedier, more convenient, and less expensive services, are putting pressure on Indian banks.

A case study for unlocking the potential of deep learning in asset-liability-management.

The extensive application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. This article presents the key notions of Deep Asset-Liability-Management ("Deep ALM") for a technological transformation in the management of assets and liabilities along a whole term structure. The approach has a profound impact on a wide range of applications such as optimal decision making for treasurers, optimal procurement of commodities or the optimization of hydroelectric power plants. As a by-product, intriguing aspects of goal-based investing or Asset-Liability-Management (ALM) in abstract terms concerning urgent challenges of our society are expected alongside. We illustrate the potential of the approach in a stylized case.