scholarly journals Modelling the Fair Value of Annuities Contracts: The Impact of Interest Rate Risk and Mortality Risk

2006 ◽  
Author(s):  
Laura Ballotta ◽  
Giorgia Esposito ◽  
Steven Haberman
Keyword(s):  
Interest Rate ◽  
Mortality Risk ◽  
Fair Value ◽  
Interest Rate Risk ◽  
Rate Risk ◽  
The Impact

scholarly journals The Effect of Risks on Banking Safety: Applied Study on Jordanian Traditional Banks (2005-2016)

2017 ◽  
Vol 9 (9) ◽  
pp. 102
Author(s):  
Mohammad Abdel Mohsen Al-Afeef ◽  
Atallah Hassan Al-Ta'ani
Keyword(s):  
Interest Rate ◽  
Credit Risk ◽  
Commercial Banks ◽  
Banking Sector ◽  
Liquidity Risk ◽  
Interest Rate Risk ◽  
Bank Credit ◽  
Applied Study ◽  
Rate Risk ◽  
The Impact

Banking sector is one of the most important sectors that support the sustainable economic development in Jordan, therefore this study aimed to test the impact of risks; (Liquidity risk, bank credit risk and interest rate risk) on the safety in the banking sector in the Jordanian commercial banks during the period 2005-2016.The results of the study showed that there is a statistically significant impact for each of liquidity risk and interest rate risk on the safety in the banking sector, and there isn't statistically significant impact for credit risk on the safety in the banking sector during the period of this study, and also find that the explanatory of model was 60.5%, which means that 39.5% due to other factors.

scholarly journals The impact of interest rate risk on bank lending

2020 ◽  
Vol 115 ◽  
pp. 105797 ◽  
Author(s):  
Toni Beutler ◽  
Robert Bichsel ◽  
Adrian Bruhin ◽  
Jayson Danton
Keyword(s):  
Interest Rate ◽  
Bank Lending ◽  
Interest Rate Risk ◽  
Rate Risk ◽  
The Impact

A Bayesian Pricing of Longevity Derivatives with Interest Rate Risks

2017 ◽  
Vol 0 (0) ◽  
Author(s):  
Atsuyuki Kogure ◽  
Takahiro Fushimi
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Mortality Risk ◽  
Interest Rate Risk ◽  
Stochastic Interest Rates ◽  
Rate Risk ◽  
Stochastic Interest ◽  
The Interest Rate ◽  
Longevity Bonds ◽  
Carter Model

AbstractMortality-linked securities such as longevity bonds or longevity swaps usually depend on not only mortality risk but also interest rate risk. However, in the existing pricing methodologies, it is often the case that only the mortality risk is modeled to change in a stochastic manner and the interest rate is kept fixed at a pre-specified level. In order to develop large and liquid longevity markets, it is essential to incorporate the interest rate risk into pricing mortality-linked securities. In this paper we tackle the issue by considering the pricing of longevity derivatives under stochastic interest rates following the CIR model. As for the mortality modeling, we use a two-factor extension of the Lee-Carter model by noting the recent studies which point out the inconsistencies of the original Lee-Carter model with observed mortality rates due to its single factor structure. To address the issue of parameter uncertainty, we propose using a Bayesian methodology both to estimate the models and to price longevity derivatives in line with (Kogure, A., and Y. Kurachi. 2010. “A Bayesian Approach to Pricing Longevity Risk Based on Risk Neutral Predictive Distributions.”

scholarly journals Assessing influence of factors on interest risk of commercial bank

2021 ◽  
Vol 2021 (1) ◽  
pp. 115-124
Author(s):  
Olga Mikhailovna Markova
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Interest Rates ◽  
Commercial Bank ◽  
Industrial Development ◽  
The United States ◽  
Interest Rate Risk ◽  
Interest Income ◽  
Rate Risk ◽  
The Impact

In modern conditions of the rapid industrial development the banks have to forecast their risks and profitability precisely, to apply information technologies to assess their activities. To evaluate the bank's income, it is necessary to carry out an internal analysis of its assets and liabilities and determine the factors effecting the bank's profitability by managing interest rate risk. The hypothesis of the study is the analysis of the impact on the net interest income and interest rate risk of a commercial bank of factors such as the exchange rate and the key rate of the Bank of Russia (for example, Sberbank, PJSC). There has been studied the impact of the factors (exchange rate and key interest rate of Central Bank of Russia) on the bank's net interest income by using correlation and regression analysis and building a regression model. Many tools are found to be used by the experienced analysts. One of the main tools is GAP analysis of interest rate risk. There have been illustrated the graphs of changes in interest rates of savings and loan associations during the crisis in the United States in the 1950-1960, of realization of interest rate risk with an increase in interest rates, the distribution of assets and liabilities according to the maturity of the balance sheet structure, the impact of changes in the interest rate GAP on net interest income, etc. A matrix of correlations of all variables in the sample (rates of growing values) was constructed. Conclusions are drawn on the need to use hedging instruments (interest rate swaps, interest rate options), as well as of attracting the most reliable data on the state of interest rate risk in the commercial banks.

Nexus Between Interest Rate Risk and Economic Value of Equity of Banks

2021 ◽  
pp. 097215092110394
Author(s):  
Soundariya G. ◽  
Treesa Aleena David ◽  
Suresh G.
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Banking Sector ◽  
Economic Value ◽  
Impulse Response Function ◽  
Interest Rate Risk ◽  
Vector Error Correction ◽  
Market Risks ◽  
Rate Risk ◽  
The Impact

This analytical study looks to provide recommendations to the banking sector on different policies and regulations by examining certain aspects of the Basel III accord, which was designed to manage specific operational, capital and market risks of banks. A review of extant literature reveals that only a few papers have been written on simulation-based approaches, using basis and re-pricing risks. We look to connect this as a source while attempting to define and measure the impact of interest rate risk (IRR) on the economic value of equity (EVE) of banks. We propose to use the driver—driven method, wherein interest rate shocks are derived through prime lending rate (PLR) for the period of 2016–2019 in the context of India. Monte Carlo Simulation and OLS regression was performed to predict the IRR; Granger causality was used to examine the cause and effect relationship; the impulse response function (IRF) was used for sensitivity analysis; and the vector error correction model (VECM) technique was used for co-integrating relationships. Notably, the EVE movement caused due to shocks in interest rates had to be traced as it envisages probable EVE losses. Importantly, our study is among the first few to show the relationship between IRR and EVE of banks, especially after the deregulation of Indian banking sector.

scholarly journals Impact of Interest Rate Risk on Supply Chain Network under Bank Credit and Trade Credit Financing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Zhang ◽  
Zhiying Zhang ◽  
Yuehui Liu
Keyword(s):  
Supply Chain ◽  
Variational Inequality ◽  
Interest Rate ◽  
Trade Credit ◽  
Interest Rate Risk ◽  
Supply Chain Network ◽  
Rate Risk ◽  
Financial Flow ◽  
Credit Financing ◽  
The Impact

The purpose of this study is to propose a methodology that reflects the impact of interest rate risk on firms in supply chain network under bank financing and trade credit and further describe how trade credit improves the impact of interest rate risk on supply chain network through a financial flow equilibrium. A mean-variance framework and a network equilibrium analysis are integrated to provide a modeling framework. The model allows for the investigation of how bank credit financing (BCF) and trade credit financing (TCF) affect the payment strategy and financial flow of interconnected firms in supply chain networks and how they are affected by interest rate risks. The optimal behavior of manufacturers and retailers is described through variational inequality. We construct a supply chain network equilibrium model and derive qualitative properties of the solution and the function that becomes assimilated to the variational inequality problem. Additionally, variational inequality is solved using the modified projection method. This study extends the research on the impact of interest rate risk on the decision in supply chain network of firms. While other studies focus on the game between banks and firms, only a few authors have made attempts to examine the game between one manufacturer and one retailer in supply chain. An effective trade credit strategy is obtained by balancing cash and credit transactions. Through the case study, we learn how to balance the capital flow effectively to improve the negative impact of interest rate risk on supply chain.

scholarly journals Revisiting the impact of intrinsic financial risks on the firm value of banks in ASEAN-5 countries: a panel data approach

2020 ◽  
Vol 15 (2) ◽  
pp. 200-213
Author(s):  
Oluwaseyi Olalere ◽  
Md. Aminul Islam ◽  
Mohd Zukime Mat Junoh ◽  
Wan Sallha Yusoff ◽  
Mohammed Masum Iqbal
Keyword(s):  
Interest Rate ◽  
Credit Risk ◽  
Firm Value ◽  
Inflation Rate ◽  
Positive Impact ◽  
Interest Rate Risk ◽  
Financial Risks ◽  
Policy Makers ◽  
Rate Risk ◽  
The Impact

The paper aims to explore the impact of financial risks on the firm value of banks in ASEAN-5 countries. The study used the panel data regression model to analyze the available data for 63 commercial banks in ASEAN-5 countries from 2009 to 2017, totaling 567 observations. GMM dynamic estimation was also used for robustness and comparison purposes. The financial risk was measured using the non-performing loans ratio (NPL), the loan to deposit ratio (LD), the liquid asset ratio (LATA), the cost to income ratio (CIR), and the net interest margin (NIM), while firm value was measured using the enterprise value. The study used controlled variables proxied by size, GDP growth and the inflation rate, while the correlation between credit risk and interest rate risk (CR•IR) was also determined. Given the results of the study, credit risk proxy by non-performing loans ratio has a significant positive effect on the firm value, the liquidity risk (LD) has a significant positive impact on the firm value of ASEAN banks, while LATA has a significant negative effect on the firm value. Operational risk (CIR) and interest rate risk (NIM) have a significant negative impact on the firm value of ASEAN-5 banks. Bank size and inflation rate significantly and negatively affect the firm value, while GDP growth is found to have a significant positive impact on the firm value of ASEAN-5 banks. An insignificant interaction is found between credit risk and interest rate risk (CR•IR). The GMM estimation also supported these findings. The results obtained will be an important signal for policy makers, which is useful for the effective mobilization and allocation of credits to productive areas and helps manage inherent risks. The study provides implications for all countries regarding the financial risks associated with the value of the firm. Therefore, this study offers new insights into this relationship by providing useful information to the academics, policy makers, governments, and other stakeholders and serves as a benchmark for further study in this area.

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