scholarly journals Interest rate risk of German financial institutions: the impact of level, slope, and curvature of the term structure

2009 ◽  
Vol 33 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Marc-Gregor Czaja ◽  
Hendrik Scholz ◽  
Marco Wilkens
Keyword(s):  
Interest Rate ◽  
Term Structure ◽  
Financial Institutions ◽  
Interest Rate Risk ◽  
Rate Risk ◽  
The Impact

scholarly journals Interest Rate Sensitivity of Non-banking Financial Sector in India

2018 ◽  
Vol 43 (3) ◽  
pp. 152-170
Author(s):  
Renu Ghosh ◽  
K. Latha ◽  
Sunita Gupta
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Stock Returns ◽  
Financial Institutions ◽  
Rate Sensitivity ◽  
Interest Rate Risk ◽  
Cross Sectional ◽  
Rate Risk ◽  
The Interest Rate ◽  
The Impact

Executive Summary Before financial liberalization, interest rates were administered and exhibited near-zero volatility. The easing of financial repression in the 1990s generated experiences with interest rate volatility in India. Administrative restrictions on interest rates in India have been steadily eased since 1993. This has led to increased interest rate risk for financial firms. Most research studies have almost exclusively focused on the developed countries especially the banking sector of the United States. The present study attempts to examine the interest rate risk of non-banking financial institutions in India by using the methodology of panel regression and generalized autoregressive conditional heteroscedasticity (GARCH) (1, 1) model for the period from 1 April 1996 to 30 August 2014. The sample used in the study consists of all non-banking financial companies (NBFCs) listed in the S&P CNX 500 index which has continuous availability of share prices over the study period. The study also examines the impact of unanticipated changes in interest rate on stock returns of NBFCs. The Box–Jenkins methodology is applied to calculate unanticipated changes in interest rate variable, autoregressive integrated moving average (ARIMA) (24, 1, 0) model. The time series used in the present study is found to be stationary at the first logarithmic difference. Stock returns exhibit significant exposure with both market returns and interest rate changes. The interest rate sensitivity of large, medium, and small financial institutions is also found to be different. Estimation results for the variance equation in GARCH (1, 1) model suggest that the volatility for individual firm stock returns is time-variant. The ARCH and GARCH coefficients are found to be significant, providing evidence against using traditional model (ordinary least square (OLS)) that assumes time-invariant volatility. This implies that the market has a memory longer than one period and volatility is more sensitive to its own lagged values than it is to new surprises in the market. This study also investigates the possible determinants that account for cross-sectional variation in the interest rate sensitivity of NBFCs. It is found that the size of the firm is the preferred determinant that accounts for cross-sectional variation in the interest rate sensitivity of finance companies. When unanticipated changes in interest rate are used in lieu of actual interest rate changes, not much difference is observed in the significance coefficients. The only significant difference observed is in the magnitude. The impact of actual interest rate changes is more than the impact of unanticipated interest rate changes in absolute terms. This difference in the magnitude of impact arises because actual data incorporate movement in both anticipated and unanticipated components of interest rate. Hence, NBFCs managers and regulators should adopt policies and strategies to avoid the transmission of interest rate risk in their stock returns.

scholarly journals Measuring Interest Rate Risk with Embedded Option Using HPL-MC Method in Fuzzy and Stochastic Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Enlin Tang ◽  
Wei Du
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Term Structure ◽  
Interest Rate Risk ◽  
Extended Model ◽  
Term Structure Model ◽  
Continuous Innovation ◽  
Liability Management ◽  
Rate Risk ◽  
Embedded Options

Under the condition of continuous innovation of financial derivatives and marketization of interest rate, interest rates fluctuate more frequently and fiercely, and the measurement of interest rate risk also attracts more attention. Under the premise that the fluctuation of interest rate follows fuzzy stochastic process, based on the option characteristics of financial instruments with embedded option, this paper takes effective duration and effective convexity as tools to measure interest rate risk when embedded options exist, tries to choose CIR extended model as term structure model, and uses the Monte Carlo method for hybrid low deviation sequences (HPL-MC) to analyze the prepayment characteristics of MBS, a representative financial instrument with embedded options, when interest rates fluctuate; on this basis, the effectiveness of effective duration management of interest rate risk is demonstrated with asset liability management cases of commercial banks.

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.

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