Scenario-based measurement of interest rate risks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sebastian Schlütter
Keyword(s):  
At Risk ◽  
Interest Rate ◽  
Stochastic Model ◽  
Interest Rates ◽  
Lower Bounds ◽  
Value At Risk ◽  
Yield Curve ◽  
Principal Component ◽  
Insurance Companies ◽  
Content Type

PurposeThis paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.Design/methodology/approachThe authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.FindingsThe backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.Research limitations/implicationsThe paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.Practical implicationsThe authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.Originality/valueThe authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Analyzing and forecasting the Chinese term structure of interest rates using functional principal component analysis

2018 ◽  
Vol 8 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Pan Feng ◽  
Junhui Qian
Keyword(s):  
Principal Component Analysis ◽  
Interest Rates ◽  
Term Structure ◽  
Yield Curve ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Content Type ◽  
Functional Principal Component ◽  
Out Of Sample ◽  
Chinese Term

Purpose The purpose of this paper is to analyze and forecast the Chinese term structure of interest rates using functional principal component analysis (FPCA). Design/methodology/approach The authors propose an FPCA-K model using FPCA. The forecasting of the yield curve is based on modeling functional principal component (FPC) scores as standard scalar time series models. The authors evaluate the out-of-sample forecast performance using the root mean square and mean absolute errors. Findings Monthly yield data from January 2002 to December 2016 are used in this paper. The authors find that in the full sample, the first two FPCs account for 98.68 percent of the total variation in the yield curve. The authors then construct an FPCA-K model using the leading principal components. The authors find that the FPCA-K model compares favorably with the functional signal plus noise model, the dynamic Nelson-Siegel models and the random walk model in the out-of-sample forecasting. Practical implications The authors propose a functional approach to analyzing and forecasting the yield curve, which effectively utilizes the smoothness assumption and conveniently addresses the missing-data issue. Originality/value To the best knowledge, the authors are the first to use FPCA in the modeling and forecasting of yield curves.

scholarly journals The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 84 ◽  
Author(s):  
Holger Fink ◽  
Andreas Fuest ◽  
Henry Port
Keyword(s):  
At Risk ◽  
Exchange Rate ◽  
Value At Risk ◽  
Yield Curve ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Functional Principal Component ◽  
Forecasting Performance ◽  
Yield Difference ◽  
The Impact

A functional ARMA-GARCH model for predicting the value-at-risk of the EURUSD exchange rate is introduced. The model implements the yield curve differentials between EUR and the US as exogenous factors. Functional principal component analysis allows us to use the information of basically the whole yield curve in a parsimonious way for exchange rate risk prediction. The data analyzed in our empirical study consist of the EURUSD exchange rate and the EUR- and US-yield curves from 15 August 2005–30 September 2016. As a benchmark, we take an ARMA-GARCH and an ARMAX-GARCHX with the 2y-yield difference as the exogenous variable and compare the forecasting performance via likelihood ratio tests. However, while our model performs better in one situation, it does not seem to improve the performance in other setups compared to its competitors.

Determinants of credit spreads in hotel CMBS loans

2019 ◽  
Vol 31 (1) ◽  
pp. 370-388
Author(s):  
Amrik Singh
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Multiple Regression ◽  
Yield Curve ◽  
Sample Selection ◽  
Yield Ratio ◽  
Credit Spreads ◽  
Future Research ◽  
Content Type ◽  
Coverage Ratio

Purpose This study aims to investigate the determinants of credit spreads in hotel loans securitized into commercial mortgage-backed securities (CMBS) between 2010 and 2015. Design/methodology/approach The sample represents 1,579 US hotel fixed interest rate whole loans with an aggregate mortgage value of $26.6bn at loan origination. The relationship between credit spreads and property, loan and market characteristic is examined via multiple regression analysis. Additionally, the method of 2-stage least squares is used to control for endogeneity bias and identify the effect of the loan-to-value (LTV) ratio on credit spreads. Findings The multiple regression models explain 80 per cent of the variation in credit spreads and show a significant association of credit spreads with hotel and loan characteristics and market conditions. The findings indicate the debt coverage ratio to be the most important predictor of credit spreads followed by the loan maturity term, implied capitalization rate, LTV and yield curve. The results show the debt yield premium to be a stronger predictor of credit spreads than the debt yield ratio. The spread between the debt yield ratio and mortgage interest rate could be used in future research as an instrumental variable to identify the effect of the LTV on credit spreads. Research limitations/implications This study is limited to the CMBS market and the period after the financial crisis. Additional limitations include sample selection bias, exclusion of multi-property loans and variable interest rate loans. Practical implications Interest rate increases in an expanding economy would likely increase the cost of borrowing for hotel owners leading to higher debt service payments and lower profitability. If an increase in interest rates is offset by a decline in credit spreads, hotel owners will still benefit from the ensuing stability in borrowing interest rates. The evidence also suggests that CMBS lenders favor select service and extended stay hotels. Owners and operators of these efficient and profitable hotels will likely obtain loans with lower credit spreads given their lower risk of default. Originality/value The current study provides evidence on the effects of loan and property characteristics in the pricing of loan risk and serves to inform CMBS market participants about the factors that drive credit spreads in hotel mortgage loans.

scholarly journals Principal component analysis in negative interest rate environment

2019 ◽  
Vol 69 (1) ◽  
pp. 101-125
Author(s):  
Milan Lazarević
Keyword(s):  
Principal Component Analysis ◽  
Interest Rate ◽  
Interest Rates ◽  
Yield Curve ◽  
Principal Component ◽  
Component Analysis ◽  
Interest Rate Risk ◽  
Management Technique ◽  
Market Conditions ◽  
Negative Interest Rates

Principal Component Analysis (PCA) is a risk management technique which is, due to the consequences of multicollinearity, particularly suitable to describe the yield curve. Its final results in this segment are presented through three main factors: shift, slope and curvature. They express predictive trajectories and explain over 95% of variability under normal market conditions. The main goal of this paper is to assess whether the established behavioural patterns are observable in the presence of negative interest rates. The EU bond market was used as an empirical basis with respect to the reactions of the European Central Bank and the establishment of negative reference interest rates in the assessed period. The algebraic properties of the principal components in the presence of negative interest rates correspond to the determined directions of movement, except that the slope and curvature have different signs given their diametrically opposite trends. The percentage of variability explained with the help of PCA is lower compared to the normal market conditions and if an equivalent level of approximation is required, it is necessary to include a fourth factor in PCA. This factor is, due to its properties, aptly named oscillatority. An implicit conclusion of our research is that the duration in the conditions of negative interest rates has less useful power in managing the interest rate risk of individual instruments.

How to derive optimal guarantee levels in participating life insurance contracts

2019 ◽  
Vol 20 (5) ◽  
pp. 445-469 ◽  
Author(s):  
Alexander Braun ◽  
Marius Fischer ◽  
Hato Schmeiser
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Life Insurance ◽  
Asset Allocation ◽  
Equity Capital ◽  
Insurance Companies ◽  
Insurance Company ◽  
Content Type ◽  
Contract Duration ◽  
The Interest Rate

Purpose The purpose of this paper is to show how an insurance company can maximize the policyholder’s utility by setting the level of the interest rate guarantee in line with his preferences. Design/methodology/approach The authors develop a general model of life insurance, taking stochastic interest rates, early default and regular premium payments into account. Furthermore, the authors assume that equity holders must receive risk-adequate returns on their initial equity contribution and that the insurance company has to maintain a solvency restriction. Findings The findings show that the optimal level for the interest rate guarantee is in general far below the maximum value typically set by the supervisory authorities and insurance companies. Originality/value The authors conclude that the approach of deviating from the maximum interest rate guarantee level given by the regulatory requirements can create additional value for the rational policyholder. In contrast to Schmeiser and Wagner (2014), the second finding shows that the interest rate guarantee embedded in a life insurance product becomes less attractive compared to a pure investment in the underlying asset portfolio to the policyholder when the guarantee level is lowered too far or the contract duration is short. They also refute Schmeiser and Wagner (2014) by showing that the equity capital required by the insurance company increases with the level of the guarantee, even if the insurer is flexible with respect to its asset allocation. The last finding is that a policyholder with higher risk aversion does not generally prefer a higher guarantee level.

scholarly journals Immunization Strategies for Funding Multiple Inflation-Linked Retirement Income Benefits

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Cláudia Simões ◽  
Luís Oliveira ◽  
Jorge M. Bravo
Keyword(s):  
Interest Rate ◽  
Yield Curve ◽  
Risk Measure ◽  
Management Strategies ◽  
Interest Rate Risk ◽  
Insurance Companies ◽  
Retirement Income ◽  
Investment Strategies ◽  
Immunization Strategies ◽  
The Impact

Protecting against unexpected yield curve, inflation, and longevity shifts are some of the most critical issues institutional and private investors must solve when managing post-retirement income benefits. This paper empirically investigates the performance of alternative immunization strategies for funding targeted multiple liabilities that are fixed in timing but random in size (inflation-linked), i.e., that change stochastically according to consumer price or wage level indexes. The immunization procedure is based on a targeted minimax strategy considering the M-Absolute as the interest rate risk measure. We investigate to what extent the inflation-hedging properties of ILBs in asset liability management strategies targeted to immunize multiple liabilities of random size are superior to that of nominal bonds. We use two alternative datasets comprising daily closing prices for U.S. Treasuries and U.S. inflation-linked bonds from 2000 to 2018. The immunization performance is tested over 3-year and 5-year investment horizons, uses real and not simulated bond data and takes into consideration the impact of transaction costs in the performance of immunization strategies and in the selection of optimal investment strategies. The results show that the multiple liability immunization strategy using inflation-linked bonds outperforms the equivalent strategy using nominal bonds and is robust even in a nearly zero interest rate scenario. These results have important implications in the design and structuring of ALM liability-driven investment strategies, particularly for retirement income providers such as pension schemes or life insurance companies.

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