ACCURATE OF VAR CALCULATED USING EMPIRICAL MODELS OF THE TERM STRUCTURE

2009 ◽  
Vol 12 (06) ◽  
pp. 811-832 ◽  
Author(s):  
PILAR ABAD ◽  
SONIA BENITO

This work compares the accuracy of different measures of Value at Risk (VaR) of fixed income portfolios calculated on the basis of different multi-factor empirical models of the term structure of interest rates (TSIR). There are three models included in the comparison: (1) regression models, (2) principal component models, and (3) parametric models. In addition, the cartography system used by Riskmetrics is included. Since calculation of a VaR estimate with any of these models requires the use of a volatility measurement, this work uses three types of measurements: exponential moving averages, equal weight moving averages, and GARCH models. Consequently, the comparison of the accuracy of VaR estimates has two dimensions: the multi-factor model and the volatility measurement. With respect to multi-factor models, the presented evidence indicates that the Riskmetrics model or cartography system is the most accurate model when VaR estimates are calculated at a 5% confidence level. On the contrary, at a 1% confidence level, the parametric model (Nelson and Siegel model) is the one that yields more accurate VaR estimates. With respect to the volatility measurements, the results indicate that, as a general rule, no measurement works systematically better than the rest. All the results obtained are independent of the time horizon for which VaR is calculated, i.e. either one or ten days.

Risks ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 124
Author(s):  
Yassmin Ali ◽  
Ming Fang ◽  
Pablo A. Arrutia Sota ◽  
Stephen Taylor ◽  
Xun Wang

We develop valuation and risk techniques for the future benefits of a retiree who participates in the American Social Security program based on their chosen date of retirement, the term structure of interest rates, and forecasted life expectancy. These valuation methods are then used to determine the optimal retirement time of a beneficiary given a specific wage history and health profile in the sense of maximizing the present value of cash flows received during retirement years. We then examine how a number of risk factors including interest rates, disease diagnosis, and mortality risks impact benefit value. Specifically, we utilize principal component analysis in order to assess both interest rate and mortality risk. We then conduct numerical studies to examine how such risks range over distinct income and demographic groups and finally summarize future research directions.


1976 ◽  
Vol 31 (4) ◽  
pp. 1035-1065 ◽  
Author(s):  
Steven W. Dobson ◽  
Richard C. Sutch ◽  
David E. Vanderford

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sebastian Schlütter

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.


1976 ◽  
Vol 31 (4) ◽  
pp. 1035 ◽  
Author(s):  
Steven W. Dobson ◽  
Richard C. Sutch ◽  
David E. Vanderford

Kybernetes ◽  
2017 ◽  
Vol 46 (4) ◽  
pp. 621-637
Author(s):  
Tanja Salamon ◽  
Borut Milfelner ◽  
Jernej Belak

Purpose Poor payment discipline has been a constant problem faced by European companies and has only deteriorated with the current global economic crisis. Even though new legislation has been adopted several times on the European level, the situation has not changed in favor of improved payment discipline. This research aims to determine the correlation between ethical culture of the company and how it influences its payments. Design/methodology/approach The factor structure of Kaptein’s (2008) instrument for measuring ethical culture was analyzed using principal component analysis with varimax rotation. This factor analysis yielded six factors with eigenvalues over 1.00. The reliabilities of the single constructs were as follows: clarity (α = 0.891), feasibility (α = 0.918), discussability (α = 0.955), supportability (α = 0.956), sanctionability (α = 0.879) and transparency (α = 0.801). These six factors explained 78 per cent of the total variance. All six factors were named according to Kaptein’s (2008) proposal, whose factor analysis yielded, in addition to the six factors, the following two factors: “Congruence of supervisors” and “Congruence of management”. Both factors represent the ethical culture dimension that Kaptein (1998) called “Congruence”, which refers to the extent to which superiors’ and managers’ acts are in line with their ethics on the declarative level. Findings The results showed that two dimensions of ethical culture, sanctionability and feasibility, improve payment discipline. Research limitations/implications The results of this study provide an important link between ethical culture and late payments. However, the research has some limitations. The first limitation is the response rate of only 9.1 per cent. The next limitation is geographical location; the results in other European countries could be different. The third limitation of the research arises from the data collection, because ethical culture was evaluated by one person from each enterprise, and the average payment delay was also calculated based only on a sample of invoices. Future research should therefore attempt to confirm the correlation between ethical culture and payment discipline in other European countries. It would be interesting to compare finds among different European countries, to determine whether there are major differences among companies in the field of payment discipline. Originality/value Good payment discipline can be defined as settling obligations to the customer on time. Late payments have been one of the biggest problems in many European economies. Trade credit becomes even more important during economic crises (Guariglia and Mateut, 2006), when investments are in decline, trading volume is reduced, bank credit is harder to obtain and interest rates are increased (Vojinović et al., 2013; Lin and Martin, 2010). Because customers do not fulfill their obligations on time, even enterprises with healthy sales growth encounter cash flow problems (Tsai, 2011). This paper’s empirical research has been implemented in Slovenia because it has some of the worst payment disciplines among European countries. Such research is unique in Slovenia as well as wider.


2015 ◽  
Vol 13 (4) ◽  
pp. 650
Author(s):  
Felipe Stona ◽  
Jean Amann ◽  
Maurício Delago Morais ◽  
Divanildo Triches ◽  
Igor Clemente Morais

This article aims to investigate the relationship between the term structure of interest rates and macroeconomic factors in selected countries of Latin America, such as Brazil, Chile and Mexico, between 2006 and 2014, on an autoregressive vector model. Specifically, we perform estimations of Nelson-Siegel, Diabold-Li and principal component analysis to test how the change of macroeconomic factors, e.g. inflation, production and unemployment levels affect the yield curves. For Brazil and Mexico, GDP and inflation variables are relevant to change the yield curves, with the former shifting more the level, and the latter with greater influence on the slope. For Chile, inflation had the greatest impact on the level and, specifically for Mexico, the unemployment variable also changed the slope of the yield curve.


2018 ◽  
Vol 8 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Pan Feng ◽  
Junhui Qian

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.


2017 ◽  
Vol 6 (1) ◽  
pp. 56 ◽  
Author(s):  
NI LUH NIKASARI ◽  
KOMANG DHARMAWAN ◽  
I GUSTI AYU MADE SRINADI

There are several methods that can be used to measure the risk of a portfolio of stocks. One of them is Average Value at Risk (AVaR). The purpose of this study is to implement Principal Component Analysis (PCA) to select stocks to be incorporated in the portfolio and also to compare the AVaR of the portfolio when  the stocks selected using PCA and selected using mean-variance method. The data we used are the closing price of the stocks BBCA, BDMN, ICBP, INTP, CPIN, KLBF, GGRM, MNCN, SMCB, and SGRO. The selected stocks using PCA are BBCA, CPIN, INTP and, MNCN with AVaR is 1.0971% for 90% confidence level and for 95% confidence level is 1.1432% whereas by using mean variance method, it is found that BDMN, GGRM, ICBP, and SMCB have to be incorporated in the portfolio with AVaR is 1.3314% for 90% confidence level and 1.4263% for 95% confidence level.


2020 ◽  
Vol 13 (4) ◽  
pp. 65
Author(s):  
Eduardo Mineo ◽  
Airlane Pereira Alencar ◽  
Marcelo Moura ◽  
Antonio Elias Fabris

The Nelson–Siegel framework published by Diebold and Li created an important benchmark and originated several works in the literature of forecasting the term structure of interest rates. However, these frameworks were built on the top of a parametric curve model that may lead to poor fitting for sensible term structure shapes affecting forecast results. We propose DCOBS with no-arbitrage restrictions, a dynamic constrained smoothing B-splines yield curve model. Even though DCOBS may provide more volatile forward curves than parametric models, they are still more accurate than those from Nelson–Siegel frameworks. DCOBS has been evaluated for ten years of US Daily Treasury Yield Curve Rates, and it is consistent with stylized facts of yield curves. DCOBS has great predictability power, especially in short and middle-term forecast, and has shown greater stability and lower root mean square errors than an Arbitrage-Free Nelson–Siegel model.


Sign in / Sign up

Export Citation Format

Share Document