Estimating Term Structure of Interest Rates: Neural Network Vs one Factor Parametric Models

2002 ◽  
Author(s):  
Fathi Abid ◽  
Mona Ben Salah
Keyword(s):  
Neural Network ◽  
Interest Rates ◽  
Term Structure ◽  
Parametric Models

ACCURATE OF VAR CALCULATED USING EMPIRICAL MODELS OF THE TERM STRUCTURE

2009 ◽  
Vol 12 (06) ◽  
pp. 811-832 ◽  
Author(s):  
PILAR ABAD ◽  
SONIA BENITO
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Confidence Level ◽  
Value At Risk ◽  
Principal Component ◽  
Empirical Models ◽  
Two Dimensions ◽  
Parametric Models ◽  
Equal Weight ◽  
Moving Averages

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.

scholarly journals Forecasting the Term Structure of Interest Rates with Dynamic Constrained Smoothing B-Splines

2020 ◽  
Vol 13 (4) ◽  
pp. 65
Author(s):  
Eduardo Mineo ◽  
Airlane Pereira Alencar ◽  
Marcelo Moura ◽  
Antonio Elias Fabris
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Yield Curve ◽  
Parametric Models ◽  
Parametric Curve ◽  
Middle Term ◽  
No Arbitrage ◽  
B Splines ◽  
Curve Model ◽  
Mean Square Errors

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.

Stability of Models for the Term Structure of Interest Rates with Application to German Market Data

2001 ◽  
Vol 7 (3) ◽  
pp. 467-507 ◽  
Author(s):  
A.J.G. Cairns ◽  
D.J. Pritchard
Keyword(s):  
Interest Rates ◽  
Term Structure ◽  
Yield Curve ◽  
Optimal Solution ◽  
Exponential Model ◽  
Parametric Models ◽  
Bond Markets ◽  
German Market ◽  
Market Data ◽  
Coupon Bond

ABSTRACTThis paper discusses the use of parametric models for description of the term structure of interest rates and their uses. We extend earlier work of Cairns (1998), Chaplin (1998) and Feldman et al. (1998), by presenting new theoretical results and also by demonstrating that the same model can be applied to countries other than the United Kingdom. First, we prove that the process of fitting a yield curve to price data has a unique optimal solution in both zero-coupon-bond and low-coupon-bond markets. Furthermore, an alternative method of curve fitting to those proposed previously is shown to have a unique solution in all markets.The restricted-exponential model has previously been applied to U.K. data (Cairns, 1998). Here, we consider its wider application in European bond markets. In particular, we analyse German market data and conclude that the same model applies equally well to both countries.

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