Modeling the U.S. Short-Term Interest Rate by Mixture Autoregressive Processes


Book Description

A new kind of mixture autoregressive model with GARCH errors is introduced and applied to the U.S. short-term interest rate. According to the diagnostic tests developed in the article and further informal checks, the model is capable of capturing both of the typical characteristics of the short-term interest rate: volatility persistence and the dependence of volatility on the level of the interest rate. The model also allows for regime switches whose presence has been a third central result emerging from the recent empirical literature on the U.S. short-term interest rate. Realizations generated from the estimated model seem stable and their properties resemble those of the observed series closely. The drift and diffusion functions implied by the new model are in accordance with the results in much of the literature on continuous-time diffusion models for the short-term interest rate, and the term structure implications agree with historically observed patterns.







Estimating Parameters of Short-Term Real Interest Rate Models


Book Description

This paper sheds light on a narrow but crucial question in finance: What should be the parameters of a model of the short-term real interest rate? Although models for the nominal interest rate are well studied and estimated, dynamics of the real interest rate are rarely explored. Simple ad hoc processes for the short-term real interest rate are usually assumed as building blocks for more sophisticated models. In this paper, parameters of the real interest rate model are estimated in the broad class of single-factor interest rate diffusion processes on U.S. monthly data. It is shown that the elasticity of interest rate volatility—the relationship between the volatility of changes in the interest rate and its level—plays a crucial role in explaining real interest rate dynamics. The empirical estimates of the elasticity of the real interest rate volatility are found to be about 0.5, much lower than that of the nominal interest rate. These estimates show that the square root process, as in the Cox-Ingersoll-Ross model, provides a good characterization of the short-term real interest rate process.




An Elementary Introduction to Stochastic Interest Rate Modeling


Book Description

This textbook is written as an accessible introduction to interest rate modeling and related derivatives, which have become increasingly important subjects of interest in financial mathematics. The models considered range from standard short rate to forward rate models and include more advanced topics such as the BGM model and an approach to its calibration. An elementary treatment of the pricing of caps and swaptions under forward measures is also provided, with a focus on explicit calculations and a step-by-step introduction of concepts. Each chapter is accompanied with exercises and their complete solutions, making this book suitable for advanced undergraduate or beginning graduate-level students.




Estimating One-factor Models of Short-term Interest Rates


Book Description

Considers a wide range of several continuous-time one-factor models for short-term interest rates that are nested into one general model.




Comparison of the Short Term Interest Rate Models


Book Description

This article attempts to identify the best model of the short term interest rates that can predict its stochastic process over time. We studied nine different models of the short term interest rates. The choice of these models was the aim of analyzing the relevance of certain specifications of the the short term interest rate stochastic process, the effect of mean reversion and the sensitivity of the volatility to the level of interest rate.The yield on US three months treasury bills is used as a proxy for the short term interest rates. The parameters of the different stochastic process are estimated using the generalized method of moments. The results show that the effect of mean reversion is not statistically significant and that volatility is highly sensitive to the level of interest rates. To further study the performance prediction of the intertemporal behavior of the short term interest rate of the various models; we simulated their stochastic process for different periods.The results show that none of the studied models reproduce the actual path of the short term interest rates. The problem lies in the parametric specification of the mean and volatility of the diffusion process To further study the accurate parametric specification of the interest rate stochastic process we use a nonparametric estimation of the drift and the diffusion functions. The results prove that both should be nonlinear.




An Empirical Comparison of the Short Term Interest Rate Models


Book Description

This article attempts to identify the best model of the short term interest rates that can predict its stochastic process over time.We studied eight different models of interest rates in the short term. The choice of these models was the aim of analyzing the relevance of certain specifications of the stochastic process of the short term interest rates, the effect of mean reversion and the sensitivity of the volatility to the level of interest rate.The yield on three months treasury bills is used as a proxy for the short term interest rates. The parameters of the different stochastic process are estimated using the generalized method of moments. The results show that the effect of mean reversion is not statistically significant and that volatility is highly sensitive to the level of interest rates.To further study the performance prediction of the intertemporal behavior of the short term interest rate of the various models; we simulated their stochastic process for different periods.The results show that none of the studied models reproduce the actual path of the short term interest rates. The problem lies in the parametric specification of the mean and volatility of the diffusion process.




Another Look at Models of the Short-Term Interest Rate


Book Description

The short-term rate of interest is fundamental to much of theoretical and empirical finance. Yet no consensus has emerged on the dynamics of its volatility. We show that models which parameterize volatility only as a function of interest rate levels tend to over-emphasize the sensitivity of volatility to levels and fail to model adequately the serial correlation in conditional variances. On the other hand, serial correlation-based models like GARCH models fail to capture adequately the relationship between interest rate levels and volatility. We introduce and test a new class of models for the dynamics of short- term interest rate volatility which allows volatility to depend on both interest rate levels and information shocks. Two important conclusions emerge. First, the sensitivity of interest rate volatility to interest rate levels has been overstated in the literature. While this relationship is important, adequately modeling volatility as a function of unexpected information shocks is also important. Second, we conclude that the volatility processes in many existing theoretical models of interest rates are misspecified, and suggest new paths toward improving the theory.




Threshold Dynmamics of Short-Term Interest Rates


Book Description

This paper studies a nonlinear one-factor term structure model in discrete time. The single factor is the short-term interest rate, which is modeled as a self-exciting threshold autoregressive (SETAR) process. Our specification allows for shifts in the intercept and the variance. The process is stationary but mimics the nearly I(1) dynamics typically encountered with interest rates. In comparison with a linear model, we find empirical evidence in favor of the threshold model for Germany and the US. Based on the estimated short-rate dynamics we derive the implied arbitrage-free term structure of interest rates. Since analytical solutions are not feasible, bond prices are computed by means of Monte Carlo integration. The resulting term structure exhibits properties that are qualitatively similar to those observed in the data and which cannot be captured by the linear Gaussian one-factor model. In particular, our model captures the nonlinear relation between long rates and the short rate found in the data.




Time Series Analysis: Methods and Applications


Book Description

'Handbook of Statistics' is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with volume 30 dealing with time series.