Modelling and forecasting short-term interest rate volatility: A semiparametric approach

This paper employs a semiparametric procedure to estimate the diffusion process of short-term interest rates. The Monte Carlo study shows that the semiparametric approach produces more accurate volatility estimates than models that accommodate asymmetry, level effect and serial dependence in the conditional variance. Moreover, the semiparametric approach yields robust volatility estimates even if the short rate drift function and the underlying innovation distribution are misspecified. Empirical investigation with the U.S. three-month Treasury bill rates suggests that the semiparametric procedure produces superior in-sample and out-of-sample forecast of short rate changes volatility compared with the widely used single-factor diffusion models. This forecast improvement has implications for pricing interest rate derivatives.     

A statistical comparison of the short-term interest rate models for Japan,U.S., and Germany

We propose a simple and practical model selection method for continuous time models. We apply the method to several continuous time short-term interest rate models using discrete time series data of Japan, U.S. and Germany. All the models can be easily estimated from discrete observations, and their performances can be evaluated in a uniform statistical framework. The models that allow dependence of volatility on the level of interest rates tend to perform well empirically. The degree of volatility dependence on the interest rate levels seems to be different across the countries. For the German data, we observe that a model with nonlinear drift performs better than the best linear drift model.     

Regime shifts in interest rate volatility

I find evidence of regime shifts in interest rate volatility using short-rate data from the U.S., the U.K., Japan, and Canada. The regime shifts, if unaccounted for, could lead to spurious volatility persistence when the volatility processes are estimated with the stochastic volatility (SVOL) model. In contrast, the apparent persistence in volatility drops sharply in three out of the four countries when I estimate the volatility processes with the regime-switching stochastic volatility (RSSV) model. I also contribute to the literature by showing how to account for correlation in the regime-switching stochastic volatility model, which is important for modeling asymmetric volatility.     

Beta Regimes for the Yield Curve

We propose an affine term structure model which accommodatesnonlinearities in the drift and volatility function of the short-terminterest rate. Such nonlinearities are a consequence of discretebeta-distributed regime shifts constructed on multiple thresholds.We derive iterative closed-form formula for the whole yieldcurve dynamics that can be estimated using a linearized Kalmanfilter. Fitting the model on US data, we collect empirical evidenceof its potential in estimating conditional volatility and correlationacross yields.     

What drives U.S. financial sector volatility? A Bayesian model averaging perspective

We investigate the driving forces behind the quarterly stock price volatility of firms in the U.S. financial sector over the period from 1990 to 2017. The driving forces represent a set of 28 economic indicators that are routinely used to detect financial instability and crises and correspond to the development of the financial, monetary, real, trade and fiscal sector as well as to the development of the bond and equity markets. The dimensionality and model choice uncertainty are addressed using Bayesian model averaging, which led to the identification of only seven variables that tend to systematically drive the stock price volatility of financial firms in the U.S.: housing prices, short-term interest rates, net national savings, default yield spread, and three credit market variables. We also confirm that our results are not an artefact of volatility associated with market downturns (for negative semi-volatility), as the results are similar even when market volatility is associated with market upsurge (positive semi-volatility). Given the identified drivers, our results provide supporting empirical evidence that dampening credit cycles might lead to decreased volatility in the financial sector.     

The reaction of the Australian financial markets to the interest rate news from the Reserve Bank of Australia and the U.S. Fed

This paper provides comprehensive evidence on the impacts of the Reserve Bank of Australia's (RBA) and the U.S. Fed's target interest rate announcement news on the Australian financial markets over the period 1998–2006. The RBA's news had a significant impact on the first moments of market returns/changes in line with a priori expectations, and the conditional volatility in most of the markets was significantly higher following the news. Asymmetric news effect is also observed for the Australian interest rates where markets tended to respond more strongly to unexpected rate rises than rate falls. While the U.S. Fed's news influenced only the USD/AUD exchange rate, the Australian market volatility was significantly lower in all market segments following the Fed's news.     

Asymmetric Mean Reversion in European Interest Rates: A Two-factor Model

Abstract

This paper tests for asymmetric mean reversion in European short-term interest rates using a combination of the interest rate models introduced by Longstaff and Schwartz (Longstaff, F.A., Schwarts, E.S. (1992) Interest rate volatility and the ferm structure: A two factor general equilibrium model, Journal of Finance, 48, pp. 1259–1282.) and Bali (Bali, T. (2000) Testing the empirical performance of stochastic volatility models of the short-term interest rates, Journal of Financial and Quantitative Analysis, 35, pp. 191–215.). Using weekly rates for France, Germany and the United Kingdom, it is found that short-term rates follow in all instances asymmetric mean reverting processes. Specifically, interest rates exhibit non-stationary behavior following rate increases, but they are strongly mean reverting following rate decreases. The mean reverting component is statistically and economically stronger thus offsetting non-stationarity. Volatility depends on past innovations past volatility and the level of interest rates. With respect to past innovations volatility is asymmetric rising more in response to positive innovations. This is exactly opposite to the asymmetry found in stock returns.     

Short-run money demand

The conventional wisdom holds that the short-run demand for money is unstable. This paper challenges the conventional view by finding a stable demand for M1 in U.S. data from 1959 through 1993. The approach follows previous work in interpreting long-run money demand as a cointegrating relation, and it uses Goldfeld's partial-adjustment model to interpret short-run dynamics. The key innovation is the choice of the interest rate in the money demand function. Most previous work uses a short-term market rate, but this paper uses the average return on “near monies”—the savings accounts and money market mutual funds that are close substitutes for M1. This choice helps rationalize the behavior of money demand; in particular, the increase in the volatility of velocity after 1980 is explained by increased volatility in the returns on near monies.     

Likelihood-based specification analysis of continuous-time models of the short-term interest rate

An extensive collection of continuous-time models of the short-term interest rate is evaluated over data sets that have appeared previously in the literature. The analysis, which uses the simulated maximum likelihood procedure proposed by Durham and Gallant (2002), provides new insights regarding several previously unresolved questions. For single factor models, I find that the volatility, not the drift, is the critical component in model specification. Allowing for additional flexibility beyond a constant term in the drift provides negligible benefit. While constant drift would appear to imply that the short rate is nonstationary, in fact, stationarity is volatility-induced. The simple constant elasticity of volatility model fits weekly observations of the three-month Treasury bill rate remarkably well but is easily rejected when compared with more flexible volatility specifications over daily data. The methodology of Durham and Gallant can also be used to estimate stochastic volatility models. While adding the latent volatility component provides a large improvement in the likelihood for the physical process, it does little to improve bond-pricing performance.     

Fundamental Properties of Bond Prices in Models of the Short-Term Rate

This article develops restrictions that arbitrage-constrainedbond prices impose on the short-term rate process in order tobe consistent with given dynamic properties of the term structureof interest rates. The central focus is the relationship betweenbond prices and the short-term rate volatility. In both scalarand multidimensional diffusion settings, typical relationshipsbetween bond prices and volatility are generated by joint restrictionson the risk-neutralized drift functions of the state variablesand convexity of bond prices with respect to the short-termrate. The theory is illustrated by several examples and is partiallyextended to accommodate the occurrence of jumps and default.