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We examine several discrete-time versions of the Cox, Ingersoll and Ross (CIR) model for the term structure, in which the short rate is subject to discrete shifts. Our empirical analysis suggests that careful consideration of which parameters of the short-term interest rate equation that are allowed to be switched is crucial. Ignoring this issue may result in a parameterization that produces no improvement (in terms of bond pricing) relative to the standard CIR model, even when there are clear breaks in the data.

The returns of many financial assets show significant skewness, but in the literature this issue is only marginally dealt with. Our conjecture is that this distributional asymmetry may be due to two different dynamics in positive and negative returns.In this paper we propose a process that allows the simultaneous modelling of skewed conditional returns and different dynamics in their conditional second moments. The main stochastic properties of the model are analyzed and necessary and sufficient conditions for weak and strict stationarity are derived.An application to the daily returns on the principal index of the London Stock Exchange supports our model when compared to other frequently used GARCH-type models, which are nested into ours.

This paper examines, numerically, the impact of a negative exogenous shock to marginal productivity (such as ecological government regulation that becomes effective at some point in time) in an endogenous finite time growth model with sluggish reallocation of human capital. The policy can be anticipated or unanticipated by the economic agents, and it can also be announced but not implemented. It turns out that these frictions have very strong long-run effects on consumption and output, and on the optimal allocation of capital and labor in particular. The qualitative properties are closely related to those found in homogenous labor models with positive productivity shocks. The numerical optimization method employed here proved very successful in qualitatively similar problems in engineering but has not yet found its way into macroeconomic models of growth.

This article proposes a general regime-switching univariate diffusion model to describe the dynamics of the short-term interest rate. The maximum likelihood estimates are obtained using the weekly series of U.S. three-month treasury bill rates. The estimation results reveal that there are strong evidences for the existence of high and low volatility regimes, for the time varying transition probability of the regime variable, and for the high persistence of both regimes. In both regimes, the volatility, but not the drift, is estimated accurately and plays a key role in explaining the dynamics of the interest rates. High persistence's and different volatilities of two regimes can well explain volatility clustering observed in the data. Based on the inferred probability of the process being in each regime, most of the high volatility periods correspond to some historic events. The likelihood-based test shows that misspecification can result in misleading outcomes particularly regarding the volatility and transition probabilities of the regime index.

We consider a multivariate dynamic model for the joint distribution of binary outcomes associated with directions-of-change for several markets or assets. The marginal distribution of each binary outcome follows a dynamic binary choice model, while the association structure is parameterized via possibly time varying dependence ratios. We illustrate the technique using daily stock index returns from three European markets, from three Baltic markets, and from two Chinese exchanges.