We examine various dynamic term structure models for monthly US Treasury yields from 1964 to 2001. Of particular interest is the predictability of bond excess returns. Recent evidence indicates that using multiple forward rates can sharply predict future excess returns on bonds; the R2 of this predictability regression can be as high as 30%. In addition, the projection coefficients in these predictability regressions exhibit a tent shaped pattern that relates to the maturity of the forward rate. This dimension of the data in conjunction with the transition dynamics of bond yields (i.e., conditional volatility and cross-correlation of bond yields) poses an serious challenge to term structure models. In this paper we present and estimate a regime-shifts term structure model - our findings show that this model can account for all aspects of the predictability regression and the transition dynamics of yields. Alternative models, such as, affine factor models cannot account for these features of the data. We find that the regimes in the model are related to the NBER business cycle indicator.

This paper develops and estimates a general equilibrium model for the term structures of nominal and real interest rates that incorporates regime-switching into the dynamics of the state variables. The model generates time-varying risk premia via changes in the covariance structure of the state variables and Peso problems through regime-switching. When the model is estimated using real and nominal yields from the U.K., I find that Peso problems emanating from instability in inflation have a significant impact on the nominal term structure. Peso problems affect (i) the sample predictability of excess returns, (ii) nominal term premia, and (iii) the inflation risk premia linking real and nominal yields with expected inflation.

The consumption growth data strongly favor a two-regime specification. The high volatility, low growth regime is associated with deep recessions: the Great Depression, the recession of 1937-1938, the post-war recession of 1945, and the most recent financial crisis. I develop parsimonious models in which (i) consumption and dividend growth follow regime-switching dynamics, (ii) the regime characteristics are consistent with the empirical evidence from the consumption growth data, and (iii) the risks associated with regime shifts are priced in asset markets. The models explain major regime-dependent asset market phenomena. Regime-shift risk exhibits the dominant influence on asset prices: It generates a high equity premium, and also induces time-varying risk premiums and explains the return predictability.

Since the groundbreaking research of Harry Markowitz into the application of operations research to the optimization of investment portfolios, finance has been one of the most important areas of application of operations research. The use of hidden Markov models (HMMs) has become one of the hottest areas of research for such applications to finance. This handbook offers systemic applications of different methodologies that have been used for decision making solutions to the financial problems of global markets. As the follow-up to the authors’ Hidden Markov Models in Finance (2007), this offers the latest research developments and applications of HMMs to finance and other related fields. Amongst the fields of quantitative finance and actuarial science that will be covered are: interest rate theory, fixed-income instruments, currency market, annuity and insurance policies with option-embedded features, investment strategies, commodity markets, energy, high-frequency trading, credit risk, numerical algorithms, financial econometrics and operational risk. Hidden Markov Models in Finance: Further Developments and Applications, Volume II presents recent applications and case studies in finance and showcases the formulation of emerging potential applications of new research over the book’s 11 chapters. This will benefit not only researchers in financial modeling, but also others in fields such as engineering, the physical sciences and social sciences. Ultimately the handbook should prove to be a valuable resource to dynamic researchers interested in taking full advantage of the power and versatility of HMMs in accurately and efficiently capturing many of the processes in the financial market.