Developments In Macro Finance Yield Curve Modelling

Changes in the shape of the yield curve have traditionally been one of the key macroeconomic indicators of a likely change in economic outlook. However, the recent financial crises have created a challenge to the management of monetary policy, demanding a revision in the way that policymakers model expected changes in the economy. This volume brings together central bank economists and leading academic monetary economists to propose new methods for modelling the behaviour of interest rates. Topics covered include: the analysis and extraction of expectations of future monetary policy and inflation; the analysis of the short-term dynamics of money market interest rates; the reliability of existing models in periods of extreme market volatility and how to adjust them accordingly; and the role of government debt and deficits in affecting sovereign bond yields and spreads. This book will interest financial researchers and practitioners as well as academic and central bank economists.

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. In this book, Francis Diebold and Glenn Rudebusch propose two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson-Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). Diebold and Rudebusch show how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, Yield Curve Modeling and Forecasting contains essential tools with enhanced utility for academics, central banks, governments, and industry.

State-of-the-art research from academics and policymakers on the role of and challenges to monetary policy during the ongoing financial crisis.

Macroeconomic concepts such as in ation and real economic activity are not directly observed. Researchers often use factor models in order to measure these unobserved concepts. The underlying view is that a small number of factors exist which represent the concept and drive many related variables. Consequently, the U.S. economy is often modeled as an a ne function of some factors. If indeed there is such a factor structure for the U.S. economy, then it can be represented by a generalized dynamic factor model (GDFM). In the rst chapter, I describe and summarize the literature on GDFMs. In the second chapter, I investigate the interactions and mutually independent dynamics of changes in in ation and real growth by applying the GDFM to a block of real growth variables, a block of in ation variables, and to their joint panel. In this manner, an empirical decomposition of the U.S. economy is obtained and this allows the reconcilitaion of forward and backward looking Phillips curves. In the third chapter, I build and study a discrete time generalized dynamic a ne term structure model. This is characterized by three main features that are conceptually important for a ne yield curve models. I allow: (a) for state vector dynamics beyond Markovian types; (b) that all yields may contain an idiosyncratic component to re ect measurement-errors in the data; and (c) that idiosyncratic components may be crosssectional as well as time-serial correlated. It is possible to directly compare this model with the version that is restricted by Du e-Kan's no-arbitrage conditions. Chapter four addresses whether or not changes in yields can be explained by changes to the latent dynamic factors which underlie the macroeconomic concepts of in ation and real growth. As such, I contribute to the debate about whether or not monetary policy should react to real activity measures.

We extend the class of dynamic factor yield curve models for the inclusion of macro-economic factors. We benefit from recent developments in the dynamic factor literature for extracting the common factors from a large panel of macroeconomic series and for estimating the parameters in the model. We include these factors into a dynamic factor model for the yield curve, in which we model the salient structure of the yield curve by imposing smoothness restrictions on the yield factor loadings via cubic spline functions. We carry out a likelihood-based analysis in which we jointly consider a factor model for the yield curve, a factor model for the macroeconomic series, and their dynamic interactions with the latent dynamic factors. We illustrate the methodology by forecasting the U.S. term structure of interest rates. For this empirical study we use a monthly time series panel of unsmoothed Fama-Bliss zero yields for treasuries of different maturities between 1970 and 2009, which we combine with a macro panel of 110 series over the same sample period. We show that the relation between the macroeconomic factors and yield curve data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Finally, we perform an extensive out-of-sample forecasting study. Our main conclusion is that macroeconomic variables can lead to more accurate yield curve forecasts.

Macro-finance modelling is an increasingly popular topic. Various approaches have been developing rapidly, usually using econometric techniques. This book focuses on structural approach to an analysis of average yield curve and its dynamics using macroeconomic factors. An underlying model is based on basic Dynamic Stochastic General Equilibrium (DSGE) approach. Log-linearized solution of the model is the key for derivation of yield curve and its main determinants - pricing kernel, price of risk and affine term structure of interest rates - based on no-arbitrage assumption. The book presents a consistent derivation of a structural macro-finance model, with a reasonable computational burden that allows for time varying term premia. A simple VAR model, widely used in macro-finance literature, serves as a benchmark. The two models are briefly compared and analysis shows their ability to fit an average yield curve observed from the data. It also presents a possible importance of this issue for monetary and fiscal institutions. The book should help shed some light on the use of DSGE framework within macro-finance modelling and should be useful for students and researchers in this field.