We propose a canonical representation for affine term structure models where the state vector is comprised of the first few Taylor-series components of the yield curve and their quadratic (co-)variations. With this representation: (i) the state variables have simple physical interpretations such as level, slope and curvature, (ii) their dynamics remain affine and tractable, (iii) the model is by construction 'maximal' (i.e., it is the most general model that is econometrically identifiable), and (iv) model-insensitive estimates of the state vector process implied from the term structure are readily available. (Furthermore, this representation may be useful for identifying the state variables in a squared-Gaussian framework where typically there is no one-to-one mapping between observable yields and latent state variables). We find that the 'unrestricted' A1(3) model of Dai and Singleton (2000) estimated by 'inverting' the yield curve for the state variables generates volatility estimates that are negatively correlated with the time series of volatility estimated using a standard GARCH approach. This occurs because the 'unrestricted' A1(3) model imposes the restriction that the volatility state variable is simultaneously a linear combination of yields (i.e., it impacts the cross-section of yields), and the quadratic variation of the spot rate process (i.e., it impacts the time-series of yields). We then investigate the A1(3) model which exhibits 'unspanned stochastic volatility' (USV). This model predicts that the cross section of bond prices is independent of the volatility state variable, and hence breaks the tension between the time-series and cross-sectional features of the term structure inherent in the unrestricted model. We find that explicitly imposing the USV constraint on affine models significantly improves the volatility estimates, while maintaining a good fit cross-sectionally.

Building on the approach of Duffie and Kan (1996) who use finite maturity yields as the state vector, we propose a new representation of affine models in which the state vector is composed of infinitesimal maturity yields and their quadratic covariations. Because these variables possess unambiguous economic interpretations, they generate a representation that is globally identifiable. Further, this representation is more flexible than the maximal model of Dai and Singleton (2000) in that there are more identifiable parameters. We implement this new representation for two different three-factor models. The fact that our state vector can be estimated model-independently from yield curve data presents advantages for the estimation and interpretation of multi-factor models.

This paper develops new results for identification and estimation of Gaussian affine term structure models. We establish that three popular canonical representations are unidentified, and demonstrate how unidentified regions can complicate numerical optimization. A separate contribution of the paper is the proposal of minimum-chi-square estimation as an alternative to MLE. We show that, although it is asymptotically equivalent to MLE, it can be much easier to compute. In some cases, MCSE allows researchers to recognize with certainty whether a given estimate represents a global maximum of the likelihood function and makes feasible the computation of small-sample standard errors.

We develop new procedures for maximum likelihood estimation of affine term structure models with spanned or unspanned stochastic volatility. Our approach uses linear regression to reduce the dimension of the numerical optimization problem yet it produces the same estimator as maximizing the likelihood. It improves the numerical behavior of estimation by eliminating parameters from the objective function that cause problems for conventional methods. We find that spanned models capture the cross-section of yields well but not volatility while unspanned models fit volatility at the expense of fitting the cross-section.

We show how to estimate affine term structure models from a panel of noisy bond yields using simulated maximum likelihood based on importance sampling. We approximate the likelihood function of the state-space representation of the model by correcting the likelihood function of a Gaussian first-order approximation for the non-normalities introduced by the affine factor dynamics. Depending on the accuracy of the correction, which is computed through simulations, the quality of the estimator ranges from quasi-maximum likelihood (no correction) to exact maximum likelihood as the simulation size grows.

Applied financial econometrics subjects are featured in this second volume, with papers that survey important research even as they make unique empirical contributions to the literature. These subjects are familiar: portfolio choice, trading volume, the risk-return tradeoff, option pricing, bond yields, and the management, supervision, and measurement of extreme and infrequent risks. Yet their treatments are exceptional, drawing on current data and evidence to reflect recent events and scholarship. A landmark in its coverage, this volume should propel financial econometric research for years. Presents a broad survey of current research Contributors are leading econometricians Offers a clarity of method and explanation unavailable in other financial econometrics collections

Held at Oakland University, School of Business Administration, Department of Accounting and Finance. This book provides a summary of state-of-the-art methods and research in the analysis of credit. As such, it offers very useful insights into this vital area of finance, which has too often been under-researched and little-taught in academia. Including an overview of processes that are utilized for estimating the probability of default and the loss given default for a wide array of debts, the book will also be useful in evaluating individual loans and bonds, as well as managing entire portfolios of such assets. Each chapter is written by authors who presented and discussed their contemporary research and knowledge at the Third International Conference on Credit Analysis and Risk Management, held on August 21–22, 2014 at the Department of Accounting and Finance, School of Business administration, Oakland University. This collection of writings by these experts in the field is uniquely designed to enhance the understanding of credit analysis in a fashion that permits a broad perspective on the science and art of credit analysis.