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Author: Yan Li Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This paper develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature -- the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first stage is correctly specified. Simulations indicate that our estimates and tests perform well in finite samples. In our empirical analysis, we use our new method to examine the performance of the conditional CAPM, which has generated controversial results in the recent asset-pricing literature.
Author: Yan Li Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This paper develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature -- the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first stage is correctly specified. Simulations indicate that our estimates and tests perform well in finite samples. In our empirical analysis, we use our new method to examine the performance of the conditional CAPM, which has generated controversial results in the recent asset-pricing literature.
Author: Liyan Yang Publisher: ISBN: Category : Languages : en Pages : 39
Book Description
Recent studies of conditional factor models do not specify conditioning information but use data from small windows to estimate the time series of conditional alphas and betas. In this paper, we propose a nonparametric method using an optimal window to estimate time-varying coefficients. In addition, we offer two empirical tests of a conditional factor model. Using our new method, we examine the performance of the conditional CAPM and the conditional Fama-French three-factor model in explaining the return variations of portfolios sorted by size, book-to-market ratios, and past returns, for which recent literature has generated controversial results. We find that, although in general the conditional FF model outperforms the conditional CAPM, both models fail to explain well-known asset-pricing anomalies. Moreover, for both models, the failure is more pronounced for the equally-weighted portfolios than for the value-weighted ones.
Author: Mohamed Saidane Publisher: ISBN: Category : Languages : en Pages : 27
Book Description
Mixed-State conditionally heteroscedastic latent factor models attempt to describe a complex nonlinear dynamic system with a succession of linear latent factor models indexed by a switching variable. Unfortunately, despite the framework's simplicity exact state and parameter estimation are still intractable because of the interdependency across the latent factor volatility processes. Recently, a broad class of learning and inference algorithms for time series models have been successfully cast in the framework of dynamic Bayesian networks (DBN). This paper describes a novel DBN-based switching conditionally heteroscedastic latent factor model. The key methodological contribution of this paper is the novel use of the Generalized Pseudo-Bayesian method GPB2, a structured variational learning approach and an approximated version of the Viterbi algorithm in conjunction with the EM algorithm for overcoming the intractability of exact inference in mixed-state latent factor model. The conditional EM algorithm that we have developed for the maximum likelihood estimation is based on an extended switching Kalman filter approach which yields inferences about the unobservable path of the common factors and their variances, and the latent variable of the state process. Extensive Monte Carlo simulations show promising results for tracking, interpolation, synthesis, and classification using learned models.
Author: Publisher: Elsevier ISBN: 0444636544 Category : Business & Economics Languages : en Pages : 594
Book Description
Handbook of Econometrics, Volume 7A, examines recent advances in foundational issues and "hot" topics within econometrics, such as inference for moment inequalities and estimation of high dimensional models. With its world-class editors and contributors, it succeeds in unifying leading studies of economic models, mathematical statistics and economic data. Our flourishing ability to address empirical problems in economics by using economic theory and statistical methods has driven the field of econometrics to unimaginable places. By designing methods of inference from data based on models of human choice behavior and social interactions, econometricians have created new subfields now sufficiently mature to require sophisticated literature summaries. Presents a broader and more comprehensive view of this expanding field than any other handbook Emphasizes the connection between econometrics and economics Highlights current topics for which no good summaries exist
Author: Maia Berkane Publisher: Springer Science & Business Media ISBN: 146121842X Category : Mathematics Languages : en Pages : 285
Book Description
This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.
Author: Bent Jesper Christensen Publisher: Princeton University Press ISBN: 1400833108 Category : Business & Economics Languages : en Pages : 488
Book Description
Economic Modeling and Inference takes econometrics to a new level by demonstrating how to combine modern economic theory with the latest statistical inference methods to get the most out of economic data. This graduate-level textbook draws applications from both microeconomics and macroeconomics, paying special attention to financial and labor economics, with an emphasis throughout on what observations can tell us about stochastic dynamic models of rational optimizing behavior and equilibrium. Bent Jesper Christensen and Nicholas Kiefer show how parameters often thought estimable in applications are not identified even in simple dynamic programming models, and they investigate the roles of extensions, including measurement error, imperfect control, and random utility shocks for inference. When all implications of optimization and equilibrium are imposed in the empirical procedures, the resulting estimation problems are often nonstandard, with the estimators exhibiting nonregular asymptotic behavior such as short-ranked covariance, superconsistency, and non-Gaussianity. Christensen and Kiefer explore these properties in detail, covering areas including job search models of the labor market, asset pricing, option pricing, marketing, and retirement planning. Ideal for researchers and practitioners as well as students, Economic Modeling and Inference uses real-world data to illustrate how to derive the best results using a combination of theory and cutting-edge econometric techniques. Covers identification and estimation of dynamic programming models Treats sources of error--measurement error, random utility, and imperfect control Features financial applications including asset pricing, option pricing, and optimal hedging Describes labor applications including job search, equilibrium search, and retirement Illustrates the wide applicability of the approach using micro, macro, and marketing examples
Author: Raquel Prado Publisher: CRC Press ISBN: 1498747043 Category : Mathematics Languages : en Pages : 473
Book Description
• Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.
Author: Manuel Díaz-Campos Publisher: Taylor & Francis ISBN: 0429577958 Category : Foreign Language Study Languages : en Pages : 631
Book Description
The Routledge Handbook of Variationist Approaches to Spanish provides an up-to-date overview of the latest research examining sociolinguistic approaches to analyzing variation in Spanish. Divided into three sections, the book includes the most current research conducted in Spanish variationist sociolinguistics. This comprehensive volume covers phonological, morphosyntactic, social, and lexical variation in Spanish. Each section is further divided into subsections focusing on specific areas of language variation, highlighting the most salient and current developments in each subfield of Hispanic sociolinguistics. As such, this Handbook delves further into the details of topics relating to variation and change in Spanish than previous publications, with a focus on the symbolic sociolinguistic value of specific phenomena in the field. Encouraging readers to think critically about language variation, this book will be of interest to advanced undergraduate and graduate students, as well as researchers seeking to explore lesser-known areas of Hispanic sociolinguistics. The Routledge Handbook of Variationist Approaches to Spanish will be a welcome addition to specialists and students in the fields of linguistics, Hispanic linguistics, sociolinguistics, and linguistic anthropology.
Author: Monica Billio Publisher: ISBN: Category : Languages : en Pages :
Book Description
The class of parametric dynamic latent variable models is becoming increasingly popular in finance and economics. Latent factor models, switching regimes models, stochastic volatility models, and dynamic disequilibrium models are only a few examples of this class of model. Inference in this class may be difficult since the computation of the likelihood function requires integrating out the unobservable components and calculating very high dimensional integrals. We propose an estimation procedure that could be applied to any dynamic latent model. The approach is based on the indirect inference principle and, in order to capture the dynamic features of these models, the binding functions are conditional expectations of functions of the endogenous variables given their past values. These conditional expectations are estimated by a nonparametric kernel-based approach. Unlike the indirect inference method, no optimization step is involved in the computation of the binding function and the approach is useful when no convenient auxiliary model is available. In spite of the nonparametric feature of the approach, the estimator is consistent and its convergence rate may be arbitrarily close to the classical parametric one. Moreover, a scoring method to select the best binding functions is proposed. Finally, some Monte Carlo experiments for factor ARCH and GARCH models show the feasibility of the approach.
Author: Yan Li
Author: Yan Li
Author: Liyan Yang
Author: Dennis Kristensen
Author: Mohamed Saidane
Author: 
Author: Maia Berkane
Author: Bent Jesper Christensen
Author: Raquel Prado
Author: Manuel Díaz-Campos
Author: Monica Billio