Download or read book Evaluating Factor Pricing Models Using High Frequency Panels written by Yoosoon Chang and published by . This book was released on 2015 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops a new framework and statistical tools to analyze stock returns using high frequency data. We consider a continuous-time multi-factor model via a continuous-time multivariate regression model incorporating realistic empirical features, such as persistent stochastic volatilities with leverage effects. We find that conventional regression approach often leads to misleading and inconsistent test results. We overcome this by using samples collected at random intervals, which are set by the clock running inversely proportional to the market volatility. We find that the size factor has difficulty in explaining the size-based portfolios, while the book-to-market factor is a valid pricing factor.

Download or read book Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities written by M. Hashem Pesaran and published by . This book was released on 2017 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes a novel test of zero pricing errors for the linear factor pricing model when the number of securities, N, can be large relative to the time dimension, T, of the return series. The test is based on Student t tests of individual securities and has a number of advantages over the existing standardised Wald type tests. It allows for non-Gaussianity and general forms of weakly cross correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5;000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe-Lintner CAPM and Fama-French three factor models are found mainly during the recent financial crisis. Also we find a significant negative correlation between a twelve-months moving average p-values of the test and excess returns of long/short equity strategies (relative to the return on S&P 500) over the period November 1994 to June 2015, suggesting that abnormal profits are earned during episodes of market inefficiencies.

Download or read book Firm Characteristics and Empirical Factor Models written by Leonid Kogan and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "A three-factor model using the standardized-unexpected-earnings and cashflow-to-price factors explains 15 well-known asset pricing anomalies." Our data-mining experiment provides a backdrop against which such claims can be evaluated. We construct three-factor linear pricing models that match return spreads associated with as many as 15 out of 27 commonly used firm characteristics over the 1971-2011 sample. We form target assets by sorting firms into ten portfolios on each of the chosen characteristics and form candidate pricing factors as long-short positions in the extreme decile portfolios. Our analysis exhausts all possible 351 three-factor models, consisting of two characteristic-based factors in addition to the market portfolio. 65% of the examined factor models match a larger fraction of the target return cross-sections than the CAPM or the Fama-French three-factor model. We find that the relative performance of the complete set of three-factor models is highly sensitive to the sample choice and the factor construction methodology. Our results highlight the challenges of evaluating empirical factor models.

Download or read book Exact Factor Pricing in a European Framework written by John Crombez and published by . This book was released on 2000 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Testing Competing Factor Pricing Models written by Paul Söderlind and published by . This book was released on 2016 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: A GMM-based system for two different linear factor pricing models is used to test if the pricing errors are the same. Simulations demonstrate the small sample properties. As an illustration, the test is applied to the Fama-French (1996, 2015) models.

Download or read book Factor Models written by Jared R. Studyvin and published by . This book was released on 2015 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor analytic models use a variance-covariance matrix to estimate underlying factors through the equation Sigma = Lambda Phi Lambda' + Psi . In order to provide estimates for the matrices Lambda, Phi, and Psi, some of the parameters need to be restricted and at least k2 restrictions are required to provide a unique estimate (Joreskog, 1969). The most commonly specified models place restrictions on Phi or Lambda. The estimation of the specified models, which make restrictions on Lambda or Phi, was compared and evaluated. Several different Lambda and Phi conditions were used to assess whether the specified models can reproduce these target Lambda and Phi conditions. The estimation results indicated that when the Lambda and Phi conditions matched the restrictions imposed by the specified models, the models performed well in reproducing the target values. However, when the conditions did not match the imposed restrictions the specified models performed poorly. In practice, a researcher must assume "a priori" knowledge of the restrictions to be imposed on Lambda and Phi, and hence, would not really know if the estimation results are correct or not. Several measures of fit (fit statistics) are commonly used to assess factor analytic models and were evaluated using a simulation. The simulation results indicated in general that fit statistics have no consistent relationship with the estimation quality of the specified models. In particular, the fit statistics could not identify when the estimated correlation between the factors was incorrect. In an attempt to provide better estimation of factor analytic models, a new model was developed which places no restrictions on Lambda or Phi. This was accomplished by expanding Lambda by a set of known constants which then could be appropriately restricted, thus satisfying Joreskog's requirement. The estimation based on this model performed better than the other methods considered and appears to be capable of estimating Lambda and Phi of any possible form.

Download or read book The Role of Factor Strength and Pricing Errors for Estimation and Inference in Asset Pricing Models written by M. Hashem Pesaran and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we are concerned with the role of factor strength and pricing errors in asset pricing models, and their implications for identification and estimation of risk premia. We establish an explicit relationship between the pricing errors and the presence of weak factors that are correlated with stochastic discount factor. We introduce a measure of factor strength, and distinguish between observed factors and unobserved factors. We show that unobserved factors matter for pricing if they are correlated with the discount factor, and relate the strength of the weak factors to the strength (pervasiveness) of non-zero pricing errors. We then show, that even when the factor loadings are known, the risk premia of a factor can be consistently estimated only if it is strong and if the pricing errors are weak. Similar results hold when factor loadings are estimated, irrespective of whether individual returns or portfolio returns are used. We derive distributional results for two pass estimators of risk premia, allowing for non-zero pricing errors. We show that for inference on risk premia the pricing errors must be sufficiently weak. We consider both when n (the number of securities) is large and T (the number of time periods) is short, and the case of large n and T. Large n is required for consistent estimation of risk premia, whereas the choice of short T is intended to reduce the possibility of time variations in the factor loadings. We provide monthly rolling estimates of the factor strengths for the three Fama- French factors over the period 1989-2018.

Download or read book High Frequency Factor Analysis with Partially Observable Factors written by Dachuan Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers a novel factor structure -- Partially Observable Factor Model -- where both observable factors and latent factors exist in the model simultaneously. Such factor structure can make sure both interpretability and goodness-of-fit at the same time. Necessary estimation methodologies for this partially observable factor model are developed in this paper for the high frequency data. The proposed estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously. In the case of finite dimensionality, we provide the estimation theory for the integrated eigenvalues of the residual covariance matrix, which including the bias-corrected estimator, central limit theorem and asymptotic variance estimator. As a result, the asymptotic normality of the bias-corrected estimator can be applied to test the existence of the latent factors. In the case of high dimensionality, we proposed the estimation method for the high dimensional covariance and precision matrices. In contrast to the existing literature, which assuming the residual covariance matrix generated from regression to be weakly correlated or sparse, this paper relaxed such assumptions and designed a new estimator for this task. The theoretical development of this new estimator is non-trivial and the convergence rates are established accordingly. Monte Carlo simulation demonstrates the validity of our estimation methodologies. Empirical study demonstrates that the latent factors significantly exist in the residual process of the high frequency regression.

Download or read book Handbook of High Frequency Trading and Modeling in Finance written by Ionut Florescu and published by John Wiley & Sons. This book was released on 2016-04-25 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

Download or read book A Comparison of Multi factor Asset Pricing Models Using US Stock Market Data written by Pia Grammig and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Realized Mixed Frequency Factor Models for Vast Dimensional Covariance Estimation written by Karim Bannouh and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast dimensional covari- ance matrices of asset returns. The MFFM uses high-frequency (intraday) data to estimate factor (co)variances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. We propose the use of highly liquid assets such as exchange traded funds (ETFs) as factors. Prices for these contracts are observed essentially free of microstructure noise at high frequencies, allowing us to obtain precise estimates of the factor covariances. The factor loadings instead are estimated from daily data to avoid biases due to market microstructure effects such as the relative illiquidity of individual stocks and non-synchronicity between the returns on factors and stocks. Our theoretical, simulation and empirical results illustrate that the performance of the MFFM is excellent, both compared to conventional factor models based solely on low-frequency data and to popular realized covariance estimators based on high-frequency data.

Download or read book A Factor Analysis Model for Evaluating and Comparing Company Performance written by Elmer Earl Burch and published by . This book was released on 1970 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assessing Specification Errors in Stochastic Discount Factor Models written by Lars Peter Hansen and published by . This book was released on 1994 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we develop alternative ways to compare asset pricing models when it is understood that their implied stochastic discount factors do not price all portfolios correctly. Unlike comparisons based on x2 statistics associated with null hypothesis that models are correct, our measures of model performance do not reward variability of discount factor proxies. One of our measures is designed to exploit fully the implications of arbitrage-free pricing of derivative claims. We demonstrate empirically the usefulness of methods in assessing some alternative stochastic factor models that have been proposed in asset pricing literature

Download or read book An Application of Factor Pricing Models to the Polish Stock Market written by Adam Zaremba and published by . This book was released on 2018 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: We evaluate and compare the performance of four popular factor pricing models: the capital asset pricing model (Sharpe 1964), the Fama and French (1993) three-factor model, Carhart's (1997) four-factor model, and the five-factor model of Fama and French (2015). We aim to establish which of these models is most applicable in the Polish stock market. To do so, we employ a battery of tests -- cross-sectional regressions, examination of one-way and two-way sorted portfolios, tests of monotonic relationships, and factor redundancy tests -- and apply them to a sample of more than 1100 stocks for the years 2000-2018. The results indicate that the four-factor model outperforms the other models; it has the greatest explanatory power for cross-sectional returns and is therefore well-suited for asset pricing in Poland.

Download or read book Using Principal Component Analysis to Estimate a Highdimensional Factor Model with High frequency Data written by Yacine Aït-Sahalia and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Exploring Multifactor Models written by Kevin Q. Wang and published by . This book was released on 2002 with total page 49 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multifactor asset pricing models play an important role in evaluation of anomalies and managed portfolios. In empirical studies, however, the researchers' prior often includes a rich set of competing models which all seem plausible, but none is conclusively dominant. In this paper we propose a unified approach to model selection and inference for application and evaluation of factor pricing models. We run horse races among a set of high profile models to select winners according to predictive ability and then utilize White's (2000) reality check methodology to test the top performers. We provide empirical results on testing multifactor explanations of industry momentum, and present a test of the three moment CAPM.

Download or read book Multi Factor Models and Signal Processing Techniques written by Jay Emmanuelle and published by . This book was released on 2015 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper surveys the existing literature on the most widely-used factor models employed in the realm of financial asset pricing field. Through the concrete application of evaluating risks in the hedge fund industry, this paper demonstrates that signal processing techniques are an interesting alternative to the selection of factors and can provide more efficient estimation procedures than the classical ones.