The basics of financial econometrics : tools, concepts, and asset management applications / Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev, Bala G. Arshanapalli, with the assistance of Markus Hochstotter.
"An accessible guide to the growing field of financial econometrics ."--Provided by publisher.
Saved in:
| Online Access: |
Full Text (via Wiley) |
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| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2014]
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| Series: | Frank J. Fabozzi series.
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| Subjects: |
MARC
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| 100 | 1 | |a Fabozzi, Frank J. |0 http://id.loc.gov/authorities/names/n79107968 |1 http://isni.org/isni/0000000121357840. | |
| 245 | 1 | 4 | |a The basics of financial econometrics : |b tools, concepts, and asset management applications / |c Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev, Bala G. Arshanapalli, with the assistance of Markus Hochstotter. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2014] | |
| 300 | |a 1 online resource (xxi, 428 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a The Frank J. Fabozzi series. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a Simple Linear Regression -- Multiple Linear Regression -- Building and Testing a Multiple Linear Regression Model -- Introduction to Time Series Analysis -- Regression Models with Categorical Variables -- Quantile Regressions -- Robust Regressions -- Autoregressive Moving Average Models -- Cointegration -- Autoregressive Heteroscedasticity Model and Its Variants -- Factor Analysis and Principal Components Analysis -- Model Estimation -- Model Selection -- Formulating and Implementing Investment Strategies Using Financial Econometrics -- Appendix A: Descriptive Statistics -- Appendix B: Continuous Probability Distributions Commonly Used in Financial Econometrics -- Appendix C: Inferential Statistics -- Appendix D: Fundamentals of Matrix Algebra -- Appendix E: Model Selection Criterion: AIC and BIC -- Appendix F: Robust Statistics. | |
| 520 | |a "An accessible guide to the growing field of financial econometrics ."--Provided by publisher. | ||
| 546 | |a English. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 650 | 0 | |a Finance |x Econometric models. |0 http://id.loc.gov/authorities/subjects/sh2008120472. | |
| 650 | 0 | |a Econometrics. |0 http://id.loc.gov/authorities/subjects/sh85040763. | |
| 650 | 7 | |a Econometrics. |2 fast |0 (OCoLC)fst00901574. | |
| 650 | 7 | |a Finance |x Econometric models. |2 fast |0 (OCoLC)fst00924377. | |
| 776 | 0 | 8 | |i Print version: |a Fabozzi, Frank J. |t Basics of econometrics. |d Hoboken, New Jersey : John Wiley & Sons, Inc., [2014] |z 9781118573204 |w (DLC) 2013043119 |w (OCoLC)863100554. |
| 830 | 0 | |a Frank J. Fabozzi series. |0 http://id.loc.gov/authorities/names/no2002018077. | |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://onlinelibrary.wiley.com/doi/book/10.1002/9781118856406 |z Full Text (via Wiley) |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g ch. 1 |t Introduction -- |t Financial Econometrics at Work -- |t Data Generating Process -- |t Applications of Financial Econometrics to Investment Management -- |t Key Points -- |g ch. 2 |t Simple Linear Regression -- |t Role of Correlation -- |t Regression Model: Linear Functional Relationship between Two Variables -- |t Distributional Assumptions of the Regression Model -- |t Estimating the Regression Model -- |t Goodness-of-Fit of the Model -- |t Two Applications in Finance -- |t Linear Regression of a Nonlinear Relationship -- |t Key Points -- |g ch. 3 |t Multiple Linear Regression -- |t Multiple Linear Regression Model -- |t Assumptions of the Multiple Linear Regression Model -- |t Estimation of the Model Parameters -- |t Designing the Model -- |t Diagnostic Check and Model Significance -- |t Applications to Finance -- |t Key Points -- |g ch. 4 |t Building and Testing a Multiple Linear Regression Model -- |t Problem of Multicollinearity -- |t Model Building Techniques -- |t Testing the Assumptions of the Multiple Linear Regression Model -- |t Key Points -- |g ch. 5 |t Introduction to Time Series Analysis -- |t What Is a Time Series-- |t Decomposition of Time Series -- |t Representation of Time Series with Difference Equations -- |t Application: The Price Process -- |t Key Points -- |g ch. 6 |t Regression Models with Categorical Variables -- |t Independent Categorical Variables -- |t Dependent Categorical Variables -- |t Key Points -- |g ch. 7 |t Quantile Regressions -- |t Limitations of Classical Regression Analysis -- |t Parameter Estimation -- |t Quantile Regression Process -- |t Applications of Quantile Regressions in Finance -- |t Key Points -- |g ch. 8 |t Robust Regressions -- |t Robust Estimators of Regressions -- |t Illustration: Robustness of the Corporate Bond Yield Spread Model -- |t Robust Estimation of Covariance and Correlation Matrices -- |t Applications -- |t Key Points -- |g ch. 8 |t Autoregressive Moving Average Models -- |t Autoregressive Models -- |t Moving Average Models -- |t Autoregressive Moving Average Models -- |t ARMA Modeling to Forecast S&P 500 Weekly Index Returns -- |t Vector Autoregressive Models -- |t Key Points -- |g ch. 10 |t Cointegration -- |t Stationary and Nonstationary Variables and Cointegration -- |t Testing for Cointegration -- |t Key Points -- |g ch. 11 |t Autoregressive Heteroscedasticity Model and Its Variants -- |t Estimating and Forecasting Volatility -- |t ARCH Behavior -- |t GARCH Model -- |t What Do ARCH/GARCH Models Represent-- |t Univariate Extensions of GARCH Modeling -- |t Estimates of ARCH/GARCH Models -- |t Application of GARCH Models to Option Pricing -- |t Multivariate Extensions of ARCH/GARCH Modeling -- |t Key Points -- |g ch. 12 |t Factor Analysis and Principal Components Analysis -- |t Assumptions of Linear Regression -- |t Basic Concepts of Factor Models -- |t Assumptions and Categorization of Factor Models -- |t Similarities and Differences between Factor Models and Linear Regression -- |t Properties of Factor Models -- |t Estimation of Factor Models -- |t Principal Components Analysis -- |t Differences between Factor Analysis and PCA -- |t Approximate (Large) Factor Models -- |t Approximate Factor Models and PCA -- |t Key Points -- |g ch. 13 |t Model Estimation -- |t Statistical Estimation and Testing -- |t Estimation Methods -- |t Least-Squares Estimation Method -- |t Maximum Likelihood Estimation Method -- |t Instrumental Variables -- |t Method of Moments -- |t M-Estimation Method and M-Estimators -- |t Key Points -- |g ch. 14 |t Model Selection -- |t Physics and Economics: Two Ways of Making Science -- |t Model Complexity and Sample Size -- |t Data Snooping -- |t Survivorship Biases and Other Sample Defects -- |t Model Risk -- |t Model Selection in a Nutshell -- |t Key Points -- |g ch. 15 |t Formulating and Implementing Investment Strategies Using Financial Econometrics -- |t Quantitative Research Process -- |t Investment Strategy Process -- |t Key Points -- |g Appendix |t A Descriptive Statistics -- |t Basic Data Analysis -- |t Measures of Location and Spread -- |t Multivariate Variables and Distributions -- |g Appendix B |t Continuous Probability Distributions Commonly Used in Financial Econometrics -- |t Normal Distribution -- |t Chi-Square Distribution -- |t Student's t-Distribution -- |t F -Distribution -- |t α-Stable Distribution -- |g Appendix C |t Inferential Statistics -- |t Point Estimators -- |t Confidence Intervals -- |t Hypothesis Testing -- |g Appendix D |t Fundamentals of Matrix Algebra -- |t Vectors and Matrices Defined -- |t Square Matrices -- |t Determinants -- |t Systems of Linear Equations -- |t Linear Independence and Rank -- |t Vector and Matrix Operations -- |t Eigenvalues and Eigenvectors -- |g Appendix E |t Model Selection Criterion: AIC and BIC -- |t Akaike Information Criterion -- |t Bayesian Information Criterion -- |g Appendix F |t Robust Statistics -- |t Robust Statistics Defined -- |t Qualitative and Quantitative Robustness -- |t Resistant Estimators -- |t M-Estimators -- |t Least Median of Squares Estimator -- |t Least Trimmed of Squares Estimator -- |t Robust Estimators of the Center -- |t Robust Estimators of the Spread -- |t Illustration of Robust Statistics. |
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