The Econometrics of Financial Markets 2nd Edition by John Y. Campbell, Andrew W. Lo, A. Craig MacKinlay – Ebook PDF Instant Download/Delivery: 0691043019 ,9780691043012
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ISBN 10: 0691043019
ISBN 13: 9780691043012
Author: John Y. Campbell, Andrew W. Lo, A. Craig MacKinlay
A landmark book on quantitative methods in financial markets for graduate students and finance professionals
Recent decades have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is designed for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory.
Each chapter develops statistical techniques within the context of a particular financial application. This exciting text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have learned into their own applications.
The Econometrics of Financial Markets 2nd Table of contents:
1 Introduction
1.1 Organization of the Book
1.2 Useful Background
1.2.1 Mathematics Background
1.2.2 Probability and Statistics Background
1.2.3 Finance Theory Background
1.3 Notation
1.4 Prices, Returns, and Compounding
1.4.1 Definitions and Conventions
1.4.2 The Marginal, Conditional, and Joint Distribution of Returns
1.5 Market Efficiency
1.5.1 Efficient Markets and the Law of Iterated Expectations
1.5.2 Is Market Efficiency Testable?
2 The Predictability of Asset Returns
2.1 The Random Walk Hypotheses
2.1.1 The Random Walk 1: IID Increments
2.1.2 The Random Walk 2: Independent Increments
2.1.3 The Random Walk 3: Uncorrelated Increments
2.2 Tests of Random Walk 1: IID Increments
2.2.1 Traditional Statistical Tests
2.2.2 Sequences and Reversals, and Runs
2.3 Tests of Random Walk 2: Independent Increments
2.3.1 Filter Rules
2.3.2 Technical Analysis
2.4 Tests of Random Walk 3: Uncorrelated Increments
2.4.1 Autocorrelation Coefficients
2.4.2 Portmanteau Statistics
2.4.3 Variance Ratios
2.5 Long-Horizon Returns
2.5.1 Problems with Long-Horizon Inferences
2.6 Tests For Long-Range Dependence
2.6.1 Examples of Long-Range Dependence
2.6.2 The Hurst-Mandelbrot Rescaled Range Statistic
2.7 Unit Root Tests
2.8 Recent Empirical Evidence
2.8.1 Autocorrelations
2.8.2 Variance Ratios
2.8.3 Cross-Autocorrelations and Lead-Lag Relations
2.8.4 Tests Using Long-Horizon Returns
2.9 Conclusion
3 Market Microstructure
3.1 Nonsynchronous Trading
3.1.1 A Model of Nonsynchronous Trading
3.1.2 Extensions and Generalizations
3.2 The Bid-Ask Spread
3.2.1 Bid-Ask Bounce
3.2.2 Components of the Bid-Ask Spread
3.3 Modeling Transactions Data
3.3.1 Motivation
3.3.2 Rounding and Barrier Models
3.3.3 The Ordered Probit Model
3.4 Recent Empirical Findings
3.4.1 Nonsynchronous Trading
3.4.2 Estimating the Effective Bid-Ask Spread
3.4.3 Transactions Data
3.5 Conclusion
4 Event-Study Analysis
4.1 Outline of an Event Study
4.2 An Example of an Event Study
4.3 Models for Measuring Normal Performance
4.3.1 Constant-Mean-Return Model
4.3.2 Market Model
4.3.3 Other Statistical Models
4.3.4 Economic Models
4.4 Measuring and Analyzing Abnormal Returns
4.4.1 Estimation of the Market Model
4.4.2 Statistical Properties of Abnormal Returns
4.4.3 Aggregation of Abnormal Returns
4.4.4 Sensitivity to Normal Return Model
4.4.5 CARs for the Earnings-Announcement Example
4.4.6 Inferences with Clustering
4.5 Modifying the Null Hypothesis
4.6 Analysis of Power
4.7 Nonparametric Tests
4.8 Cross-Sectional Models
4.9 Further Issues
4.9.1 Role of the Sampling Interval
4.9.2 Inferences with Event-Date Uncertainty
4.9.3 Possible Biases
4.10 Conclusion
5 The Capital Asset Pricing Model
5.1 Review of the CAPM
5.2 Results from Efficient-Set Mathematics
5.3 Statistical Framework for Estimation and Testing
5.3.1 Sharpe-Lintner Version
5.3.2 Black Version
5.4 Size of Tests
5.5 Power of Tests
5.6 Nonnormal and Non-IID Returns
5.7 Implementation of Tests
5.7.1 Summary of Empirical Evidence
5.7.2 Illustrative Implementation
5.7.3 Unobservability of the Market Portfolio
5.8 Cross-Sectional Regressions
5.9 Conclusion
6 Multifactor Pricing Models
6.1 Theoretical Background
6.2 Estimation and Testing
6.2.1 Portfolios as Factors with a Riskfree Asset
6.2.2 Portfolios as Factors without a Riskfree Asset
6.2.3 Macroeconomic Variables as Factors
6.2.4 Factor Portfolios Spanning the Mean-Variance Frontier
6.3 Estimation of Risk Premia and Expected Returns
6.4 Selection of Factors
6.4.1 Statistical Approaches
6.4.2 Number of Factors
6.4.3 Theoretical Approaches
6.5 Empirical Results
6.6 Interpreting Deviations from Exact Factor Pricing
6.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio
6.6.2 Squared Sharpe Ratios
6.6.3 Implications for Separating Alternative Theories
6.7 Conclusion
7 Present-Value Relations
7.1 The Relation between Prices, Dividends, and Returns
7.1.1 The Linear Present-Value Relation with Constant Expected Returns
7.1.2 Rational Bubbles
7.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns
7.1.4 Prices and Returns in a Simple Example
7.2 Present-Value Relations and US Stock Price Behavior
7.2.1 Long-Horizon Regressions
7.2.2 Volatility Tests
7.2.3 Vector Autoregressive Methods
7.3 Conclusion
8 Intertemporal Equilibrium Models
8.1 The Stochastic Discount Factor
8.1.1 Volatility Bounds
8.2 Consumption-Based Asset Pricing with Power Utility
8.2.1 Power Utility in a Lognormal Model
8.2.2 Power Utility and Generalized Method of Moments
8.3 Market Frictions
8.3.1 Market Frictions and Hansen-Jagannathan Bounds
8.3.2 Market Frictions and Aggregate Consumption Data
8.4 More General Utility Functions
8.4.1 Habit Formation
8.4.2 Psychological Models of Preferences
8.5 Conclusion
9 Derivative Pricing Models
9.1 Brownian Motion
9.1.1 Constructing Brownian Motion
9.1.2 Stochastic Differential Equations
9.2 A Brief Review of Derivative Pricing Methods
9.2.1 The Black-Scholes and Merton Approach
9.2.2 The Martingale Approach
9.3 Implementing Parametric Option Pricing Models
9.3.1 Parameter Estimation of Asset Price Dynamics
9.3.2 Estimating σ in the Black-Scholes Model
9.3.3 Quantifying the Precision of Option Price Estimators
9.3.4 The Effects of Asset Return Predictability
9.3.5 Implied Volatility Estimators
9.3.6 Stochastic Volatility Models
9.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation
9.4.1 Discrete Versus Continuous Time
9.4.2 How Many Simulations to Perform
9.4.3 Comparisons with a Closed-Form Solution
9.4.4 Computational Efficiency
9.4.5 Extensions and Limitations
9.5 Conclusion
10 Fixed-Income Securities
10.1 Basic Concepts
10.1.1 Discount Bonds
10.1.2 Coupon Bonds
10.1.3 Estimating the Zero-Coupon Term Structure
10.2 Interpreting the Term Structure of Interest Rates
10.2.1 The Expectations Hypothesis
10.2.2 Yield Spreads and Interest Rate Forecasts
10.3 Conclusion
11 Term-Structure Models
11.1 Affine-Yield Models
11.1.1 A Homoskedastic Single-Factor Model
11.1.2 A Square-Root Single-Factor Model
11.1.3 A Two-Factor Model
11.1.4 Beyond Affine-Yield Models
11.2 Fitting Term-Structure Models to the Data
11.2.1 Real Bonds, Nominal Bonds, and Inflation
11.2.2 Empirical Evidence on Affine-Yield Models
11.3 Pricing Fixed-Income Derivative Securities
11.3.1 Fitting the Current Term Structure Exactly
11.3.2 Forwards and Futures
11.3.3 Option Pricing in a Term-Structure Model
11.4 Conclusion
12 Nonlinearities in Financial Data
12.1 Nonlinear Structure in Univariate Time Series
12.1.1 Some Parametric Models
12.1.2 Univariate Tests for Nonlinear Structure
12.2 Models of Changing Volatility
12.2.1 Univariate Models
12.2.2 Multivariate Models
12.2.3 Links between First and Second Moments
12.3 Nonparametric Estimation
12.3.1 Kernel Regression
12.3.2 Optimal Bandwidth Selection
12.3.3 Average Derivative Estimators
12.3.4 Application: Estimating State-Price Densities
12.4 Artificial Neural Networks
12.4.1 Multilayer Perceptrons
12.4.2 Radial Basis Functions
12.4.3 Projection Pursuit Regression
12.4.4 Limitations of Learning Networks
12.4.5 Application: Learning the Black-Scholes Formula
12.5 Overfitting and Data-Snooping
12.6 Conclusion
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