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課程名稱 |
應用財務計量方法
APPLIED FINANCIAL ECONOMETRICS METHOD |
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開課學期 |
96-1 |
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授課對象 |
社會科學院 經濟學研究所 |
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授課教師 |
林建甫 |
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課號 |
ECON7145 |
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課程識別碼 |
323 M6310 |
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班次 |
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學分 |
2 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期五3,4(10:20~12:10) |
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上課地點 |
社法研4 |
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備註 |
限碩士班以上
總人數上限：15人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/961fe |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
本課程為研究取向的課，其主要目的在提供一個入門的財務計量研究的方法。從如何建立研究環境，尋找研究動機，保持研究心態，蒐集資料，到應用計量方法來檢定相關財務經濟學的理論是否正確，都是我想要談的範圍。 課程的內容分為兩個部份， 一為Campbell, Lo, MacKinlay 財務計量教科書 The Econometrics of Financial Markets, 及計量經濟背景工具介紹， 尤其是偏重最近研究使用的工具， 時間數列的技巧， 例如 ARCH，單根，共積，長記憶模型，非線性模型，結構改變等等。 第二部份為軟體的介紹，從如何管理電腦，網路上蒐集研究的資料，到你所不知道的 Excel，專業的 Eviews, Gauss, Matlab﹐R, 都是介紹的重點。因為想談得問題非常的多﹐沒有辦法完全涵蓋後面的參考教材， 我將挑幾個有趣的問題深入探討。參考教材是讓你參考時有找的方便，同學若讀到、用到其他相關的訊息﹐也歡迎討論﹐甚至加入課程內容。 |
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課程目標 |
課程的上法為兩節課的理論及一節課的電腦程式介紹， 如此的設計可以避免相同課程學習的邊際效用遞減。 課中我會穿插研究相關的知識， 有心理層面也有最新的訊息。 課餘時間來找我， 我會針對你的問題給你解答。 我希望修這門課的研究生都有收穫。 雖然各人需要的研究方法可能不一樣， 但可以各取所需， 各盡所能。 修課成績標準， 碩士班、博士班也可不同。 但只要你相信一分耕耘一分收穫， 統計的大數法則： 『數大? K是準』。 多努力之下， 自然可以趨近高分數的平均數。 |
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課程要求 |
第一部份 教科書
Campbell, Lo, MacKinlay, The Econometrics of Financial Markets, Princeton University Press, 1996, ISBN 0-691-04301-9.
1 Introduction 3
1.1 Organization of the Book 4
1.2 Useful Background 6
1.2.1 Mathematics Background 6
1.2.2 Probability and Statistics Background 6
1.2.3 Finance Theory Background 7
1.3 Notation 8
1.4 Prices, Returns, and Compounding 9
1.4.1 Definitions and Conventions 9
1.4.2 The Marginal, Conditional, and Joint Distribution of Returns 13
1.5 Market Efficiency 20
1.5.1 Efficient Markets and the Law of Iterated Expectations 22
1.5.2 Is Market Efficiency Testable? 24
2 The Predictability of Asset Returns 27
2.1 The Random Walk Hypotheses 28
2.1.1 The Random Walk 1: IID Increments 31
2.1.2 The Random Walk 2: Independent Increments 32
2.1.3 The Random Walk 3: Uncorrelated Increments 33
2.2 Tests of Random Walk 1: IID Increments 33
2.2.1 Traditional Statistical Tests 33
2.2.2 Sequences and Reversals, and Runs 34
2.3 Tests of Random Walk 2: Independent Increments 41
2.3.1 Filter Rules 42
2.3.2 Technical Analysis 43
2.4 Tests of Random Walk 3: Uncorrelated Increments 44
2.4.1 Autocorrelation Coefficients 44
2.4.2 Portmanteau Statistics 47
2.4.3 Variance Ratios 48
2.5 Long-Horizon Returns 55
2.5.1 Problems with Long-Horizon Inferences 57
2.6 Tests For Long-Range Dependence 59
2.6.1 Examples of Long-Range Dependence 59
2.6.2 The Hurst-Mandelbrot Rescaled Range Statistic 62
2.7 Unit Root Tests 64
2.8 Recent Empirical Evidence 65
2.8.1 Autocorrelations 66
2.8.2 Variance Ratios 68
2.8.3 Cross-Autocorrelations and Lead-Lag Relations 74
2.8.4 Tests Using Long-Horizon Returns 78
2.9 Conclusion 80
3 Market Microstructure 83
3.1 Nonsynchronous Trading 84
3.1.1 A Model of Nonsynchronous Trading 85
3.1.2 Extensions and Generalizations 98
3.2 The Bid-Ask Spread 99
3.2.1 Bid-Ask Bounce 101
3.2.2 Components of the Bid-Ask Spread 103
3.3 Modeling Transactions Data 107
3.3.1 Motivation 108
3.3.2 Rounding and Barrier Models 114
3.3.3 The Ordered Probit Model 122
3.4 Recent Empirical Findings 128
3.4.1 Nonsynchronous Trading 128
3.4.2 Estimating the Effective Bid-Ask Spread 134
3.4.3 Transactions Data 136
3.5 Conclusion 144
4.1 Outline of an Event Study 150
4.2 An Example of an Event Study 152
4.3 Models for Measuring Normal Performance 153
4.3.1 Constant-Mean-Return Model 154
4.3.2 Market Model 155
4.3.3 Other Statistical Models 155
4.3.4 Economic Models 156
4.4 Measuring and Analyzing Abnormal Returns 157
4.4.1 Estimation of the Market Model 158
4.4.2 Statistical Properties of Abnormal Returns 159
4.4.3 Aggregation of Abnormal Returns 160
4.4.4 Sensitivity to Normal Return Model 162
4.4.5 CARs for the Earnings-Announcement Example 163
4.4.6 Inferences with Clustering 166
4.5 Modifying the Null Hypothesis 167
4.6 Analysis of Power 168
4.7 Nonparametric Tests 172
4.8 Cross-Sectional Models 173
4.9 Further Issues 175
4.9.1 Role of the Sampling Interval 175
4.9.2 Inferences with Event-Date Uncertainty 176
4.9.3 Possible Biases 177
4.10 Conclusion 178
5 The Capital Asset Pricing Model 181
5.1 Review of the CAPM 181
5.2 Results from Efficient-Set Mathematics 184
5.3 Statistical Framework for Estimation and Testing 188
5.3.1 Sharpe-Lintner Version 189
5.3.2 Black Version 196
5.4 Size of Tests 203
5.5 Power of Tests 204
5.6 Nonnormal and Non-IID Returns 208
5.7 Implementation of Tests 211
5.7.1 Summary of Empirical Evidence 211
5.7.2 Illustrative Implementation 212
5.7.3 Unobservability of the Market Portfolio 213
5.8 Cross-Sectional Regressions 215
5.9 Conclusion 217
6 Multifactor Pricing Models 219
6.1 Theoretical Background 219
6.2 Estimation and Testing 222
6.2.1 Portfolios as Factors with a Riskfree Asset 223
6.2.2 Portfolios as Factors without a Riskfree Asset 224
6.2.3 Macroeconomic Variables as Factors 226
6.2.4 Factor Portfolios Spanning the Mean-Varianceprotect Frontier 228
6.3 Estimation of Risk Premia and Expected Returns 231
6.4 Selection of Factors 233
6.4.1 Statistical Approaches 233
6.4.2 Number of Factors 238
6.4.3 Theoretical Approaches 239
6.5 Empirical Results 240
6.6 Interpreting Deviations from Exact Factor Pricing 242
6.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio 243
6.6.2 Squared Sharpe Ratios 245
6.6.3 Implications for Separating Alternative Theories 246
6.7 Conclusion 251
7 Present-Value Relations 253
7.1 The Relation between Prices, Dividends, and Returns 254
7.1.1 The Linear Present-Value Relation with Constant Expected Returns 255
7.1.2 Rational Bubbles 258
7.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns 260
7.1.4 Prices and Returns in a Simple Example 264
7.2 Present-Value Relations and US Stock Price Behavior 267
7.2.1 Long-Horizon Regressions 267
7.2.2 Volatility Tests 275
7.2.3 Vector Autoregressive Methods 279
7.3 Conclusion 286
8 Intertemporal Equilibrium Models 291
8.1 The Stochastic Discount Factor 293
8.1.1 Volatility Bounds 296
8.2 Consumption-Based Asset Pricing with Power Utility 304
8.2.1 Power Utility in a Lognormal Model 306
8.2.2 Power Utility and Generalized Method ofprotect Moments 314
8.3 Market Frictions 314
8.3.1 Market Frictions and Hansen-Jagannathanprotect Bounds 315
8.3.2 Market Frictions and Aggregate Consumptionprotect Data 316
8.4 More General Utility Functions 326
8.4.1 Habit Formation 326
8.4.2 Psychological Models of Preferences 332
8.5 Conclusion 334
9 Derivative Pricing Models 339
9.1 Brownian Motion 341
9.1.1 Constructing Brownian Motion 341
9.1.2 Stochastic Differential Equations 346
9.2 A Brief Review of Derivative Pricing Methods 349
9.2.1 The Black-Scholes and Merton Approach 350
9.2.2 The Martingale Approach 354
9.3 Implementing Parametric Option Pricing Models 355
9.3.1 Parameter Estimation of Asset Price Dynamics 356
9.3.2 Estimating $sigma $ in the Black-Scholes Model 361
9.3.3 Quantifying the Precision of Option Price Estimators 367
9.3.4 The Effects of Asset Return Predictability 369
9.3.5 Implied Volatility Estimators 377
9.3.6 Stochastic Volatility Models 379
9.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation 382
9.4.1 Discrete Versus Continuous Time 383
9.4.2 How Many Simulations to Perform 384
9.4.3 Comparisons with a Closed-Form Solution 384
9.4.4 Computational Efficiency 386
9.4.5 Extensions and Limitations 390
9.5 Conclusion 391
10 Fixed-Income Securities 395
10.1 Basic Concepts 396
10.1.1 Discount Bonds 397
10.1.2 Coupon Bonds 401
10.1.3 Estimating the Zero-Coupon Term Structure 409
10.2 Interpreting the Term Structure of Interest Rates 413
10.2.1 The Expectations Hypothesis 413
10.2.2 Yield Spreads and Interest Rate Forecasts 418
10.3 Conclusion 423
11 Term-Structure Models 427
11.1 Affine-Yield Models 428
11.1.1 A Homoskedastic Single-Factor Model 429
11.1.2 A Square-Root Single-Factor Model 435
11.1.3 A Two-Factor Model 438
11.1.4 Beyond Affine-Yield Models 441
11.2 Fitting Term-Structure Models to the Data 442
11.2.1 Real Bonds, Nominal Bonds, and Inflation 442
11.2.2 Empirical Evidence on Affine-Yield Models 445
11.3 Pricing Fixed-Income Derivative Securities 455
11.3.1 Fitting the Current Term Structure Exactly 456
11.3.2 Forwards and Futures 458
11.3.3 Option Pricing in a Term-Structure Model 461
11.4 Conclusion 464
12 Nonlinearities in Financial Data 467
12.1 Nonlinear Structure in Univariate Time Series 468
12.1.1 Some Parametric Models 470
12.1.2 Univariate Tests for Nonlinear Structure 475
12.2 Models of Changing Volatility 479
12.2.1 Univariate Models 481
12.2.2 Multivariate Models 490
12.2.3 Links between First and Second Moments 494
12.3 Nonparametric Estimation 498
12.3.1 Kernel Regression 500
12.3.2 Optimal Bandwidth Selection 502
12.3.3 Average Derivative Estimators 504
12.3.4 Application: Estimating State-Price Densities 507
12.4 Artificial Neural Networks 512
12.4.1 Multilayer Perceptrons 512
12.4.2 Radial Basis Functions 516
12.4.3 Projection Pursuit Regression 518
12.4.4 Limitations of Learning Networks 518
12.4.5 Application: Learning the Black-Scholes Formula 519
12.5 Overfitting and Data-Snooping 523
12.6 Conclusion 524
參考資料 方法論及工具
I. Econometrics Methodology
Gilbert, C.L., 1986, Professor Hendry's Econometric methodology, Oxford Bulletin of Economics and Statistics, 48, 3, 283-307.
Granger, C.W.J., 1993, Reducing self-interest and improving the relevence of economic research, unpublished paper.
Hendry D.F. and N.R. Ericsson, 1991, An Econometric analysis of U.K. money demand in monetary trends in the United States and the United Kingdom by Milton Friedman and Anna J. Schwartz, The American Economic Review, 81, 8-38.
II. ARIMA, Unit Root & Cointegration
Dickey, D.A. and W.A. Fuller, 1976, Distribution of the estimators for autoregressive time series with a unit root, Journal of American Statistical Association, 74, 427-431.
Engle, R.F. and C.W.J. Granger, 1991, Long-run Economic relations, CH 1, Oxford University press, New York.
Granger, C.W.J., 1993, What are we learning about the long-run? unpublished paper.
Granger, C.W.J., 1986, Developments in the study of cointegrated Economic variables, Oxford Bulletin of Economics and Statistics, 48, 283-307.
Granger, C.W.J., 1997, An Introduction to Stochastic Unit Root, Journal of Econometrics, 80, 35-62.
Johansen, S., 1988, Statistical analysis of cointegration vectors, Journal of Economic Dynamics and Control, 12, 231-254.
Johansen, S. and Juselius, K., 1992, Testing structural hypotheses in a multivariate cointegration analysis of the PPP and UIP for UK, Journal of Econometrics, 53, 211-244.
Levin, A. and C.F. Lin, 1992, Unit root tests in Panel Data: Asymptotic and finite-sample properties, UCSD Economics Discussion Paper No. 92-23.
Ljung, G. and G. Box, 1978, On a measure of lack of fit in time series models. Biometrika, 65,2, 297-303.
Phillips, P.C.B., 1986, Understanding spurious regressions in Econometrics, Journal of Econometrics, 33, 311-340.
Phillips, P.C.B., 1987, Time series regression with a unit root, Econometrica, 55, 277-301.
Engle, R.F. and C.W.J. Granger, 1987, Cointegration and error correction: representation, estimation and testing, Econometrica, 55, 251-276.
Phillips, P.C.B. and P. Perron, 1988, Testing for a unit root in time series regression, Biometrika, 75, 335-346.
III. Transfer function & Vector Time Series
Box, G.E.P. and G. C.Tiao, Intervention analysis with applications to economic and environment problems, Journal of the American Statistical Association, 70, 349, 70-79.
Tiao, G., R. Tsay and T. Wang, 1994, Usefulness of linear transformations in multivariate time-series analysis, in Dufour and Raj ed., 1994, New Developments in Time Series Econometrics.
IV. ARCH
Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327.
Engle, R.F. 1982, Autoregressive conditional Heteroskedasticity with estimates of the Variance of U.K. Inflation, Econometrica, 50, 987-1008
Engle, R.F., D.M. Lilien and R.P. Robins, 1987, Estimating time varying premia in term structure: The ARCH-M model. Econometrica, 55, 391-407.
V. Long Memory Model
Bollerslev, T. and H.O. Mikkelsen 1996, Modeling and pricing long memory in stock market volatility, Journal of Econometrics, 73, 151--184.
Ding, Z., C.W.J. Granger, and R.F. Engle 1993, A long memory property of stock returns and a new model, Journal of Empirical Finance, 1, 83--106.
Geweke, J.F. and S. Porter-Hudak 1983, The estimation and application of long memory time series models, Journal of Time Series Analysis, 1, 221--238.
Granger, C.W.J., 1999, Current Perspectives on Long Memory Process,
Granger, C.W.J. 1980, Long memory relationships and the aggregation of dynamic models, Journal of Econometrics, 14, 227--238. unpublished paper.
Granger, C.W.J. and R. Joyeux 1980, An introduction to long-memory time series models and fractional differencing, Journal of Time Series Analysis, 1, 15--29.
Granger, C.W.J. and Z. Ding 1996, Varieties of Long Memory Models, Journal of Econometrics, 73, 61-77.
Lo, A.W. 1991, Long term memory in stock market prices, Econometrica, 59, 1279--1313.
Lobato, I.N. and N.E. Savin 1998, Real and Spurious long-memory properties of stock-market data, Journal of Business and Economic Statistics, 16, 261--268.
Sowell, F. 1992, Modeling long-run behavior with the fractional ARIMA model, Journal of Monetary Economics, 29, 277--302.
VI. Nonlinear Models
STAR Model
Terasvirta, T., 1994, Specification, estimation, and evaluation of smooth transition autoregressive models, Journal of the American Statistical Association, 89,
Neural Network Model
Hornik, K., M. Stinchcombe and H. White, 1989, Multi-layer feedforward networks and universal approximators, Neural Networks, 2, 359-366.
White, H., 1989, Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models, Journal of the American Statistical Association, 84, 1003-1013.
Chaos Model
Brock w., Hsieh, D. and LeBaron B., 1992, Nonlinear Dynamics, Chaos, and Statability: Statistical Theory and Economic Evidence, MIT press. Ch,1.
Lee, T.H., H. White and C.W.J. Granger, 1993, Testing for neglected non-linearity in time series models. A comparison of neural network methods and alternative tests, Journal of Econometrics, 56, 3, 269-291.
Nonlinearity test
Terasvirta, T., C.F. Lin and C.W.J. Granger, 1992, Power of the eural network linearity test, Journal of time series, 14, 2, 209-220.
Brock w., Hsieh, D. and LeBaron B., 1992, Nonlinear Dynamics, Chaos, and Statability: Statistical Theory and Economic Evidence, MIT press. Ch,2.
Stochastic volatility model
Breidt, F.J., N. Crato and P. de Lima, 1998, The detection and estimation of long memory in stochastic volatility, Journal of Econometrics, 83, 325-348.
Harvey, A.C. 1993, Long-memory in stochastic volatility, manuscript, London School of Economics: London.
Markov Switching Model
Dueker, M.J. 1997, Markov switching in GARCH processes and mean-reverting stock-market volatility, Journal of Business and Economic Statistics, 15, 26--34.
Hamilton, J.D. and R. Susmel 1994, Autoregressive conditional heteroskedasticity and change in regime, Journal of Econometrics, 64, 307--333.
Hamilton, J.D. 1996, Specification testing in Markov-switching time-series models, Journal of Econometrics, 70, 127--157.
Hansen, B.E. 1992, The likelihood ratio test under nonstandard conditions: testing the Markov switching model of GNP, Journal of Applied Econometrics, 7, 61--82.
VII. Structural Cahnge.
Brown, R. L., J. Durbin, and J. M. Evans, 1975, Techniques for Testing the Constancy of Regression Relationships over Time, Journal of the Royal Statistical Society, Series B, 37, 149-192.
Chow, G. C., 1960, Testing for Equality Between Sets of Coefficients in Two Linear Regressions, Econometrica, 28, 591-605.
Lin, C.F. and Terasvirta, T., 1994, Testing the constancy of regression parameters against continuous structural change, Journal of Econometrics, 62,211-228.
Lin, C.F. and Terasvirta, T., 1999, Testing Parameter Constancy in Linear Models Against Stochastic Stationary Parameters, Journal of Econometrics,1999, 90,2, pp. 193-213.
Ploberger, W., W. Kr(846d)er, and K. Kontrus, 1989, A new test for structural stability in the linear regression model, Journal of Econometrics, 40, 307-318.
Ploberger, W., and W. , 1992, The CUSUM-test with OLS Residuals, Econometrica, 60, 271-285.
第二部份︰電腦及程式
經濟金融研究相關電腦網路資訊
你所不知道的 Excel
好用的套裝程式 Eviews
計量人喜歡的寫作程式 Gauss, R
財務人喜歡的寫作程式 Matlab
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預期每週課後學習時數 |
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Office Hours |
另約時間 |
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指定閱讀 |
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參考書目 |
1.Campbell, Lo, MacKinlay, The Econometrics of Financial Markets, Princeton
University Press, 1996, ISBN 0-691-04301-9.
2. Cochrane, Asset Pricing, Princeton University Press, 2001, ISBN 0-691-07498-
3. Tsay, Ruey S, Analysis of Financial Time Series, 2nd edition,
Wiley, 2006.
Reference Textbooks
1. Hayashi, F. (2000), Econometrics, Princeton University Press.
2. Greene, W.H. (2002), Econometrics Analysis, 5th edition, Prentice-Hall.
3. White, Halbert, (2000), Asymptotic Theory for Econometricians: Revised
Edition, Academic Press.
4. Johnston, J. and J. Dinardo (1998), Econometric methods, 4th Edition,
McGraw-Hill.
5. Kennedy, P. (2003), A Guide to Econometrics, 5th edition, the MIT Press.
6. Mittelhammer, Ron C., George G. Judge, and Douglas J. Miller. (2000),
Econometric Foundations, Cambridge University Press, New York.
7. Wooldridge, J.M. (2002), Introductory Econometrics: A Modern Approach, 2nd
edition, South Western College Publishing.
8. Hamilton, James D., Time Series Analysis, Princeton University
Press, 1994.
9. Enders,Walter, Applied Econometric Time Series, Second Edition, John Wiley
& Sons.
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評量方式
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