Escola de Pós-graduação em Economia (EPGE) Fundação Getúlio Vargas (FGV/RJ)
Econometria Aplicada a Finanças
Prof. Márcio Antônio Salvato
Objetivo: Modelagem estatística de séries temporais financeiras.
Tópicos:
1) Introdução Finanças [ZN, SN]
Fronteira média-variância, Markovitz, Value at Risk (VaR), Constant Expected Return model (CER), Capital Asset Price Model (CAPM) 2) Distribuição e comportamento dinâmico de retorno de ativos [TSAY
(1,2,7), MFTS (2,3,4,5,19)]
a. Distribuições não normais b. Previsão de retornos
c. Aplicações para gestão de risco
3) Modelagem de volatilidade [MFTS (7,8,9,13,14), TSAY (3,10,11)]
a. Autoregressive conditional heteroskedasticity (ARCH) b. Modelos de volatilidade estocástica
c. Aplicações para gestão de risco e derivativos
4) Séries temporais com alta freqüência [MFTS (9), TSAY (5)]
a. Market microstructure models
b. Realized variance, covariance and bi-power variation
Bibliografia Básica
Zivot, E. and Wang, J. (2006). Modeling Financial Time Series with S-PLUS, Second Edition. Springer-Verlag. [MFTS]
Tsay, R. (2006). Analysis of Financial Time Series, Second Edition. Wiley.
[TSAY]
Zivot Notes [ZN]
Paul Söderlin Notes [SN]
Bibliografia Complementar
• Distribuição e comportamento dinâmico de retorno de ativos:
Campbell, Lo and MacKinlay (1998). The Econometrics of Financial Markets.
Princeton University Press, Princeton, New Jersey. Chapters 1, 2, and 7
Cochrane, J. H. (2001). Asset Pricing, Princeton University Press, Princeton, New Jersey. Chapter 20
Fama, E. and K. French (1988). "Permanent and temporary components of stock prices," Journal of Political Economy, 96, 246-273. Available in JSTOR.
Nelson, C.R., and M. Kim (1993). Predictable stock returns: The role of small sample bias, Journal of Finance, 48, 641-661
Stambaugh, R (1986). Bias in regressions with lagged stochastic regressors, CRSP Working Paper 156, University of Chicago.
• Modelagem de Volatidade:
Zivot, E. (2008). "Practical Issues in the Analysis of Univariate GARCH Models,"
forthcoming in the Handbook of Financial Time Series. Splus script for examples in paper.
Diebold, F.X. and J. Lopez (1995). "Modeling Volatility Dynamics," NBER Technical Working Paper No. 173.
Engle, R.F. (2000). "What Good is a Volatility Model," unpublished manuscript, Stern School of Business, NYU.
Engle, R.F. (2001). "GARCH 101: The Use of ARCH/GARCH Model in Applied Economics," Journal of Economic Perspectives, 15(4), 157-168.
Granger, C. and S.-H. Poon (2001). "Forecasting Financial Market Volatility,"
unpublished manuscript, Strathclyde University.
• Séries temporais com alta freqüência:
Andersen, T., T. Bollerslev, F.X. Diebold, H. Ebens (2001). “The Distribution of Realized Stock Return Volatility,” Journal of Financial Economics, 61, 43-76.
Andersen, T., T. Bollerslev, F.X. Diebold, P. Labys (2001). The Distribution of Realized Exchange Rate Volatility, Journal of the American Statistical Association 96, 42-55.
Andersen, T., T. Bollerslev, F.X. Diebold, P. Labys (2003). “Modeling and Forecasting Realized Volatility,” Econometrica, 71(2), 579-626.
Andersen, T., T. Bollerslev, F.X. Diebold, and C. Vega (2004). “Real-Time Price Discovery in Stock, Bond and Foreign Exchange Markets,” unpublished manuscript, Northwestern University, Duke University, University of Pennsylvania, and University of Rochester.
Barndorff-Nielsen, O.E., and N. Shephard (2002a). “Estimating Quadratic Variation Using Realized Variance,” Journal of Applied Econometrics, 17, 457- 477.
Barndorff-Nielsen, O.E., and N. Shephard (2002b). “Econometric Analysis of Realized Volatility and Its Use in Estimating Stochastic Volatility Models,” Journal of the Royal Statistical Society, Series B, 64, 253-280.
Yan, B. and Zivot, E. (2008). "A Structural Analysis of Price Discovery Measures". Working paper, Department of Economics, University of Washington.
Yan, B. and Zivot, E. (2008). "The Dynamics of Price Discovery". Working paper, Department of Economics, University of Washington.
Time Series and Extreme Value
Time Series
1. Fama, E. and K. French (1988). "Permanent and temporary components of stock prices," Journal of Political Economy, 96, 246-273. Available in JSTOR.
2. Nelson, C.R., Kim, M.J., 1993. Predictable stock returns: the role of small sample bias. Journal of Finance 48, 641–661.
3. Stambaugh, R.F., 1999. Predictive regressions. Journal of Financial Economics 54, 375–421.
4. Valkanov, R., 2003. Long-horizon regressions: theoretical results and applications. Journal of Financial Economics 68, 201–232.
Extreme Value
1. McNeil, A. J., and R. Frey. (2000). ‘‘Estimation of Tail-related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach.’’ Journal of Empirical Finance 7:271–300.
2. Brooks, C., A. D. Clare, J. W. Dalle Molle, and G. Persand. (2005). ‘‘A Comparison of Extreme Value Theory Approaches for Determining Value at Risk.’’ Journal of Empirical Finance 12:339–352.
3. LaBaron, B. and R. Samanta (2005). Extreme Value Theory and Fat Tails in Equity Markets, Working Paper, Brandeis University (SSRN Working Paper No. ssrn-id873656)
Volatility Models
GARCH
1. Christodoulakis, G. A. and S.E. Satchell (2007). Hashing GARCH: A Reassessment of Volatility Forecasting Performance, in J. Knight and S.
Satchell (eds) Forecating Volatility in the Financial Markets, Third Edition, Butterworth-Heinemann.
2. Hwang, S. and S.E. Satchell (2007). Implied Volatility Forecasting: A Comparison of different Procedures Including Fractionally Integrated Models with Applications to UK Equity Prices, in J. Knight and S. Satchell (eds) Forecating Volatility in the Financial Markets, Third Edition,
Butterworth-Heinemann.
3. Hansen, P. and J. Lund (2005). A FORECAST COMPARISON OF VOLATILITY MODELS: DOES ANYTHING BEAT A GARCH(1,1)?, Journal of Applied Econometrics, 20, 873-889.
4. Blair, B.j., S.H. Poon, S.J. Taylor (2001). Forecasting S&P 100 volatility:
the incremental information content of implied volatilities and high- frequency index returns, Journal of Econometrics, 105, 5-26.
5. Hyung, N, S.J. Poon, S.J. and C. Granger (2006) A Source of Long
Memory in Volatility, Working Paper, Department of Economics, University of Seoul.
6. Kuester, K, S. Mittnik, and M.S. Paolella (2006). Value-at-Risk Prediction:
A Comparison of Alternative Strategies, Journal of Financial Econometrics, 4(1), 53-89.
7. Rapach, David E., and Strauss, Jack K. (2008). “Structural Breaks and GARCH Models of Exchange Rate Volatility.” Journal of Applied
Econometrics, Vol. 23, No. 1 (January-February 2008), pp. 65-90
Stochastic Volatility
1. Shephard, N. (1996) “Statistical Aspects of ARCH and Stochastic
Volatility”, in: D. R. Cox, D. V. Hinkley and O. E. Barndorff-Nielsen (eds), Time Series Models in Econometrics, Finance and Other Fields, London:
Chapman & Hall.
High Frequency
1. Ait-Sahalia, Y, P. Mykland, and L. Zhang (2005). How Often to Sample a Continuous-time Process in the Presence of Market Microstructure Noise.
Review of Financial Studies 18, 351-416.
2. Andersen, T.G., T. Bollerslev, and F.X. Diebold (2007). Roughing It Up:
Including Jump Components in the Measurement, Modeling and
Forecasting of Return Volatility, NBER WP 11775, forthcoming in Review of Economics and Statistics.
3. Andersen, T.G., T. Bollerslev, F.X Diebold, and C. Vega (2003). Micro Effects of Macro Announcements: Real-time Price Discovery in Foreign Exchange. American Economic Review 93, 38-62.
4. Cornish, R. (2007). A Comparison of the Properties of Realized Variance for the FTSE 100 and FTSE 250 Equity Indices, in J. Knight and S.
Satchell (eds) Forecating Volatility in the Financial Markets, Third Edition, Butterworth-Heinemann.
5. Koopman, S. J., B. Jungbacker, and E. Hol. (2005). ‘‘Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realized and Implied Volatility Measurements.’’ Journal of Empirical Finance 12:445–
475.
6. Voev, V. (2004). Dynamic Modelling of Large Dimensional Covariance Matrices. Working Paper, University of Konstanz.
7. Hwang, and G. Tauchen (2005). The Relative Contribution of Jumps to Total Price Variation. Journal of Financial Econometrics, 3(4), 456-499.
8. Barndorff-Neilsen, O. And N. Shephard (2006). The Econometrics of Testing for Jumps in Financial Economics Using Bi-Power Variation, Journal of Financial Econometrics, 4, 1-30.
9. Hansen, P.R. and A Lunde (2006). Realized Variance and Market
Microstructure Noise (with discussion). Journal of Business and Economic Statistics, 24, 127-161.
