Financial Time Series - Recent Trends in Econometrics
Keywords:
high frequency data, chaos, stochastic volatility, brownian motion, markov process, garch process
Abstract
The paper points to a coverage of the latest research techniques and findings relating to the econometric analysis of financial markets. It contains a wealth of new materials reflecting the developments during the last decade or so. Particular attention is paid to the wide range of nonlinear models that are used to analyze financial data observed at high frequencies and to the long memory characteristics found in financial time series. There is also a discussion, briefly, of the treatment of volatility, chaos, the Fed model, stochastic estimation and Bayesian estimation, the Fed model and tail dependent time series models.
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References
T. Anderson, T. Bolleslev, Diebold X, H. Ebens (2003) The Distribution of Stock Return Volatility. 61(2), 43-76. https://doi.org/10.3386/w7933
T. Anderson, T. Bolleslev, Diebold X, J. Wu (2006) Realized Beta: Persistence and Predictability Advances in Econometrics. 20, 1-39.
T. Anderson, J. Lund (1997) Estimating continuous-time stochastic volatility models of the short-term interest rate. 77(2), 343-377. https://doi.org/10.1016/s0304-4076(96)01819-2
O. Barndorff - Nielson, N. Shephard (2002) Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models J of Royal Statistical Society. 64(2), 253-280. https://doi.org/10.1111/1467-9868.00336
O. Barndorff - Nielson, Shephard N (2001) Non-Gaussian Ornstein-Uhlenbeck-based Models and Some of Their Uses in Financial Economics. 63(2), 167-241. https://doi.org/10.1111/1467-9868.00282
Black F, Scholes M (1973) The Pricing of Options and Corporate Liabilities. 8(3), 1637-1659.
Zongwu Cai, Rong Chen (2006) Flexible Seasonal Time Series Models, in Thomas B. Fomby, Dek Terrell (eds.) Econometric Analysis of Financial and Economic Time Series Advances in Econometrics. 63-87.
J. Danielsson (2002) The emperor has no clothes: Limits to risk modelling. 26(7), 1273-1296. https://doi.org/10.1016/s0378-4266(02)00263-7
J. Diebold F Xand Nason (1990) Non Parametric Exchange Rate Prediction Journal of International Economics. 28, 315-332.
H. Ebens (1998) Realized Stock Volatility Working Paper.
Robert Engle (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. 50(4), 987. https://doi.org/10.2307/1912773
D. Famer, J. Sidorowitch (1987) Predicting Chaotic Time Series. 59(3), 845-948.
Fruhwirth - Schnatter Fruhwirth - Schnatter, S Markov Chains Estimation of Classical and Dynamic Switching and Mixture Models, Journal of American Statistical Association, Vol96, (3). 96, 194-209.
C. Granger, R. Joyeux (1980) AN INTRODUCTION TO LONG‐MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING. 1.
P. Lewis, Ray Stevens, J. (1994) Modeling Time Series Using Multivariate Adaptive Regression Splines (MARS) in A J Weigend and N A Gershenfeld (eds) Time Series Prediction: Forecasting the Futureand Understanding the Past, Addison- Wesley, Reading, MA.
Andrew Lo, A. Mackinlay (1998) Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. 1(1), 41-66. https://doi.org/10.1093/rfs/1.1.41
Thomas Mikosch, Daniel Straumann (2002) Whittle estimation in a heavy-tailed GARCH(1,1) model. 100(1-2), 187-222. https://doi.org/10.1016/s0304-4149(02)00097-2
N. Plackard, J. Crutchfield, J. Farmer, R. Shaw (1980) Geometry from a Time Series. 45.
M. Pesaron, S. Potter (1993) Nonlinear Dynamics, Chaos and Econometrics.
J. Rosinski (2001) Series Representation of Levy Process from the Perspective of Point Process in O T Barndorff - Nielson, T Mikoschk, LResnick (eds) Levy Process: Theory and Application, Birkhauser.
R. Trippi (1995) Chaos and Nonlinear Dynamics in the Financial Market: Theory, Evidence and Applications, Irwin, Chikago.
P. Zhang L Mykland, At, Y. (2005) A Tale of Two Time Scales Determining Integrated Volatility with Noisy High Frequency Data Journal of American Statistical Association Vol, (2). 1394-1411.
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2013-04-04
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