Financial Time Series -Recent Trends in Econometrics

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Amaresh Das
Amaresh Das
1 University of New Orleans

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GJMBR Volume 13 Issue C5

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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.

18 Cites in Articles

References

  1. Torben Andersen,Tim Bollerslev,Francis Diebold,Heiko Ebens (2003). The Distribution of Stock Return Volatility.
  2. T Anderson,T Bolleslev,F Diebold,J Wu (2006). Realized Beta: Persistence and Predictability Advances in Econometrics.
  3. T Anderson,J Lund (1997). Estimating Continuous Time Stochastic Volatility Models of the Short-term Interest Rate.
  4. O Barndorff -Nielson,N Shephard (2002). Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models.
  5. Ole Barndorff-Nielsen,Neil Shephard (2001). Non-Gaussian Ornstein–Uhlenbeck-based Models and Some of Their Uses in Financial Economics.
  6. J Diebold F Xand Nason (1990). Non Parametric Exchange Rate Prediction.
  7. Carla Lopez,Chhinder Sodhi,David Hackam (1998). A surprising role for enteroendocrine cells and GLP-1 in regulating intestinal inflammation..
  8. Robert Engle (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.
  9. J Farmer,John Sidorowich (1987). Predicting chaotic time series.
  10. Fruhwirth -Schnatter S Markov Chains Estimation of Classical and Dynamic Switching and Mixture Models.
  11. C Granger,R Joyeux (1980). An Introduction to Long Memory Time Series Models and Financial Differencing Journal of the Time Series Analysis.
  12. Peter Lewis,Bonnie Ray (1994). NONLINEAR MODELING OF MULTIVARIATE AND CATEGORICAL TIME SERIES USING MULTIVARIATE ADAPTIVE REGRESSION SPLINES.
  13. Andrew Lo,A Mackinlay (1998). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test.
  14. Thomas Mikosch,Daniel Straumann (2002). Whittle estimation in a heavy-tailed GARCH(1,1) model.
  15. N Packard,J Crutchfield,J Farmer,R Shaw (1980). Geometry from a Time Series.
  16. M Pesaron,S Potter (1908). Unknown Title.
  17. J Rosinski (2001). Series Representation of Levy Process from the Perspective of Point Process in O T Barndorff -Nielson.
  18. (1995). Chaos and Nonlinear Dynamics in the Financial Market: Theory, Evidence and Applications.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Amaresh Das. 2013. \u201cFinancial Time Series -Recent Trends in Econometrics\u201d. Global Journal of Management and Business Research - C: Finance GJMBR-C Volume 13 (GJMBR Volume 13 Issue C5): .

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GJMBR Volume 13 Issue C5
Pg. 13- 18
Journal Specifications

Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

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v1.2

Issue date

May 21, 2013

Language

English

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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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Financial Time Series -Recent Trends in Econometrics

Amaresh Das
Amaresh Das University of New Orleans

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