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A recent overview on financial and special time series models
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 Title & Authors
A recent overview on financial and special time series models
Hwang, S.Y.;
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 Abstract
Contrasted with the standard linear ARMA models, financial time series exhibits non-standard features such as fat-tails, non-normality, volatility clustering and asymmetries which are usually referred to as "stylized facts" in financial time series context (Terasvirta, 2009). We are accordingly led to ad hoc models (apart from ARMA) to accommodate stylized facts (Andersen et al., 2009). The paper aims to give a contemporary overview on financial and special time series models based on the recent literature and on the author`s publications. Various models are illustrated including asymmetric models, integer valued models, multivariate models and high frequency models. Selected statistical issues on the models are discussed, bringing some perspectives to the future works in this area.
 Keywords
financial time series;GARCH type models;stylized facts;
 Language
Korean
 Cited by
 References
1.
Andersen, T. G. and Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets, Journal of Empirical Finance, 4, 115-158. crossref(new window)

2.
Andersen, T. G., Davis, R. A., Kreiss, J.-P., and Mikosch, T. (2009). Handbook of Financial Time Series, Springer, Berlin.

3.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327. crossref(new window)

4.
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control, 3rd Ed., Prentice Hall, New Jersey.

5.
Choi, S. M., Hong, S. Y., Choi, M. S., Park, J. A., Baek, J. S., and Hwang, S. Y. (2009). Analysis of multivariate-GARCH via DCC modeling, Korean Journal of Applied Statistics, 22, 995-1005. crossref(new window)

6.
Chung, S. and Hwang, S. Y. (2016a). A profile Godambe information of power transformations for ARCH time series, to appear in Communications in Statistics-Theory and Methods.

7.
Chung, S. and Hwang, S. Y. (2016b). Stock return volatility based on intraday high frequency data: double-threshold ACD-GARCH Model, to appear in Korean Journal of Applied Statistics.

8.
Connor, G. (1995). Three types of factor models: a comparison of their explanatory power, Financial Analysis Journal, 51, 42-46.

9.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007. crossref(new window)

10.
Engle, R. F. and Ng, V. K. (1993). Measuring and testing the impact of news on volatility, Journal of Finance, 48, 1749-1778. crossref(new window)

11.
Engle, R. F. and Russell, J. R. (1998). Autoregressive conditional duration: a new model for irregularly spaced transaction data, Econometrica, 66, 1127-1162. crossref(new window)

12.
Ferland, R., Latour, A., and Oraichi, D. (2006). Integer-valued GARCH process, Journal of Time Series Analysis, 27, 923-942. crossref(new window)

13.
Fernandez, C. and Steel, M. F. J. (1998). On Bayesian modeling of fat tails and skewness, Journal of the American Statistical Association, 93, 359-371.

14.
Francq, C. and Zakoian, J. M. (2013). Optimal predictions of powers of conditionally heteroscedastic pro-cesses, Journal of Royal Statistical Society B, 75, 345-367. crossref(new window)

15.
Glosten, L. R., Jagannathan, R., and Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks, The Journal of Finance, 48, 1779-1801. crossref(new window)

16.
Godambe, V. P. (1985). The foundation of finite sample estimation in stochastic processes, Biometrika, 72, 419-428. crossref(new window)

17.
Grunwald, G. K., Hyndman, R. J., Tedesco, L., and Tweedie, R. L. (2000). Non-Gaussian conditional linear AR(1) models, Australian and New Zealand Journal of Statistics, 42, 479-495. crossref(new window)

18.
Hansen, P. R. and Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?, Journal of Applied Econometrics, 20, 873-889. crossref(new window)

19.
Heyde, C. C. (1997). Quasi-Likelihood and Its Application, Springer, New York.

20.
Hwang, S. Y., Baek, J. S., Park, J. A., and Choi, M. S. (2010). Explosive volatilities for threshold-GARCH processes generated by asymmetric innovations, Statistics & Probability Letters, 80, 26-33. crossref(new window)

21.
Hwang, S. Y. and Basawa, I. V. (2014). Martingale estimating functions for stochastic processes : A review toward a unifying tool, Contemporary Developments in Statistical Theory, edited by Lahiri et al., Springer, Switzerland, 9-28.

22.
Hwang, S. Y., Basawa, I. V., Choi, M. S., and Lee, S. D. (2014a). Non-ergodic martingale estimating functions and related asymptotics, Statistics, 48, 487-507. crossref(new window)

23.
Hwang, S. Y., Choi, M. S., and Yeo, I.-K. (2014b). Quasilikelihood and quasi maximum likelihood for GARCH-type processes: Estimating function approach, Journal of the Korean Statistical Society, 43, 631-641. crossref(new window)

24.
Jorion, P. (1997). Value at Risk: The New Benchmark for Controling Market Risk, McGraw-Hill, Chicago.

25.
Kim, H. and Lee, M. (2005). Econometric and Financial Time Series, Kyungmunsa, Seoul.

26.
Lai, T. L. and Xing, H. (2008). Statistical Models and Methods for Financial Markets, Springer, New York.

27.
Lee, J. W., Yoon, J. E., and Hwang, S. Y. (2013). A graphical improvement in volatility analysis for financial series, Korean Journal of Applied Statistics, 26, 785-796. crossref(new window)

28.
Li, W. K. (2004). Diagnostic Checks in Time Series, Chapman & Hall, New York.

29.
Linton, O. B. (2009). Semi-parametric and nonparametric ARCH modeling, in Handbook of Financial Time Series, 157-168, Eds., Andersen, T.G., Davis, R.A., Kreiss, J.-P. and Mikosch, T., Springer, Berlin.

30.
Park, J. A., Baek, J. S., and Hwang, S. Y. (2009). Persistent threshold-GARCH processes: model and application, Statistics & Probability Letters, 79, 907-914. crossref(new window)

31.
Song, E., Choi, M. S., and Hwang, S. Y. (2008). Volatility analysis for multivariate time series via dimension reduction, Communications of the Korean Statistical Society, 15, 825-835. crossref(new window)

32.
Straumann, D. (2005). Estimation in Conditionally Heteroscedastic Time Series Models, LNS No. 181, Springer, Berlin.

33.
Taylor, S. J. (1994). Modeling stochastic volatility: a review and comparative study, Mathematical Finance, 4, 183-204. crossref(new window)

34.
Terasvirta, T. (2009). An introduction to univariate GARCH models, In Handbook of Financial Time Series, 17-42, Eds., Andersen, T.G., Davis, R.A., Kreiss, J.-P. and Mikosch, T., Springer, Berlin.

35.
Tong, H. (1990). Nonlinear Time Series, Oxford University Press, Oxford.

36.
Tsay, R. S. (2010). Analysis of Financial Time Series, Third Ed. Wiley, New York.

37.
Wei, W. W. S. (2006). Time Series Analysis, 2nd Ed., Pearson, New York

38.
Xiao, L. (2013). Realized volatility forecasting: empirical evidence from stock market indices and exchange rates, Applied Financial Economics, 23, 57-69. crossref(new window)

39.
Yoon, J. E. and Hwang, S. Y. (2015a). Zero-inflated INGARCH using conditional Poisson and negative binomial: data application, Korean Journal of Applied Statistics, 28, 583-592. crossref(new window)

40.
Yoon, J. E. and Hwang, S. Y. (2015b). Volatility computations for financial time series: high frequency and hybrid method, Korean Journal of Applied Statistics, 28, 1163-1170. crossref(new window)

41.
Zhu, F. (2011). A negative binomial integer-valued GARCH model, Journal of Time Series Analysis, 32, 54-67. crossref(new window)

42.
Zhu, F. (2012). Zero-inflated Poisson and negative binomial integer-valued GARCH models, Journal of Statistical Planning and Inference, 142, 826-839. crossref(new window)

43.
Zivot, E. (2009). Practical issues in the analysis of univariate GARCH models, In Handbook of Financial Time Series, 113-155, Eds., Andersen, T.G., Davis, R.A., Kreiss, J.-P. and Mikosch, T., Springer, Berlin.