1 |
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.
DOI
|
2 |
Straumann, D. (2005). Estimation in Conditionally Heteroscedastic Time Series Models, LNS No. 181, Springer, Berlin.
|
3 |
Taylor, S. J. (1994). Modeling stochastic volatility: a review and comparative study, Mathematical Finance, 4, 183-204.
DOI
|
4 |
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.
|
5 |
Tong, H. (1990). Nonlinear Time Series, Oxford University Press, Oxford.
|
6 |
Tsay, R. S. (2010). Analysis of Financial Time Series, Third Ed. Wiley, New York.
|
7 |
Wei, W. W. S. (2006). Time Series Analysis, 2nd Ed., Pearson, New York
|
8 |
Xiao, L. (2013). Realized volatility forecasting: empirical evidence from stock market indices and exchange rates, Applied Financial Economics, 23, 57-69.
DOI
|
9 |
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.
DOI
|
10 |
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.
DOI
|
11 |
Zhu, F. (2011). A negative binomial integer-valued GARCH model, Journal of Time Series Analysis, 32, 54-67.
DOI
|
12 |
Zhu, F. (2012). Zero-inflated Poisson and negative binomial integer-valued GARCH models, Journal of Statistical Planning and Inference, 142, 826-839.
DOI
|
13 |
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.
|
14 |
Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994). Time Series Analysis: Forecasting and Control, 3rd Ed., Prentice Hall, New Jersey.
|
15 |
Andersen, T. G. and Bollerslev, T. (1997). Intraday periodicity and volatility persistence in financial markets, Journal of Empirical Finance, 4, 115-158.
DOI
|
16 |
Andersen, T. G., Davis, R. A., Kreiss, J.-P., and Mikosch, T. (2009). Handbook of Financial Time Series, Springer, Berlin.
|
17 |
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, 307-327.
DOI
|
18 |
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.
DOI
|
19 |
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.
|
20 |
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.
|
21 |
Connor, G. (1995). Three types of factor models: a comparison of their explanatory power, Financial Analysis Journal, 51, 42-46.
|
22 |
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.
DOI
|
23 |
Engle, R. F. and Ng, V. K. (1993). Measuring and testing the impact of news on volatility, Journal of Finance, 48, 1749-1778.
DOI
|
24 |
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.
DOI
|
25 |
Engle, R. F. and Russell, J. R. (1998). Autoregressive conditional duration: a new model for irregularly spaced transaction data, Econometrica, 66, 1127-1162.
DOI
|
26 |
Ferland, R., Latour, A., and Oraichi, D. (2006). Integer-valued GARCH process, Journal of Time Series Analysis, 27, 923-942.
DOI
|
27 |
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.
|
28 |
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.
DOI
|
29 |
Godambe, V. P. (1985). The foundation of finite sample estimation in stochastic processes, Biometrika, 72, 419-428.
DOI
|
30 |
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.
DOI
|
31 |
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.
DOI
|
32 |
Heyde, C. C. (1997). Quasi-Likelihood and Its Application, Springer, New York.
|
33 |
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.
DOI
|
34 |
Jorion, P. (1997). Value at Risk: The New Benchmark for Controling Market Risk, McGraw-Hill, Chicago.
|
35 |
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.
|
36 |
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.
DOI
|
37 |
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.
DOI
|
38 |
Kim, H. and Lee, M. (2005). Econometric and Financial Time Series, Kyungmunsa, Seoul.
|
39 |
Lai, T. L. and Xing, H. (2008). Statistical Models and Methods for Financial Markets, Springer, New York.
|
40 |
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.
DOI
|
41 |
Li, W. K. (2004). Diagnostic Checks in Time Series, Chapman & Hall, New York.
|
42 |
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.
|
43 |
Park, J. A., Baek, J. S., and Hwang, S. Y. (2009). Persistent threshold-GARCH processes: model and application, Statistics & Probability Letters, 79, 907-914.
DOI
|