Modeling of Volatility with Non-linear Time Series Model

Preprint OPEN
Kim Song Yon ; Kim Mun Chol (2013)
  • Subject: Mathematics - Statistics Theory | Mathematics - Probability | Quantitative Finance - Statistical Finance
    arxiv: Statistics::Theory | Statistics::Methodology

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Cond... View more
  • References (9)

    [1] F. Black, M. Scholes, The pricing of options and corporate liabilities, Journal of Political Economy, 81(3) (1973), 637-654.

    [2] T. Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3) (1986), 307-327.

    [3] R.F. Engle, Risk and volatility: Economic models and financial practice, Nobel lecture, Stockholm, December 8, 29, 2003.

    [4] L.R. Glosten, R. Jagannathan, D.E. Runkle, On the relation between the expected value and the volatility of the nominal excess return on stocks, Journal of Finance, 48(5) (1993), 1779-1801.

    [5] W. Ha¨rdle, T. Kleinow, G. Stahl, Applied quantitative finance: Theory and computational tools, Springer, 2002.

    [6] S.Y. Kim, Threshold AR with Asymmetric ARCH type Error (in Korean), Dissertation for ph D, 2007.

    [7] S.Y. Kim, M.C. Kim, The identification of thresholds and time delay in selfexciting TAR model by wavelet, International Symposium in Commemoration of the 65th Anniversary of the Foundation of Kim Il Sung University (Mathematics), 20-21. Sep. Juche100 (2011) Pyongyang, DPR Korea, arXiv 1303.4867 [math-ph].

    [8] D.B. Nelson, Conditional heteroskedasticity in assert returns: a new approach, Econometrica, 59(2) (1991), 347-370.

    [9] H. Tong, Non-linear time series: A dynamical system approach, Clarendon Press, Oxford, 1990.

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