Non-linear modelling and forecasting of S&P 500 volatility

被引：10
|
作者：
Verhoeven, P [1 ]
Pilgram, B
McAleer, M
Mees, A
机构：
[1] Curtin Univ Technol, Sch Econ & Finance, Perth, WA 6102, Australia
[2] Univ Western Australia, Dept Math & Stat, Perth, WA 6907, Australia
[3] Univ Western Australia, Dept Econ, Perth, WA 6907, Australia
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2002年 / 59卷 / 1-3期
关键词：
non-linear Markov modelling; non-parametric model; parametric model; volatility forecasting;
D O I：
10.1016/S0378-4754(01)00411-6
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper investigates the use of a flexible forecasting method based on non-linear Markov modelling and canonical variate analysis, and the use of a prediction algorithm to forecast conditional volatility. We assess the dynamic behaviour of the model by forecasting volatility of a stock index. It is found that the non-linear non-parametric model based on canonical variate analysis forecasts stock index volatility significantly better than the GJR-GARCH(1, 1)-t model due to the flexibility in accommodating multiple dynamic patterns in volatility which are not captured by its parametric counterpart. (C) 2002 IMACS. Published by Elsevier Science B.V. All fights reserved.
引用
收藏
页码：233 / 241
页数：9
相关论文
共 50 条
  • [1] A hybrid modeling approach for forecasting the volatility of S&P 500 index return
    Hajizadeh, E.
    Seifi, A.
    Zarandi, M. N. Fazel
    Turksen, I. B.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (01) : 431 - 436
  • [2] S&P 500 volatility, volatility regimes, and economic uncertainty
    Adrangi, Bahram
    Chatrath, Arjun
    Raffiee, Kambiz
    [J]. BULLETIN OF ECONOMIC RESEARCH, 2023, 75 (04) : 1362 - 1387
  • [3] Using neural networks for forecasting volatility of S&P 500 Index futures prices
    Hamid, SA
    Iqbal, Z
    [J]. JOURNAL OF BUSINESS RESEARCH, 2004, 57 (10) : 1116 - 1125
  • [4] The Information Contents of VIX Index and Range-based Volatility on Volatility Forecasting Performance of S&P 500
    Hung, Jui-Cheng
    Ni, Ren-Xi
    Chang, Matthew C.
    [J]. ECONOMICS BULLETIN, 2009, 29 (04): : 2592 - 2604
  • [5] The dynamics of the S&P 500 implied volatility surface
    Skiadopoulos G.
    Hodges S.
    Clewlow L.
    [J]. Review of Derivatives Research, 2000, 3 (3) : 263 - 282
  • [6] Forecasting S&P 500 spikes: an SVM approach
    Theophilos Papadimitriou
    Periklis Gogas
    Athanasios Fotios Athanasiou
    [J]. Digital Finance, 2020, 2 (3-4): : 241 - 258
  • [7] Forecasting with Deep Learning: S&P 500 index
    Kamalov, Firuz
    Smail, Linda
    Gurrib, Ikhlaas
    [J]. 2020 13TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2020), 2020, : 422 - 425
  • [8] The jump component of S&P 500 volatility and the VIX index
    Becker, Ralf
    Clements, Adam E.
    McClelland, Andrew
    [J]. JOURNAL OF BANKING & FINANCE, 2009, 33 (06) : 1033 - 1038
  • [9] Are combination forecasts of S&P 500 volatility statistically superior?
    Becker, Ralf
    Clements, Adam E.
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2008, 24 (01) : 122 - 133
  • [10] Non-linear dependence modelling with bivariate copulas: statistical arbitrage pairs trading on the S&P 100
    Krauss, Christopher
    Stuebinger, Johannes
    [J]. APPLIED ECONOMICS, 2017, 49 (52) : 5352 - 5369
12345