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Modelling Financial Time Series

Author

Listed:
  • Stephen J Taylor

    (Lancaster University, UK)

Abstract

This book contains several innovative models for the prices of financial assets. First published in 1986, it is a classic text in the area of financial econometrics. It presents ARCH and stochastic volatility models that are often used and cited in academic research and are applied by quantitative analysts in many banks. Another often-cited contribution of the first edition is the documentation of statistical characteristics of financial returns, which are referred to as stylized facts.

Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Stephen J Taylor, 2007. "Modelling Financial Time Series," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6578, January.
    • Handle: RePEc:wsi:wsbook:6578
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    File URL: https://www.worldscientific.com/worldscibooks/10.1142/6578
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    Citations

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    Cited by:

    1. Chang, Kai & Pei, Ping & Zhang, Chao & Wu, Xin, 2017. "Exploring the price dynamics of CO2 emissions allowances in China's emissions trading scheme pilots," Energy Economics, Elsevier, vol. 67(C), pages 213-223.
    2. Anders Eriksson & Daniel P. A. Preve & Jun Yu, 2019. "Forecasting Realized Volatility Using a Nonnegative Semiparametric Model," JRFM, MDPI, vol. 12(3), pages 1-23, August.
    3. Vicente Medina & Angel Pardo, 2013. "Is the EUA a new asset class?," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 637-653, March.
    4. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 25-41.
    5. Daan Steenkamp & Henk Janse van Vuuren & Rossouw van Jaarsveld & Roy Havemann, 2022. "The bond market impact of the South African Reserve Bank bond purchase programme," Working Papers 876, Economic Research Southern Africa.
    6. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2022. "An Alternative Estimation Method for Time-Varying Parameter Models," Econometrics, MDPI, vol. 10(2), pages 1-27, April.
    7. Kuang-Ting Chen, 2015. "Modeling Market Inefficiencies within a Single Instrument," Papers 1511.02046, arXiv.org.
    8. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54030, University Library of Munich, Germany.
    9. Vicente Medina Martínez & Ángel Pardo Tornero, 2012. "Stylized facts of CO2 returns," Working Papers. Serie AD 2012-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    10. Bernardi, Mauro & Maruotti, Antonello & Petrella, Lea, 2017. "Multiple risk measures for multivariate dynamic heavy–tailed models," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 1-32.
    11. Xi Zhang & Jiawei Shi & Di Wang & Binxing Fang, 2018. "Exploiting Investors Social Network for Stock Prediction in China's Market," Papers 1801.00597, arXiv.org.
    12. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    13. Victoria Lemieux & Payam S. Rahmdel & Rick Walker & B.L. William Wong & Mark D. Flood, 2015. "Clustering Techniques and Their Effect on Portfolio Formation and Risk Analysis," Staff Discussion Papers 15-01, Office of Financial Research, US Department of the Treasury.
    14. Galli, Fausto, 2014. "Stochastic conditonal range, a latent variable model for financial volatility," MPRA Paper 54841, University Library of Munich, Germany.

    Book Chapters

    The following chapters of this book are listed in IDEAS

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