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Analyzing MSCI Global Healthcare Return and Volatility with Structural Change Based on Residual CUSUM GARCH Approach

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Causal Inference in Econometrics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 622))

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Abstract

This study aims to analyze the Morgan Stanley Capital International (MSCI) world return and volatility of the healthcare price index using daily time series data. Since the data of MSCI healthcare returns cannot be described by linear models, the residual CUSUM GARCH(1,1) model is applied in this paper. The CUSUM test is used to estimate the optimal change point. The findings of this paper are (1) the estimated point is at day 1,201 of the entire daily data set of 4,209 observations; (2) if the change point is not taken into consideration, the estimated parameters of GARCH(1,1) become \(\hat{\gamma }_1+\hat{\beta }_1 \approx 1\), i.e., we encounter the “IGARCH effect”, which leads to an infinite variance for a model. The contribution of this paper is the recommendation for the analysis of the change point as the necessary condition, rather than jumping into using the whole data set to estimate all parameters of the model without testing nonlinearity, especially for financial time series data.

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Notes

  1. 1.

    Developed markets countries are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the UK, and the US.

  2. 2.

    Visit https://www.msci.com/index-methodology.

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Acknowledgments

We are thankful to Professor Sangyeol Lee from Seoul National University, Department of Statistics, who inspired us by giving us the great idea of doing this research. Additionally, we then extend our gratitude to Miss Young Mai Lee, Professor Sangyeol Lee’s Ph.D. student, who designed the computational part correctly. We greatly appreciate the referees comments to improve our paper substantially. Furthermore, we are indebted to Professor Thierry Denoeux from Universit\(\acute{e}\) Technologie de Compi\(\grave{e}\)gne, who took the time to read this paper. Moreover, we are grateful to Lhoyd Castillo, the Customer Support Executive, Investment Management, from Thomson Reuters, who provided significant data for this work. Many thanks are extended to Puay Ungphakorn Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University.

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Authors and Affiliations

  1. Faculty of Management Science, Suratthani Rajabhat University, Surat Thani, 84100, Thailand

    Nantiworn Thianpaen

  2. Faculty of Economics, Chiang Mai University, Chiang Mai, 52000, Thailand

    Nantiworn Thianpaen & Songsak Sriboonchitta

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  1. Nantiworn Thianpaen
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  2. Songsak Sriboonchitta
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Correspondence to Nantiworn Thianpaen .

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Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Ins. of Sci. & Tech, Ishikawa, Japan

    Van-Nam Huynh

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Faculty of Economics, Chiang Mai University, Chiangmai, Thailand

    Songsak Sriboonchitta

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Thianpaen, N., Sriboonchitta, S. (2016). Analyzing MSCI Global Healthcare Return and Volatility with Structural Change Based on Residual CUSUM GARCH Approach. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S. (eds) Causal Inference in Econometrics. Studies in Computational Intelligence, vol 622. Springer, Cham. https://doi.org/10.1007/978-3-319-27284-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-27284-9_24

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