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Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data

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  • Rituparna Sen

    (Indian Statistical Institute, Chennai Centre)

  • Pulkit Mehrotra

    (HSBC Analytics)

Abstract

Recent advancements in technology have led to wide availability of high-frequency financial data. The aim of this paper is to study the behavior of the Indian stock market. In particular, we analyze the returns at 5 min interval from NSE using the index NIFTY and the stocks State Bank of India and Infosys. A non-parametric approach is taken to detect jumps in the return process. The analysis shows that index jumps relate very closely with the general market news and announcements while individual stock jumps are associated with company specific news. We find that volatility of the market is best captured by asymmetric power ARCH models.

Suggested Citation

  • Rituparna Sen & Pulkit Mehrotra, 2016. "Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 137-150, June.
    • Handle: RePEc:spr:jqecon:v:14:y:2016:i:1:d:10.1007_s40953-016-0028-5
    DOI: 10.1007/s40953-016-0028-5
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    Cited by:

    1. Xin Yang & Shan Chen & Hong Liu & Xiaoguang Yang & Chuangxia Huang, 2023. "Jump volatility spillover network based measurement of systemic importance of Chinese financial institutions," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1201-1213, April.

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    More about this item

    Keywords

    High frequency financial data; Realized volatility; Jump detection; Asymmetric power ARCH;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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