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Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)

Author

Listed:
  • Kumar SANTOSH

    (Department of Commerce, Darshan Sah College, Katihar (Under Purnea University, Purnia), India)

  • Meher Kumar BHARAT

    (PG Department of Commerce, Purnea University, Purnia, Bihar, India)

  • Ramona BIRAU

    (Faculty of Economic Science, University Constantin Brancusi, Targu-Jiu, Romania)

  • Mircea Laurentiu SIMION

    (University of Craiova, Doctoral School of Economic Sciences, Craiova, Romania)

  • Anand ABHISHEK

    (PG Department of Economics, Purnea University, Purnia, Bihar, India)

  • Singh MANOHAR

    (Department of Commerce, Government Autonomous PG College, Chhindwara, Madhya Pradesh)

Abstract

High frequency data is a recent entrant to the world of statistics as they relate to the markets. This study measures the volatility of S& P / Toronto index by utilizing the GARCH family models (EGARCH, TGARCH, MGARCH and PGARCH models) using a daily database counted 7636 observations. The empirical results for the selected index for the whole-time frame show that it is volatile, and the PGARCH is a better-fitted model with the student's t distribution. Positive shocks have a greater impact on conditional volatility than negative shocks, according to the data. These extra analyses can be supplied to further corroborate their results and provide useful information for economic thespians interested in adding Toronto stock market index to their investment portfolios.

Suggested Citation

  • Kumar SANTOSH & Meher Kumar BHARAT & Ramona BIRAU & Mircea Laurentiu SIMION & Anand ABHISHEK & Singh MANOHAR, 2023. "Quantifying Long-Term Volatility for Developed Stock Markets: An Empirical Case Study Using PGARCH Model on Toronto Stock Exchange (TSX)," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-68.
    • Handle: RePEc:ddj:fseeai:y:2023:i:2:p:61-68
    DOI: 10.35219/eai15840409338
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    as
    1. Manera, Matteo & Nicolini, Marcella & Vignati, Ilaria, 2013. "Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation," Energy: Resources and Markets 151372, Fondazione Eni Enrico Mattei (FEEM).
    2. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Santosh Kumar & Abhishek Kumar Gupta, 2022. "Modelling Market Indices, Commodity Market Prices and Stock Prices of Energy Sector using VAR with Variance Decomposition Model," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 122-130, July.
    3. Esmaiel Abounoori & Mohammad Amin Zabol, 2020. "Modeling Gold Volatility: Realized GARCH Approach," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 24(1), pages 299-311, Winter.
    4. CHIA-LIN CHANG & MICHAEL McALEER & ROENGCHAI TANSUCHAT, 2012. "Modelling Long Memory Volatility In Agricultural Commodity Futures Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-27.
    5. Bharat Kumar Meher & Iqbal Thonse Hawaldar & Latasha Mohapatra & Adel M. Sarea, 2020. "The Impact of COVID-19 on Price Volatility of Crude Oil and Natural Gas Listed on Multi Commodity Exchange of India," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 422-431.
    6. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    7. Ghulam Ali, 2013. "EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 2(3), pages 1-6.
    8. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    9. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    10. Ormos, Mihály & Timotity, Dusan, 2016. "Unravelling the asymmetric volatility puzzle: A novel explanation of volatility through anchoring," Economic Systems, Elsevier, vol. 40(3), pages 345-354.
    11. Isita Mukherjee & Bhaskar Goswami, 2017. "The volatility of returns from commodity futures: evidence from India," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-23, December.
    12. HaiYue Liu & Aqsa Manzoor & CangYu Wang & Lei Zhang & Zaira Manzoor, 2020. "The COVID-19 Outbreak and Affected Countries Stock Markets Response," IJERPH, MDPI, vol. 17(8), pages 1-19, April.
    13. Helen Higgs & Andrew C. Worthington, 2005. "Systematic Features of High-Frequency Volatility in Australian Electricity Markets: Intraday Patterns, Information Arrival and Calendar Effects," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 23-42.
    14. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
    15. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    16. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    17. Thakolsri, Supachok & Sethapramote, Yuthana & Jiranyakul, Komain, 2015. "Asymmetric volatility of the Thai stock market: evidence from high-frequency data," MPRA Paper 67181, University Library of Munich, Germany.
    18. Cristi Spulbar & Ramona Birau & Iqbal Thonse Hawaldar & Jatin Trivedi & Anca Ioana Iacob (Troto), 2023. "Measuring Asymmetric Volatility Of Uk, France, And German Stock Markets," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 134-146, February.
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    1. Santosh KUMAR & Bharat Kumar MEHER & Ramona BIRAU & Abhishek ANAND & Mircea Laurentiu SIMION, 2023. "Investigating Volatility Dynamics of the Portugal Stock Market using FIGARCH Models," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 3, pages 39-45.

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