IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v3y2017i1d10.1186_s40854-017-0067-8.html
   My bibliography  Save this article

Derived signals for S & P CNX nifty index futures

Author

Listed:
  • B. Prasanna Kumar

    (Davangere University P. G. Centre, Guddadarangavvanhalli)

Abstract

Background The financial futures market in India is relatively new. The major advantage of derivatives as financial products is that their use minimizes the risks associated with securities. However, hedging effectiveness requires understanding key market signals such as trading margins, credit availability, and price discreteness. Methods This study considers the Standard & Poor’s CNX Nifty 50 Index futures for data analysis with the application of V-IGARCH (1, 1) two-stage model. The purpose for V-IGARCH (1, 1) is used to observe the positive effects of credit availability on the variance of futures returns. The first stage V-IGARCH (1, 1) endogenous mean and conditional variance returns are measured with exogenous factors from the second stage V-IGARCH (1, 1) models. The second stage V-IGARCH (1, 1) models specify the market participants’ exogenous conditional probabilistic values for returns. Results In the first stage, it was observed that returns and trading margins, as well as credit availability, were cointegrated, thereby indicating a long-term relationship between them. In the first stage of the V-IGARCH (1, 1) model, heteroscedasticity with the mean returns through residuals was observed, where the estimated coefficients were negative. This finding indicated that maximizing returns requires efficient use of trading margins as well as availability of credit positions. From the second stage regression estimation, it was observed that trading prices and total money supply were directly related, and thus had direct effects on returns. The total money supply increased gradually until the last trading hour. In the conditional variance equation, total money supply was related negatively to the availability of credit for market participants. Under these circumstances, the efficient interbank call interest rate was necessary to maintain the trading margin. In effect, efficient Nifty returns would be achieved. Conclusions This study found that trading margins, credit availability, and price discreteness affect the variance of returns in the Indian futures markets. The study also found that market participation was inadequate as a result of endogenous and exogenous conditional probabilistic reasons. Efficient trading margins and effective credit availability positions were not realized. Price discreteness had a negative impact on returns, as trading prices and credit availability in each of the trading hours were inversely related. Trading risks, and hence losses, were not minimized by hedging positions. The monopoly power in the Nifty market was 8.9526. Given this monopoly power, returns were less elastic with respect to the existing trading margins, financial resources, and market microstructure (price discreteness) that were available for reinvestment. Therefore, before investing in derivatives (index futures), market investors should evaluate trading margins, credit availability positions, and price discreteness. Through these signals, investors will be able to gain essential market knowledge and participate accordingly in trading for efficient returns.

Suggested Citation

  • B. Prasanna Kumar, 2017. "Derived signals for S & P CNX nifty index futures," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-22, December.
    • Handle: RePEc:spr:fininn:v:3:y:2017:i:1:d:10.1186_s40854-017-0067-8
    DOI: 10.1186/s40854-017-0067-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-017-0067-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-017-0067-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. John R. Graham & Roni Michaely & Michael R. Roberts, 2003. "Do Price Discreteness and Transactions Costs Affect Stock Returns? Comparing Ex‐Dividend Pricing before and after Decimalization," Journal of Finance, American Finance Association, vol. 58(6), pages 2611-2636, December.
    2. Sandra E. Black & Philip E. Strahan, 2002. "Entrepreneurship and Bank Credit Availability," Journal of Finance, American Finance Association, vol. 57(6), pages 2807-2833, December.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    5. Barik Kumar & M. Supriya, 2014. "Evidence on Hedging Effectiveness in Indian Derivatives Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 21(2), pages 121-131, May.
    6. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    8. Kathy Yuan, 2005. "Asymmetric Price Movements and Borrowing Constraints: A Rational Expectations Equilibrium Model of Crises, Contagion, and Confusion," Journal of Finance, American Finance Association, vol. 60(1), pages 379-411, February.
    9. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    10. van Tassel, Eric, 2002. "Signal Jamming in New Credit Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 469-490, May.
    11. Frank, Murray & Jagannathan, Ravi, 1998. "Why do stock prices drop by less than the value of the dividend? Evidence from a country without taxes," Journal of Financial Economics, Elsevier, vol. 47(2), pages 161-188, February.
    12. Jakob, Keith & Ma, Tongshu, 2004. "Tick size, NYSE rule 118, and ex-dividend day stock price behavior," Journal of Financial Economics, Elsevier, vol. 72(3), pages 605-625, June.
    13. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruwei Zhao & Xiong Xiong & Dehua Shen & Wei Zhang, 2019. "Investor Structure and Stock Price Crash Risk in a Continuous Double Auction Market: An Agent-Based Perspective," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 695-715, March.
    2. Parizad Phiroze Dungore & Sarosh Hosi Patel, 2021. "Analysis of Volatility Volume and Open Interest for Nifty Index Futures Using GARCH Analysis and VAR Model," IJFS, MDPI, vol. 9(1), pages 1-11, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barik Kumar & M. Supriya, 2014. "Evidence on Hedging Effectiveness in Indian Derivatives Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 21(2), pages 121-131, May.
    2. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, November.
    3. Committee, Nobel Prize, 2003. "Time-series Econometrics: Cointegration and Autoregressive Conditional Heteroskedasticity," Nobel Prize in Economics documents 2003-1, Nobel Prize Committee.
    4. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, November.
    5. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, December.
    6. Haigh, Michael S. & Bryant, Henry L., 2000. "Price And Price Risk Dynamics In Barge And Ocean Freight Markets And The Effects On Commodity Trading," 2000 Conference, April 17-18 2000, Chicago, Illinois 18934, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    7. Tim Bollerslev & Robert J. Hodrick, 1992. "Financial Market Efficiency Tests," NBER Working Papers 4108, National Bureau of Economic Research, Inc.
    8. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, January.
    9. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038, Elsevier.
    10. Sugra Humbatova, 2023. "The Impact of Oil Prices on State Budget Income and Expenses: Case of Azerbaijan," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 189-212, January.
    11. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    12. Rittler, Daniel, 2009. "Price Discovery, Causality and Volatility Spillovers in European Union Allowances Phase II: A High Frequency Analysis," Working Papers 0492, University of Heidelberg, Department of Economics.
    13. Stelios D. Bekiros, 2013. "Decoupling and the Spillover Effects of the US Financial Crisis: Evidence from the BRIC Markets," Working Paper series 21_13, Rimini Centre for Economic Analysis.
    14. Tai-Liang Chen & Ching-Hsue Cheng & Jing-Wei Liu, 2019. "A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1967-1987, November.
    15. Dongweí Su, 2003. "Risk, Return and Regulation in Chinese Stock Markets," World Scientific Book Chapters, in: Chinese Stock Markets A Research Handbook, chapter 3, pages 75-122, World Scientific Publishing Co. Pte. Ltd..
    16. Isabel Cortés-Jiménez & Manuel Artís, 2005. "The role of the tourism sector in economic development - Lessons from the Spanish experience," ERSA conference papers ersa05p488, European Regional Science Association.
    17. M. T. Alguacil & V. Orts, 2003. "Inward Foreign Direct Investment and Imports in Spain," International Economic Journal, Taylor & Francis Journals, vol. 17(3), pages 19-38.
    18. Torben G. Andersen & Tim Bollerslev & Peter Christoffersen & Francis X. Diebold, 2007. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Chapters, in: The Risks of Financial Institutions, pages 513-544, National Bureau of Economic Research, Inc.
    19. Ahmed, Khalid, 2015. "The sheer scale of China’s urban renewal and CO2 emissions: Multiple structural breaks, long-run relationship and short-run dynamics," MPRA Paper 71035, University Library of Munich, Germany.
    20. Osamah M. Al-Khazali, 2003. "Stock Prices, Inflation, and Output: Evidence from the Emerging Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 2(3), pages 287-314, September.

    More about this item

    Keywords

    Signals; Credit; Margin; Discreteness; Nifty; Returns;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fininn:v:3:y:2017:i:1:d:10.1186_s40854-017-0067-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.