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Muneer Shaik


Assistant Professor

PhD in Finance from Madras University, a Masters in Economics, and Bachelors in Mechanical Engineering from BITS Pilani

Prof. Muneer Shaik handles courses in the area of finance and data science for MBA students and is actively involved in training corporate clients. He holds a PhD in Finance from IFMR Graduate School of Business (University of Madras), a Masters in Economics, and Bachelors in Mechanical Engineering from BITS Pilani, with over 5 years of financial industry experience with JPMorgan in a managerial position in the equity and fixed income department. Prof. Shaik has presented research papers at multiple conferences in India and abroad, and some have even won the best paper awards. His research interests are mainly in the areas of volatility modeling, financial econometrics, market efficiency and applications of data science in the area of finance.

  • Volatility
  • Mean Reversion
  • Market Efficiency
  • Machine Learning
  • Empirical Finance.
  1. Muneer Shaik, S. Maheswaran (2020), ‘A new unbiased robust volatility estimation using extreme values of asset prices,’ Financial Markets and Portfolio Management, Vol. 34, No.3, pages. 313-347.
  2. Muneer Shaik, S. Maheswaran (2020), ‘A new method based on Range to detect Mean reversion’, IIMB Management Review. (In Press, Corrected Proof, Available online 14 August 2020. 
  3. Mukta Kanvinde, Muneer Shaik (2020), ‘Are BRICS Stock Market Indices Mean Reverting? Evidence based on Expected Lifetime Range Ratio,’ International Journal of Business and Economics (Forthcoming)
  4. Gurmeet Singh, Muneer Shaik (2020), ‘Re-examining the Expiration Effects of Index Futures: Evidence from India,’ International Journal of Economics and Financial Issues, Vol. 10, No. 3. 
  5. Muneer Shaik, S. Maheswaran (2019), ‘Volatility Behavior of Asset Returns Based on Robust Volatility Ratio: Empirical Analysis on Global Stock Indices,’ Cogent Economics & Finance, Vol. 7, No. 1. 
  6. Muneer Shaik, S. Maheswaran (2019), ‘Robust Estimation of Volatility with and without the drift parameter,’ Journal of Quantitative Economics, Vol. 17, No. 1, pages 57-91. 
  7. Muneer Shaik, S. Maheswaran (2018), ‘Evidence of Excess Volatility based on a New Robust Volatility Ratio’, Journal of Economic Studies, Vol. 45, No. 4. 
  8. Muneer Shaik, S. Maheswaran (2018),’ Expected Lifetime Range Ratio to find Mean reversion: Evidence from Indian Stock Market’, Cogent Journal of Economics and Finance, Vol.6, No.1.
  9. Muneer Shaik (2017),’Are Northeast Asian Stock Markets Weak Form Efficient? Evidence based on Multiple Variance Ratio tests’, Empirical Economics Letters, Vol 16, No. 4, pages. 311-320.
  10. Muneer Shaik, S.Maheswaran (2017),’Random Walk in Emerging Asian Stock Markets’, International Journal of Economics and Finance, Vol. 9, No. 1, pages. 20-31.  
  11. Muneer Shaik, S. Maheswaran (2017) “Market Efficiency of ASEAN Stock Markets”, Asian Economic and Financial Review, Vol. 7, No. 2, pages. 109-122.
  12. Muneer Shaik, S.Maheswaran (2016), “Modelling the Paradox in stock markets by variance ratio volatility estimator that utilizes the extreme values of asset prices”, Journal of Emerging Market Finance, Vol. 15, No. 3, pages. 333-361. 


  1. 2019: International Conference on Economics, Business Management and Social Sciences (IACEBMSS), Istanbul (Turkey), August.
  2. 2018: World Finance Conference, Cagliari (Italy), July 26-28.
  3. 2017: 16th Annual Conference on Macroeconomics and Finance, IGIDR.
  4. 2016: 4th Pan IIM World Management Conference held at IIM Ahmedabad, December.
  5. 2016: International Conference on Financial Markets and Corporate Finance (ICFMCF 2016) at IIT Madras, August.
  6. 2015: PhD Consortium 2015, IIT Bombay.


  1. Received Best Paper Award at Manipal University, India, Dec 22-24,2016.
  2. Received Best Paper Award at the Doctoral Consortium-2016, IIT Bombay.

Foundations of Finance (Financial Management), Derivatives and Risk Management, Simulation Techniques in Finance, Machine Learning for Finance, Empirical Asset Pricing, Data Analytics using R, Python Programming.