Dr Akhilesh Prasad

Dr Akhilesh Prasad

Assistant Professor

Area: Finance

Dr. Akhilesh Prasad has completed his Doctor of Business Administration (DBA) with thesis titled “Forecasting Spikes in the CBOE VIX Index” from S. P. Jain School of Global Management. After working extensively in the Software Industry for more than 12 years, he moved into finance field. His research is primarily focused on Quantitative Finance, Financial Mathematics, Portfolio Optimization, Options Pricing, Stochastic Calculus, Statistics, and Machine and Deep Learning.

Educational Qualification:

  • Doctor of Business Administration (DBA), S. P. Jain School of Global Management – Dubai – Mumbai – Singapore – Sydney
  • Global MBA (Finance), EDHEC Business School, Nice, France
  • B. Tech. (H), Agricultural and Food Engineering, IIT Kharagpur, India

Professional Qualification:

  • Certificate in Quantitative Finance (CQF), CQF Institute (Fitch Learning), London
  • Financial Risk Manager (Part 1 & 2), FRM Exam, GARP, USA
  • Chartered Financial Analyst (Level 1 & 2), CFA Exam, CFA Institute, USA


  • Adamala, S., Singh, R., Raghuwanshi, N. S., Prasad, A., & Chamoli, A. (2019). Hydrologic Calculator: an educational interface for hydrological processes analysis. Agricultural Engineering International: CIGR Journal, 21(1), 1-17.
  • Prasad, A., & Seetharaman, A. (2021). Importance of Machine Learning in Making Investment Decision in Stock Market. Vikalpa.https://doi.org/10.1177/02560909211059992
  • Prasad A., Bakhshi P., & Seetharaman A. (2022). The Impact of the U.S. Macroeconomic Variables on the CBOE VIX Index. Journal of Risk and Financial Management. 15(3):126. https://doi.org/10.3390/jrfm15030126

Accepted Papers:

  • Forecasting the direction of daily changes in the India VIX using deep learning.


Under Review Papers:

  • Forecasting the direction of daily changes in the India VIX using machine learning.
  • Role of the global volatility indices in predicting the volatility index of the Indian economies .


Working Papers:

  • Forecasting value at risk (VaR) using machine learning.
  • Forecasting value at risk (VaR) using deep learning.
  • Accumulating wealth during takeover bid in the M&A events.