DILIP KUMAR

Associate Professor Finance and Accounting Chair -Doctoral Program +91-7900444090 (Ext-209) dilip.kumar@iimkashipur.ac.in


Dr. Dilip Kumar holds PhD in Finance and has done his PhD research work at Institute for Financial Management and Research (IFMR) Chennai. Before joining IIM Kashipur, he was a faculty member in the financial engineering department of IFMR Chennai. He has taught various courses such as Simulation Techniques in Finance, Financial Derivatives, Financial Risk Measurement and Management, Financial Engineering using MATLAB etc at both graduate and undergraduate level.

His research interests include extreme value volatility estimator, bias correction procedures for efficient estimation of volatility, robust volatility estimators, Modeling extreme value conditional volatility, risk spillover, dynamics in market efficiency under the impact of structural changes in market etc. His current research focuses on developing bias correction procedure for various extreme value volatility estimators. Another segment of his current research is about developing a robust extreme value volatility estimator and proposing a bias correction procedure for the same. He was also an Editorial Associate of the "Journal of Emerging Market Finance" published by sage publication. He is also a Chartered Financial Analyst (CFA) charter holder from the Institute of Chartered Financial Analyst of India.

  • Bashir, H. A., & Kumar, D. (2021). Investor attention, uncertainty and travel & leisure stock returns amid the COVID-19 pandemic. Current Issues in Tourism. https://doi.org/10.1080/13683500.2021.1910633
  • Zargar, F. N., & Kumar, D. (2020). Modeling unbiased extreme value volatility estimator in presence of heterogeneity and jumps: A study with economic significance analysis. International Review of Economics & Finance, 67, 25-41.
  • Zargar, F. N., & Kumar, D. (2020). Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis. The Quarterly Review of Economics and Finance, 77, 271-285.
  • Zargar, F. N., & Kumar, D. (2019). Long range dependence in the Bitcoin market: A study based on high-frequency data. Physica A: Statistical Mechanics and its Applications, 515, 625-640.
  • Kumar, D. (2019). What impacts the structural breaks in volatility transmission from crude oil to agricultural commodities?. Journal of Economic Research (JER), 24(1), 91-127.
  • Kumar, D. (2019). Structural breaks in volatility transmission from developed markets to major Asian emerging markets. Journal of Emerging Market Finance, 18(2), 172-209.
  • Zargar, F. N., & Kumar, D. (2019). Informational inefficiency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance, 47, 344-353.
  • Rajwani, S., & Kumar, D. (2019). Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach. Global Business Review, 20(4), 962-980.
  • Kumar, D. (2019). Modelling and forecasting unbiased extreme value volatility estimator: A study based on exchange rates with economic significance analysis. Journal of Prediction Markets, 13(1).
  • Kumar, D. (2018). Modeling Volatility of Indian Exchange Rates under the Impact of Regime Shifts: A study with economic significance analysis. Journal of Prediction Markets, 12(1), 43-59.
  • Kumar, D. (2017). Modeling and Forecasting Unbiased Extreme Value Volatility Estimator in Presence of Leverage Effect. Journal of Quantitative Economics, 16(2), 313-335.
  • Kumar, D. (2017). Realized volatility transmission from crude oil to equity sectors: A study with economic significance analysis. International Review of Economics & Finance, 49, 149-167.
  • Kumar, D. (2017). Forecasting energy futures volatility based on the unbiased extreme value volatility estimator. IIMB Management Review, 29(4), 294-310.
  • Kumar, D., & Maheswaran, S. (2017). Value-at-risk and expected shortfall using the unbiased extreme value volatility estimator. Studies in Economics and Finance, 34(4), 506-526.
  • Kumar, D. (2017). Structural Breaks in Unbiased Volatility Estimator: Modeling and Forecasting. Journal of Prediction Markets, 11(1).
  • Gangadharan, S. R., & Kumar, D. (2017). Integration of the Indian stock market with the world market: A study based on the time-varying Kalman filter approach. International Journal of Accounting and Finance, 7(2), 110-126.
  • Kumar, D. (2017). A study of risk spillover in the crude oil and the natural gas markets. Global Business Review, 18(6), 1465-1477.
  • Kumar, D. (2016). Sudden breaks in drift-independent volatility estimator based on multiple periods open, high, low, and close prices. IIMB Management Review, 28(1), 31-42.
  • Rajwani, S., & Kumar, D. (2016). Asymmetric dynamic conditional correlation approach to financial contagion: A study of Asian markets. Global Business Review, 17(6), 1339-1356.
  • Kumar, D. (2016). Sudden changes in crude oil price volatility: An application of extreme value volatility estimator. American Journal of Finance and Accounting, 4(3-4), 215-234.
  • Kumar, D. (2017). On volatility transmission from crude oil to agricultural commodities. International Review of Business Research Papers, 12(2), 141-151.
  • Garg, A. K., Mitra, S. K., & Kumar, D. (2016). Do foreign institutional investors herd in emerging markets? A study of individual stocks. Decision, 43(3), 281-300.
  • Kumar, D. (2015). Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis. Economic Modelling, 49, 354-371.
  • Kumar, D. (2015). Risk spillover between the GIPSI economies and Egypt, Saudi Arabia, and Turkey. Emerging Markets Finance and Trade, 51(6), 1193-1208.
  • Kumar, D., & Maheswaran, S. (2015). Long memory in Indian exchange rates: An application of power-law scaling analysis. Macroeconomics and Finance in Emerging Market Economies, 8(1-2), 90-107.
  • Kumar, D., & Maheswaran, S. (2015). Return and volatility spillover among the PIIGS economies and India. American Journal of Finance and Accounting, 4(1), 28-49.
  • Kumar, D., & Maheswaran, S. (2014). Modeling and forecasting the additive bias corrected extreme value volatility estimator. International Review of Financial Analysis, 34, 166-176.
  • Kumar, D. (2014). Long-range dependence in Indian stock market: A study of Indian sectoral indices. International Journal of Emerging Markets, 9(4), 505-519.
  • Kumar, D., & Maheswaran, S. (2014). A reflection principle for a random walk with implications for volatility estimation using extreme values of asset prices. Economic Modelling, 38, 33-44.
  • Kumar, D., & Maheswaran, S. (2014). A new approach to model and forecast volatility based on extreme value of asset prices. International Review of Economics & Finance, 33, 128-140.
  • Kumar, D. (2014). Long range dependence in the high frequency USD/INR exchange rate. Physica A: Statistical Mechanics and its Applications, 396, 134-148.
  • Kumar, D. (2014). Return and volatility transmission between gold and stock sectors: Application of portfolio management and hedging effectiveness. IIMB Management Review, 26(1), 5-16.
  • Kumar, D. (2014). Correlations, return and volatility spillovers in Indian exchange rates. Global Business Review, 15(1), 77-91.
  • Kumar, D., & Maheswaran, S. (2014). Are major global stock markets efficient? An application of the martingale difference hypothesis with wild bootstrap. American Journal of Finance and Accounting, 3(2/3/4), 217-233.
  • Kumar, D., & Maheswaran, S. (2013). Modeling persistence and long memory under the impact of regime shifts in the PIGS stock markets. Decision, 40(1), 117-134.
  • Kumar, D., & Maheswaran, S. (2013). Asymmetric long memory volatility in the PIIGS economies. Review of Accounting and Finance, 12(1), 23-43.
  • Kumar, D., & Maheswaran, S. (2013). Correlation transmission between crude oil and Indian markets. South Asian Journal of Global Business Research, 2(2),  211-229.
  • Kumar, D., & Maheswaran, S. (2013). Return, volatility and risk spillover from oil prices and the US dollar exchange rate to the Indian industrial sectors. Margin: The Journal of Applied Economic Research, 7(1), 61-91.
  • Kumar, D. (2013). Are PIIGS stock markets efficient? Studies in Economics and Finance, 30(3), 209-225.
  • Kumar, D., & Maheswaran, S. (2013). Are Major Asian Markets Efficient? An Analysis Using Non-Parametric Joint Variance Ratio Tests. Journal of Management Research, 13(1), 3.
  • Kumar, D., & Maheswaran, S. (2013). Detecting sudden changes in volatility estimated from high, low and closing prices. Economic Modelling, 31, 484-491.
  • Maheswaran, S., & Kumar, D. (2013). An automatic bias correction procedure for volatility estimation using extreme values of asset prices. Economic Modelling, 33, 701-712.
  • Kumar, D., & Maheswaran, S. (2013). Evidence of long memory in the Indian stock market. Asia-Pacific Journal of Management Research and Innovation, 9(1), 9-21.
  • Kumar, D., & Maheswaran, S. (2012). Modelling asymmetry and persistence under the impact of sudden changes in the volatility of the Indian stock market. IIMB Management Review, 24(3), 123-136.
  • Kumar, D., & Maheswaran, S. (2012). Testing the Martingale Hypothesis in the Indian stock market: Evidence from multiple variance ratio tests. Decision, 39(2), 62.
  • Kumar, D. (2012). Long memory in PIIGS economies: An application of wavelet analysis. NMIMS Management Review, 22, 21-34.
  • Kumar, D., & Maheswaran, S. (2012). Detecting Sudden Changes in the Extreme Value Volatility Estimator. Decision, 39(3), 44-67.
  • Kumar, D., & Maheswaran, S. (2011). Volatility persistence in the presence of structural breaks in the Indian banking sector. Paradigm, 15(1-2), 8-17.
S.No Name Term
1 Financial Derivatives Term IV
2 Financial Risk Measurement and Management Term V
3 Financial Analytics Term V
4 Trading Strategies and Introduction to Market Microstructure Term VI
5 Corporate Finance Term III
6 Quantitative Research Methods FPM Term II
7 Quantitative Research Methods in Finance EFPM Term II
8 Mathematical Finance FPM Term IV
9 Interest Rate Models and Credit Derivatives FPM Term V
10 Financial Risk Modelling

FPM Term VI

11 Market Microstructure and High Frequency Data Modelling FPM Term VI

 

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Chair -Doctoral Program