Prof. Dilip Kumar is an Associate Professor in the area of Finance and Accounting at IIM Kashipur. Prof. Dilip Kumar holds PhD in Finance and has done his PhD research work at Institute for Financial Management and Research (IFMR) Chennai. Prior to joining IIM Kashipur, Prof. Kumar was a faculty member in the financial engineering department of Institute for Financial Management and Research, Chennai. He mainly works in the area of Asset Pricing, Financial Management and Financial Risk analysis. His current research focusses on systemic risk, extreme risk interconnection and corporate sustainability. He also works in the areas of extreme value volatility estimator, bias correction procedures for efficient estimation of volatility and robust volatility estimators. Prof. Kumar is also a Chartered Financial Analyst charter holder from the Institute of Chartered Financial Analyst of India.

Systemic risk, Extreme risk, Corporate sustainability, Extreme value volatility estimator, Robust volatility estimator

• Kumar, D. (2022). Economic and political uncertainties and sustainability disclosures in the tourism sector firms. Tourism Economics, https://doi.org/10.1177/13548166221113434.
• Ambatipudi, V., & Kumar, D. (2022). Economic Policy Uncertainty Versus Sector Volatility: Evidence from India Using Multi-scale Wavelet Granger Causality Analysis. Journal of Emerging Market Finance, 21(2), 184-210.
• Bashir, H. A., & Kumar, D. (2022). Investor attention, Twitter uncertainty and cryptocurrency market amid the COVID-19 pandemic. Managerial Finance, https://doi.org/10.1108/MF-09-2021-0414
• Khemani, P., & Kumar, D. (2022). Is financial development crucial to achieving the “2030 agenda of sustainable development”? Evidence from Asian countries. International Journal of Emerging Markets, https://doi.org/10.1108/IJOEM-06-2021-0853
• Shiljas, K., Kumar, D., & Bashir, H. A. (2022). Nexus between Twitter-based sentiment and tourism sector performance amid COVID-19 pandemic. Tourism Economics, https://doi.org/10.1177/13548166221123102
• Zargar, F. N., & Kumar, D. (2021). Market fear, investor mood, sentiment, economic uncertainty and tourism sector in the United States amid COVID-19 pandemic: A spillover analysis. Tourism Economics, https://doi.org/10.1177/13548166211052803
• Bashir, H. A., Bansal, M., & Kumar, D. (2021). Predictive view of the value relevance of earnings in India. Journal of Financial Reporting and Accounting, https://doi.org/10.1108/JFRA-08-2021-0219
• 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.

Pre-rating assessment of MDDA, Mussoorie Dehradun Development Authority (MDDA)

• Chairperson, Finance and Accounting Area (Nov 2022 to Present)
• Chairperson, Internal Audit Committee (Dec 2021 to Dec 2023)
• Chairperson, Doctoral Programme (July 2020 to June 2022)
• Chairperson, Research Committee (June 2018 to June 2020)
• Chairperson, Finance and Accounting Area (June 2016 to June 2019)
• Chairperson, Institute Ranking (Nov 2015 to June 2019)

Financial Derivatives, Financial Risk Measurement and Management, Financial Analytics, Trading Strategies and Introduction to Market Microstructure, Corporate Finance, Stochastic Calculus in Finance, Advances in Financial Risk Modelling