Modeling tail risk in Indian commodity markets using conditional EVT-VaR and their relation to the stock market
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DOIhttp://dx.doi.org/10.21511/imfi.19(3).2022.01
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Article InfoVolume 19 2022, Issue #3, pp. 1-12
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Investment in commodity markets in India accelerated after 2007; this was accompanied by large price variability, hence, it becomes imperative to measure commodity price risk precisely. It becomes equally important to study the relationship between commodity price variability and the stock market. Hence, this study aims to calculate the tail risk of highly traded Indian commodity futures returns using the conditional EVT-VaR method for risk measurement. Secondly, the linkage between commodity markets and the stock market is also studied using the Delta CoVaR method. Results highlight the following points. There is risk transfer from the extreme increase/decrease in crude oil futures returns to the Nifty Index returns. Both extreme price increase or decrease of crude oil futures driven either by financial or a combination of financial and economic shocks affect the stock market. Zinc and Natural gas futures are not linked to the stock market, which means they can be useful in portfolio diversification. The findings suggest that, in Indian commodity markets, EVT-VaR is a useful tool for measuring risk. Only Crude oil futures shocks affect the stock market, and extreme integration between them becomes more prominent when oil shocks are driven by financial factors. Commodities other than Crude oil are not integrated with stock markets in India.
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JEL Classification (Paper profile tab)G01, G11, Q43
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References40
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Tables6
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Figures4
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- Figure 1. Rolling VaR values and returns of natural gas futures
- Figure 2. Rolling VaR values and returns of crude oil futures
- Figure 3. Rolling VaR values and returns of zinc futures
- Figure 4. Marginal risk contribution of crude oil futures, zinc futures and natural gas futures returns to stock markets
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- Table 1. Percentage of total contracts of commodity futures traded on MCX
- Table 2. Descriptive statistics
- Table 3. Correlations of variables used in the study
- Table 4. EVT parameter estimates of the Nifty index and commodity futures
- Table 5. Risk transmission from commodity futures to the Nifty 500 index using CoVaR and Delta CoVaR
- Table 6. Diffusion channels of commodity risk to Nifty 500 index
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- Abhyankar, A., Xu, B., & Wang, J. (2013). Oil price shocks and the stock market: evidence from Japan. The Energy Journal, 34(2).
- Acharya, V., Engle, R., & Richardson, M. (2012). Capital shortfall: A new approach to ranking and regulating systemic risks. American Economic Review, 102(3), 59-64.
- Adams, Z., & Glück, T. (2015). Financialization in commodity markets: a passing trend or the new normal? Journal of Banking & Finance, 60, 93-111.
- Adrian, T., & Brunnermeier, M. K. (2011). CoVaR (No. w17454). National Bureau of Economic Research.
- Algieri, B. (2014). The influence of biofuels, economic and financial factors on daily returns of commodity futures prices. Energy Policy, 69, 227-247.
- Antoniou, A., & Foster, A. J. (1992). The effect of futures trading on spot price volatility: evidence for Brent crude oil using GARCH. Journal of Business Finance & Accounting, 19(4), 473-48.
- Bali, T. G., & Neftci, S. N. (2003). Disturbing extremal behaviour of spot rate dynamics. Journal of Empirical Finance, 10(4), 455-477.
- Bastianin, A., Conti, F., & Manera, M. (2016). The impacts of oil price shocks on stock market volatility: Evidence from the G7 countries. Energy Policy, 98, 160-169.
- Bhardwaj, G., Gorton, G. B., & Rouwenhorst, K. G. (2016). Investor interest and the returns to commodity investing. The Journal of Portfolio Management, 42(3), 44-55.
- Brownlees, C., & Engle, R. F. (2017). SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies, 30(1), 48-79.
- Bosch, D., & Smimou, K. (2022). Traders’ motivation and hedging pressure in commodity futures markets. Research in International Business and Finance, 59, 101529.
- Buyuksahin, B., & Robe, M. A. (2014). Speculators, commodities and cross-market linkages. Journal of International Money and Finance, 42, 38-70.
- Cheng, I. H., & Xiong, W. (2014). Financialization of commodity markets. Annual Review of Financial Economics, 6(1), 419-441.
- Chng, M. T. (2009). Economic linkages across commodity futures: Hedging and trading implications. Journal of Banking & Finance, 33(5), 958-970.
- Chevallier, J., Gatumel, M., & Ielpo, F. (2014). Commodity markets through the business cycle. Quantitative Finance, 14(9), 1597-1618.
- Drehmann, M., & Tarashev, N. (2013). Measuring the systemic importance of interconnected banks. Journal of Financial Intermediation, 22(4), 586-607.
- Ergun, A. T., & Jun, J. (2010). Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall. The Quarterly Review of Economics and Finance, 50(3), 264-272.
- Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49.
- Ferson, W. E., & Harvey, C. R. (1994). Sources of risk and expected returns in global equity markets. Journal of Banking & Finance, 18(4), 775-803.
- Goldstein, I., & Yang, L. (2019, June). Commodity financialization and information transmission. In AFA 2016 Annual Meeting, NBER 2015 Commodity Meeting, Rotman School of Management Working Paper (No. 2555996).
- Gupta, R., & Modise, M. P. (2013). Does the source of oil price shocks matter for South African stock returns? A structural VAR approach. Energy Economics, 40, 825-831.
- Hong, H., & Yogo, M. (2012). What does futures market interest tell us about the macroeconomy and asset prices? Journal of Financial Economics, 105(3), 473-490.
- Ji, Q., Bouri, E., Roubaud, D., & Shahzad, S. J. H. (2018). Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Economics, 75, 14-27.
- Karmakar, M., & Shukla, G. K. (2015). Managing extreme risk in some major stock markets: An extreme value approach. International Review of Economics & Finance, 35, 1-25.
- Kang, S. H., McIver, R., & Yoon, S. M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19-32.
- Kaltalioglu, M., & Soytas, U. (2011). Volatility spillover from oil to food and agricultural raw material markets. Modern Economy, 2(02), 71.
- Kilian, L., & Park, C. (2009). The impact of oil price shocks on the US stock market. International Economic Review, 50(4), 1267-1287.
- Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 33-50.
- Irwin, S. H., & Sanders, D. R. (2012). Financialization and structural change in commodity futures markets. Journal of Agricultural and Applied Economics, 44(3), 371-396.
- McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3), 271-300
- Miller, J. I., & Ratti, R. A. (2009). Crude oil and stock markets: Stability, instability, and bubbles. Energy Economics, 31(4), 559-568.
- Nazlioglu, S., Soytas, U., & Gupta, R. (2015). Oil prices and financial stress: A volatility spillover analysis. Energy Policy, 82, 278-288.
- Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2016). Downside and upside risk spillovers between exchange rates and stock prices. Journal of Banking & Finance, 62, 76-96.
- Singleton, K. J. (2014). Investor flows and the 2008 boom/bust in oil prices. Management Science, 60(2), 300-318.
- Sinha, P., & Agnihotri, S. (2018). Bayesian and EVT Value-at-Risk estimates of Indian non-financial firms. Journal of International Business and Economy, 19(1), 50-75.
- Tang, K., & Xiong, W. (2012). Index investment and the financialization of commodities. Financial Analysts Journal, 68(6), 54-74.
- Thuraisamy, K. S., Sharma, S. S., & Ahmed, H. J. A. (2013). The relationship between Asian equity and commodity futures markets. Journal of Asian Economics, 28, 67-75.
- Watanabe, T. (2012). Quantile Forecasts of Financial Returns Using Realized Garch Models*. Japanese Economic Review, 63(1), 68-80.
- Watugala, S. W. (2015). Economic uncertainty and commodity futures volatility. Office of Financial Research Working Paper, 15-14.
- Wheelock, D. C., & Wohar, M. E. (2009). Can the term spread predict output growth and recessions? A survey of the literature. Federal Reserve Bank of St. Louis Review, 91(5 Part 1), 419-440.