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Efficient Generation Portfolio Construction Using Time-Varying Correlations

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Abstract

We constructed a procurement portfolio for the Indian power sector using two variants of the dynamic conditional correlation GARCH model to derive time-varying correlations between major coal indices. We used prices and qualities of observed cargos to adjust indices for quality gaps as well as for freight costs and power plant efficiency factors. Using the relative homogeneity of the energy content of imports from Australia, South Africa, and Indonesia, we found that the regional seaborne market is highly correlated during normal economic conditions, while suffering brief departures in correlation during demand and supply shocks. Our results show that the buying behavior of power producers is aligned with the mean-variance efficient portfolio of delivered prices using time-varying correlation estimates, but not free-on-board coal index prices. This study challenges the notion that thermal coal importers only source material with a freight price advantage and highlights the importance of coal quality gaps in power production.

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Notes

  1. There are other conditional GARCH models that are used in conditional volatility and correlation estimates such as GJR GARCH, BEKK GARCH, MGARCH, SWARCH, TGARCH, etc. All these models are equally efficient and suffer similar drawbacks. We chose the DCC GARCH model for two reasons. Firstly, the DCC GARCH model is efficient in estimating time-varying correlations for random variables. Secondly, the DCC GARCH model has been extensively used in the estimate of conditional correlations for portfolio optimization, see, for example, Gupta and Donleavy (2009), Case et al. (2010), and Christoffersen et al. (2011).

  2. The DCC model is estimated in a two-step process. The first step estimates univariate GARCH models for each residual series. In the second step, the residuals transformed by their standard deviations estimated in the first step are used to estimate the parameters of the time-varying correlations. See Engle and Sheppard (2001) for detailed assumptions of the model. As stressed earlier, there are other conditional GARCH models that have been used in the literature. We have used DCC GARCH because of its ease of use and it has been widely used in the context of portfolio constructions using time-varying correlations.

References

  • Adler, M., & Qi, R. (2003). Mexico’s integration into the North American capital market. Emerging Markets Review, 4, 91–120.

    Article  Google Scholar 

  • Andersen, T. G., Bollerslev, T., Christoffersen, P., & Diebold, F. X. (2007). Practical volatility and correlation modeling for financial market risk management. NBER Chapters. In The risks of financial institutions (pp. 513–548). Cambridge, MA: National Bureau of Economic Research.

  • Bekaert, G., & Harvey, C. R. (1995). Time-varying world market integration. Journal of Finance, 50(2), 403–444.

    Article  Google Scholar 

  • Bollerslev, T. (1990). Modeling the coherence in short run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics, 72, 498–505.

    Article  Google Scholar 

  • Cappiello, L., Engle, R., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.

    Article  Google Scholar 

  • Case, B., Yang, Y., & Yildirim, Y. (2010). Dynamic correlations among asset classes: REIT and stock returns. Journal of Real Estate Financial Economics, 44(3), 298–318.

    Article  Google Scholar 

  • Christoffersen, P., Errunza, V., Jacobs, K., & Jin, X. (2011). Is the potential for international diversification disappearing? Working Paper, Stern Business School, New York University, New York.

  • Drbal, L. F., Boston, P. G., & Westra, K. L. (1996). Power plant engineering. Overland Park, KS: Black & Veatch.

    Book  Google Scholar 

  • Dunis, C. L., & Shannon, G. (2005). Emerging markets of Southeast Asia and Central Asia: Do they still offer a diversification benefit? Journal of Asset Management, 6(3), 168–190.

    Article  Google Scholar 

  • Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20, 339–350.

    Article  Google Scholar 

  • Engle, R. F., & Sheppard, K. (2001). Theoretical and empirical properties of dynamic conditional correlations multivariate GARCH. NBER Working Paper 8554, National Bureau of Economic Research, Cambridge, MA.

  • Erb, C., Harvey, C., & Viskanta, T. (1994). Forecasting international equity correlations. Financial Analysts Journal, 23, 761–767.

    Google Scholar 

  • George Waterhouse Consultants. (2004). GWC coal handbook. Lincolnshire, UK: WCM Publishing.

  • Gibbons, M. R., Ross, S. A., & Shanken, J. (1989). A test of the efficiency of a given portfolio. Econometrica, 57(5), 1121–1152.

    Article  Google Scholar 

  • Gupta, T., & Donleavy, G. D. (2009). Benefits of diversifying investments into emerging markets with time-varying correlations: An Australian perspective. Journal of Multinational Financial Management, 19, 160–177.

    Article  Google Scholar 

  • Hogan, L., Thorpe, S., Swan, A., & Middleton, S. (1999). Pricing of Australia’s coking coal exports: A regional hedonic analysis. Resources Policy, 25(2), 27–38.

    Article  Google Scholar 

  • International Organization for Standardization (ISO). (1976). ISO 1928:1976 Solid mineral fuels—determination of gross calorific value by the calorimeter bomb method, and calculation of net calorific value.

  • Jithendranathan, T. (2005). What causes correlations of equity returns to change over time? A study of the US and the Russian equity markets. Investment Management and Financial Innovations, 4, 69–79.

    Google Scholar 

  • Juniper, L. A., & Pohl, J. U. (1997, July). What price coal quality in electric power generation? The Australian Coal Review, pp. 37–42.

  • King, M., & Cuc, M. (1996). Price convergence in North American natural gas spot markets. Energy Journal, 17(2), 17–42.

    Article  Google Scholar 

  • Kleit, A. N. (2001). Are regional oil markets growing closer together? An arbitrage cost approach. Energy Journal, 22, 1–15.

    Article  Google Scholar 

  • Koerner, R. J. (2001). Determination of Japanese buyer valuation of metallurgical coal characteristics by hedonic modelling. Resources Policy, 27, 179–197.

    Article  Google Scholar 

  • Kroner, K. F., & Ng, V. K. (1998). Modelling asymmetric co-movements of assets returns. Review of Financial Studies, 11, 817–844.

    Article  Google Scholar 

  • Li, R., Joyeux, R., & Ripple, R. D. (2010). International steam coal market integration. Energy Journal, 31(3), 181–202.

    Google Scholar 

  • Lo, A. W. (2002). The statistics of Sharpe ratios. Financial Analysts Journal, July/August, 36–52.

  • Longin, F., & Solnik, B. (1995). Is the correlation in international equity returns constant: 1960–90? Journal of International Money and Finance, 14, 3–26.

    Article  Google Scholar 

  • Mesroghli, S., Jorjani, E., & Chehreh Chelgani, S. (2009). Estimation of gross calorific value based on coal analysis using regression and artificial neural networks. International Journal of Coal Geology, 79, 49–54.

    Article  Google Scholar 

  • Morse, R. K., & He, G. (2010). The world’s greatest coal arbitrage: China’s coal import behavior and implications for the global coal market. Working Paper, Program on Energy and Sustainable Development, Freeman Spogli Institute for International Studies, Stanford University, Stanford, CA.

  • Pesaran, B., & Pesaran, M. H. (2007). Modelling volatilities and conditional correlations in futures markets with a multivariate t distribution. IZA Discussion Papers 2906, Institute for the Study of Labor (IZA), Bonn.

  • Pesaran, M. H., Schleicher, C., & Zaffaroni, P. (2009). Model averaging in risk management with an application to futures markets. Journal of Empirical Finance, 16(2), 280–305.

    Article  Google Scholar 

  • Swann, A., Thorpe, S., & Hogan, L. (1999). Australia–Japan coking coal trade: A hedonic analysis under benchmark and fair treatment pricing. Resources Policy, 25(2), 15–25.

    Article  Google Scholar 

  • Warell, L. (2006). Market integration in the coal industry: A cointegration approach. Energy Journal, 27(1), 99–118.

    Article  Google Scholar 

  • West, J. M. (2011). Picking winners: Understanding the future cost of energy generation in Australia. JASSA: The Finsia Journal of Applied Finance, 1, 15–19.

    Google Scholar 

  • Yang, S. Y. (2005). A DCC analysis of international stock market correlations: The role of Japan on the four Asian tigers. Applied Financial Economic Letters, 1(2), 89–93.

    Article  Google Scholar 

  • Zaklan, A., Cullmann, A., Neuman, A., & von Hirschhausen, C. (2012). The globalization of steam coal markets and the role of logistics: An empirical analysis. Energy Economics, 34(1), 105–116.

    Article  Google Scholar 

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Acknowledgments

The authors wish to acknowledge the helpful comments provided by John Carranza and an anonymous reviewer. The authors are grateful to BHP Billiton and Cargill for providing proprietary coal quality, coal price, and freight price data.

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  1. Department of Accounting, Finance and Economics, Griffith Business School, Nathan, QLD, 4111, Australia

    R. Gupta & J. M. West

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  1. R. Gupta
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Correspondence to J. M. West.

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Gupta, R., West, J.M. Efficient Generation Portfolio Construction Using Time-Varying Correlations. Nat Resour Res 23, 267–283 (2014). https://doi.org/10.1007/s11053-013-9220-x

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