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Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?

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  • Zhang, Yue-Jun
  • Lin, Jia-Juan

Abstract

It is universally acknowledged that both linear Vector Autoregressive (VAR) models and nonlinear econometric frameworks, such as Markov Regime Switching (MRS) model, can produce appropriate portfolio allocations that hedge against the bullish and bearish dynamics in commodity markets. Then, whether the linear model can outperform the nonlinear model is worth exploring for modeling when we focus on the asset allocations in commodity markets. Therefore, this paper studies whether simple VAR models can produce commodity portfolios whose performance is superior or similar to that obtained under MRS model, and also discusses the impact of investors' different risk preferences on commodity portfolio performance. The empirical results indicate that, first, VAR models can produce portfolios with better performance than MRS model in the case of a long sample. Second, an increasing risk aversion of investors may significantly reduce the portfolio performance. Among them, investors who prefer profit-making to risk-resisting and who pay same attention to returns and risks are more likely to obtain portfolios with higher returns and lower risks. Finally, WTI is an appropriate investment target for investors who only focus on the maximization of returns, while for investors with other risk preferences, gold proves an ideal investment target.

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  • Zhang, Yue-Jun & Lin, Jia-Juan, 2019. "Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?," International Review of Financial Analysis, Elsevier, vol. 66(C).
  • Handle: RePEc:eee:finana:v:66:y:2019:i:c:s1057521919304387
    DOI: 10.1016/j.irfa.2019.101395
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Bataa, Erdenebat & Park, Cheolbeom, 2017. "Is the recent low oil price attributable to the shale revolution?," Energy Economics, Elsevier, vol. 67(C), pages 72-82.
    3. Guidolin, Massimo & Hyde, Stuart, 2012. "Simple VARs cannot approximate Markov switching asset allocation decisions: An out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3546-3566.
    4. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Jumps in commodity markets," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 55-70.
    5. Kandel, Shmuel & Stambaugh, Robert F, 1996. "On the Predictability of Stock Returns: An Asset-Allocation Perspective," Journal of Finance, American Finance Association, vol. 51(2), pages 385-424, June.
    6. John Y. Campbell & Tuomo Vuolteenaho, 2004. "Inflation Illusion and Stock Prices," American Economic Review, American Economic Association, vol. 94(2), pages 19-23, May.
    7. Brennan, Michael J. & Schwartz, Eduardo S. & Lagnado, Ronald, 1997. "Strategic asset allocation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1377-1403, June.
    8. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    9. Andriosopoulos, Kostas & Nomikos, Nikos, 2014. "Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets," European Journal of Operational Research, Elsevier, vol. 234(2), pages 571-582.
    10. Yan‐ran Ma & Qiang Ji & Jiaofeng Pan, 2019. "Oil financialization and volatility forecast: Evidence from multidimensional predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 564-581, September.
    11. Hwang, Soosung & Satchell, Steve E., 2010. "How loss averse are investors in financial markets?," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2425-2438, October.
    12. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    13. Fernandez-Diaz, Jose M. & Morley, Bruce, 2019. "Interdependence among agricultural commodity markets, macroeconomic factors, crude oil and commodity index," Research in International Business and Finance, Elsevier, vol. 47(C), pages 174-194.
    14. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    15. Main, Scott & Irwin, Scott H. & Sanders, Dwight R. & Smith, Aaron, 2018. "Financialization and the returns to commodity investments," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 22-28.
    16. Geman, Hélyette & Kharoubi, Cécile, 2008. "WTI crude oil Futures in portfolio diversification: The time-to-maturity effect," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2553-2559, December.
    17. Silvennoinen, Annastiina & Thorp, Susan, 2013. "Financialization, crisis and commodity correlation dynamics," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 42-65.
    18. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joëlle, 2016. "Is idiosyncratic volatility priced in commodity futures markets?," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 219-226.
    19. Guidolin, Massimo & Ono, Sadayuki, 2006. "Are the dynamic linkages between the macroeconomy and asset prices time-varying?," Journal of Economics and Business, Elsevier, vol. 58(5-6), pages 480-518.
    20. Guiso, Luigi & Sapienza, Paola & Zingales, Luigi, 2018. "Time varying risk aversion," Journal of Financial Economics, Elsevier, vol. 128(3), pages 403-421.
    21. Karyotis, Catherine & Alijani, Sharam, 2016. "Soft commodities and the global financial crisis: Implications for the economy, resources and institutions," Research in International Business and Finance, Elsevier, vol. 37(C), pages 350-359.
    22. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    23. Yan, Lei & Irwin, Scott H. & Sanders, Dwight R., "undated". "The Relationship between Commodity Investment Flows and Crude Oil Futures Prices: Real or Spurious?," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235933, Agricultural and Applied Economics Association.
    24. Ji, Qiang & Liu, Bing-Yue & Nehler, Henrik & Uddin, Gazi Salah, 2018. "Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach," Energy Economics, Elsevier, vol. 76(C), pages 115-126.
    25. Ma, Yan-Ran & Zhang, Dayong & Ji, Qiang & Pan, Jiaofeng, 2019. "Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?," Energy Economics, Elsevier, vol. 81(C), pages 536-544.
    26. Guidolin, Massimo & Hyde, Stuart, 2012. "Can VAR models capture regime shifts in asset returns? A long-horizon strategic asset allocation perspective," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 695-716.
    27. Shimon Awerbuch, 2006. "Portfolio-Based Electricity Generation Planning: Policy Implications For Renewables And Energy Security," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 11(3), pages 693-710, May.
    28. Yan, Lei & Irwin, Scott H. & Sanders, Dwight R., 2018. "Mapping algorithms, agricultural futures, and the relationship between commodity investment flows and crude oil futures prices," Energy Economics, Elsevier, vol. 72(C), pages 486-504.
    29. Ji, Qiang & Li, Jianping & Sun, Xiaolei, 2019. "Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports," Finance Research Letters, Elsevier, vol. 30(C), pages 420-425.
    30. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    31. Gerald R. Jensen & Robert R. Johnson & Jeffrey M. Mercer, 2000. "Efficient use of commodity futures in diversified portfolios," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(5), pages 489-506, May.
    32. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
    33. Andreasson, Pierre & Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2016. "Impact of speculation and economic uncertainty on commodity markets," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 115-127.
    34. Leyuan You & Robert T. Daigler, 2010. "Using Four‐Moment Tail Risk to Examine Financial and Commodity Instrument Diversification," The Financial Review, Eastern Finance Association, vol. 45(4), pages 1101-1123, November.
    35. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    36. Zhang, Yue-Jun & Chevallier, Julien & Guesmi, Khaled, 2017. "“De-financialization” of commodities? Evidence from stock, crude oil and natural gas markets," Energy Economics, Elsevier, vol. 68(C), pages 228-239.
    37. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    38. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    39. Miffre, Joelle & Rallis, Georgios, 2007. "Momentum strategies in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1863-1886, June.
    40. Shaikh, Imlak, 2017. "The 2016 U.S. presidential election and the Stock, FX and VIX markets," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 546-563.
    41. Yan, Lei & Garcia, Philip, 2017. "Portfolio investment: Are commodities useful?," Journal of Commodity Markets, Elsevier, vol. 8(C), pages 43-55.
    42. Bilgin, Mehmet Huseyin & Gozgor, Giray & Lau, Chi Keung Marco & Sheng, Xin, 2018. "The effects of uncertainty measures on the price of gold," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 1-7.
    43. Gurevich, Gregory & Kliger, Doron & Levy, Ori, 2009. "Decision-making under uncertainty - A field study of cumulative prospect theory," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1221-1229, July.
    44. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    45. Pham, Huy Nguyen Anh & Ramiah, Vikash & Moosa, Nisreen & Huynh, Tam & Pham, Nhi, 2018. "The financial effects of Trumpism," Economic Modelling, Elsevier, vol. 74(C), pages 264-274.
    46. Kang, Sang Hoon & McIver, Ron & Yoon, Seong-Min, 2017. "Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets," Energy Economics, Elsevier, vol. 62(C), pages 19-32.
    47. Fernandez-Diaz, Jose M. & Morley, Bruce, 2019. "Interdependence among agricultural commodity markets, macroeconomic factors, crude oil and commodity index," Research in International Business and Finance, Elsevier, vol. 47(C), pages 174-194.
    48. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    49. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    50. Fuertes, Ana-Maria & Miffre, Joëlle & Rallis, Georgios, 2010. "Tactical allocation in commodity futures markets: Combining momentum and term structure signals," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2530-2548, October.
    51. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
    52. Kosater, Peter & Mosler, Karl, 2006. "Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices," Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
    53. Chng, Michael T., 2009. "Economic linkages across commodity futures: Hedging and trading implications," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 958-970, May.
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