This article examines the impact of dynamic economic states on commodity portfolio performance by using Markov regime-switching vector autoregression (MRS-VAR) framework. Temporal behaviour of different assets in portfolio has been studied using weekly dataset. Empirical evidence finds prevalence of regime-switching phenomenon in all assets suggesting their state-dependent behaviour. Results of the study confirm that a pair of asset classes exhibits different levels of correlations under different economic scenarios. Optimal asset allocation under each regime has been investigated and the results provide evidence of differential optimal commodity portfolio composition in distinct economic states. The results of the study provide interesting insights for investors and portfolio managers. By examining the switching points in different asset classes, they can rebalance their portfolio which will help them in better risk management and enhancing risk–return performance.
AbdullahA. M., SaitiB., & MasihA. M. M. (2014). Diversification in crude oil and other commodities: A comparative analysis (No. 56988). Munich: University Library of Munich, Germany.
2.
AngA., & BekaertG. (1999). International asset allocation with time-varying correlations. (National Bureau of Economic Research Working Paper 7056). Stanford, CA: National Bureau of Economic Research.
3.
AnsonM. (1998). Spot returns, roll yield and diversification with commodity. The Journal of Alternative Investments, 1(3), 16–32.
4.
BalcilarM., HammoudehS., & AsabaN. A. F. (2015). A regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates. International Review of Economics & Finance, 40, 72–89.
5.
BarberisN. (2000). Investing for the long run when returns are predictable. Journal of Finance, 55(1), 225–264.
6.
BharR., & HammoudehS. (2011). Commodities and financial variables: Analyzing relationships in a changing regime environment. International Review of Economics & Finance, 20(4), 469–484.
7.
BichettiD., & MaystreN. (2012). The synchronized and long-lasting structural change on commodity markets: Evidence from high frequency data (Munich Personal RePEc Archive Paper No. 37486). Geneva: UNCTAD.
8.
BrennanM. J., SchwartzE. S., & LagnadoR. (1997). Strategic asset allocation. Journal of Economic Dynamics and Control, 21(8–9), 1377–1403.
9.
CampbellJ. Y., & ViceiraL. M. (1999). Consumption and portfolio decisions when expected returns are time varying. The Quarterly Journal of Economics, 114(2), 433–495.
10.
ChanK. F., TreepongkarunaS., BrooksR., & GrayS. (2011). Asset market linkages: Evidence from financial, commodity and real estate assets. Journal of Banking & Finance, 35(6), 1415–1426.
11.
ChangK. L. (2012). Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market. Energy Economics, 34(1), 294–306.
12.
CharlotP., & MarimoutouV. (2014). On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree. Energy Economics, 44(C), 456–467.
13.
ChenS. S. (2010). Do higher oil prices push the stock market into bear territory?Energy Economics, 32(2), 490–495.
14.
ChenP., & YangH. (2011). Markowitz’s mean-variance asset–liability management with regime switching: A multi-period model. Applied Mathematical Finance, 18(1), 29–50.
15.
CheungC. S., & MiuP. (2010). Diversification benefits of commodity futures. Journal of International Financial Markets, Institutions & Money, 20(5), 451–474. 24p.
16.
ChoiK., & HammoudehS. (2010). Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment. Energy Policy, 38(8), 4388–4399.
17.
ChongJ., & MiffreJ. (2010). Conditional returns correlations between commodity futures and traditional assets. Journal of Alternative Investments, 12(3), 61–75.
18.
DaskalakiC., & SkiadopoulosG. (2011). Should investors include commodities in their portfolios after all? New evidence. Journal of Banking & Finance, 35(10), 2606–2626.
19.
DelatteA. L., & LopezC. (2013). Commodity and equity markets: Some stylized facts from a copula approach. Journal of Banking & Finance, 37(12), 5346–5356.
20.
ElliottR. J., SiuT. K., & BadescuA. (2010). On mean-variance portfolio selection under a hidden Markovian regime-switching model. Economic Modelling, 27(3), 678–686.
21.
ErbC., & HarveyR. (2006). The strategic and tactical value of commodity futures. Financial Analysts Journal, 62(2), 69–97.
22.
GuoF., ChenC. R., & HuangY. S. (2011). Markets contagion during financial crisis: A regime-switching approach. International Review of Economics & Finance, 20(1), 95–109.
23.
HamiltonJ. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 57(2), 357–384.
24.
HuangJ. Z., & ZhongZ. K. (2013). Time variation in diversification benefits of commodity, REITs, and TIPS. The Journal of Real Estate Finance and Economics, 46(1), 152–192.
25.
IrwinS. H., & LandaD. (1987). Real estate, futures, and gold as portfolio assets. The Journal of Portfolio Management, 14(1), 29–34.
26.
JensenG., JohnsonR. R., & MercerJ. M. (2000). Efficient use of commodity futures in diversified portfolios. The Journal of Futures Markets, 20(5), 489–506.
27.
KhalifaA. A., HammoudehS., & OtrantoE. (2014). Patterns of volatility transmissions within regime switching across GCC and global markets. International Review of Economics & Finance, 29, 512–524.
28.
KrolzigH. M. (1997). Markov-switching vector autoregression: Modelling, statistical inference, and application to business cycle analysis (Lecture Notes in Economic and Mathematical Systems). New York, NY: Springer.
29.
KumarD. (2014). Return and volatility transmission between gold and stock sectors: Application of portfolio management and hedging effectiveness. IIMB Management Review, 26(1), 5–16.
30.
LageshM. A., KasimC. M., & PaulS. (2014). Commodity futures indices and traditional asset markets in India: DCC evidence for portfolio diversification benefits. Global Business Review, 15(4), 777–793.
31.
LeeH. T. (2010). Regime switching correlation hedging. Journal of Banking & Finance, 34(11), 2728–2741.
32.
LiangK. Y., & YenC. H. (2014). Dissecting the cycles: An intermarket investigation and its implications to portfolio reallocation. International Review of Economics & Finance, 33, 39–51.
33.
MollickA. V., & AssefaT. A. (2013). US stock returns and oil prices: The tale from daily data and the 2008–2009 financial crises. Energy Economics, 36(C), 1–18.
34.
NaifarN., & Al DohaimanM. S. (2013). Nonlinear analysis among crude oil prices, stock markets’ return and macroeconomic variables. International Review of Economics & Finance, 27, 416–431.
35.
NomikosN. K., & PanosK. P. (2011). Forecasting petroleum futures markets volatility: The role of regimes and market conditions. Energy Economics, 33(2), 321–337.
36.
RaoD. T., & GoyalS. (2018). Global commodity and oil price movements, macroeconomic performance and challenges for an emerging economy: The Indian experience. Global Business Review, 19(3), 650–674.
37.
SilvennoinenA., & ThorpS. (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 42–65.
38.
SoucekM. (2013). Crude oil, equity and gold futures open interest co-movements. Energy Economics, 40(C), 306–315.
39.
SteenM., & GjolbergO. (2013). Are commodity markets characterized by herd behaviour?Applied Financial Economics, 23(1), 79–90.
ZhouX. Y., & YinG. (2003). Markowitz’s mean-variance portfolio selection with regime switching: A continuous-time model. SIAM Journal on Control and Optimization, 42(4), 1466–1482.
