When the stock or bond market displays poor performance or has a significant downside movement, commodity futures market usually shows strong performance and provides a good return. (Stagnancy & Variants,1994;Kaplan and Lumber,1998; Abandoned & Matter,1999; Manson, M. P. ,1999; Becker and Finery, 2000; ERP and Harvey,2006; Abbots,2006; Chemung & MIM,2010; Convey et al. ,2010) also analyzed the benefits of including commodity futures in an investment portfolio and concluded that including commodity futures in investment portfolios produces a significant risk/return benefit.

Portfolio Selection as introduced by Harry Margarita [39] laid the foundation for Modern Portfolio Theory. However, the major limitation of this theory was its assumption that underlying asset returns follow a normal distribution. Time-varying aspects of the assets performance like keenness, kurtosis, serial correlation, and time-varying means and variances are not considered in this model. These can only be examined using a dynamic model.

Another important weakness of the mean-variance model is that optimal portfolio allocations are based on historical average performance of asset returns. Event risk and market extreme movement are not considered. Financial markets are subject to extreme variations, mostly because of financial ormolu, large credit defaults, war, nature disaster, and political crisis. These market extreme movements will result in substantial negative returns. Neither can the investors ignore the lessons of 2008 nor assume that returns are completely independent of economic variables.

Fund managers monitoring their portfolio risk only based on historical average information might suffer a serious loss when the market moves extremely in an adverse direction. A growing body of literature documents the need for approaches that account for multiple market regimes particularly. Therefore there is a need of a practical balancing strategy that represents a convergence of literature on Regime Switching, and Dynamic Portfolio Optimization..

My primary interest in this dissertation lies in studying the return and risk of commodity futures in relation to strategic asset allocation of institutional investors, I. E. The allocation to broad asset classes such as stocks, bonds, and commodity futures in context of regime switching environment. Theoretical background Modem asset pricing models tell us that expected asset returns are related to their sensitivity to changes in the state of the economy, or business cycle fluctuations. For example, Morton (1973) develops an international capital asset pricing model with stochastic investment opportunities.

Numerous studies have documented the relations between asset returns and business cycle related variables, like dividend yields, default spreads and term spreads (Fame and French, 1989, Person and Harvey, 1998). Empirical studies have shown that almost all financial and economic series exhibit structural breaks and the risk/return characteristics of commodity futures and financial assets will be different under distinct economic regimes. Regime Switching oodles build on the seminal work by Hamilton (1989) tends to capture this differential behavior under different economic variables.

In its simplest form, a regime switching model allows the data to be drawn from two or more possible distributions (“regimes”), where the transition from one regime to another is driven by the realization of a discrete variable (the regime), which follows a Markova chain process. That is, at each point of time, there is a certain probability that the process will stay in the same regime next period. Alternatively, it might transition to another game next period. These transition probabilities may be constant or they may depend on other variable. Literature Supporting Regime switching behavior in economic and financial market: .

Antonio Garbanzo, Allan Attainment (2014) found that commodity price predictability is closely linked to the economic cycle. Paolo Galatia (2010) study the dynamics of oil futures prices in the ENZYME using a large panel dataset that includes global macroeconomic indicators, financial market indices, quantities and prices of energy products. Michael Stouten (2013) found that during periods of unstable uncial markets, the correlation between equity and energy futures open interest decreases, and the correlation of the open interest on the equity and gold futures market turnstone negative.

Hudson Wang, Congener Www, Lie Yawning (2014) In pre- crisis period, the responses of agricultural commodity prices to oil supply shock or other oil-specific demand shocks are not significant. However, after crisis the they are highly significant,. Chili Wald a, Allow Shaker a, Omar Massed b, John Fry (2011) provide strong evidence that the relationship between stock and foreign exchange arrest is regime dependent and stockpile volatility responds asymmetrically to events in the foreign exchange market. Juan C.

Rebooked (2012) found that an increase in oil prices is weakly associated with USED depreciation and vice versa. Kiang J, Yin Fan (2012) found that level of correlation between crude oil market and non-energy markets increases significantly after crises. Kumar (2014) found that conditional correlation between gold and stock market rises during periods of market turbulence and crisis indicating the scope of portfolio diversification and hedging during these periods. Shih-Sheen Chem. (2010) found evidence that higher he oil price, the higher is the probability of switching from a bull market to a bear market.

George Fills (2010) examines the relationship among consumer price index, industrial production, stock market and oil prices and found that oil prices exercise significant negative influence to the stock market and ICP and no influence on Industrial Production. And© Verbal Million , Tiber Beebe Safes (2013) found that prior to the financial crisis, stock returns are slightly (negatively) affected by oil prices and USED/Euro, however, post crises stock returns are positively affected by oil prices and a weaker USED/Euro..

Philippe Chariot , V©layout Moratorium (2014) found that correlation between exchange rate,equity ,crude and commodities switch from one regiment another, touching a peak during the period of the Supreme crisis in the US, and again during the days following the Took earthquake in Japan. Literature supporting Portfolio return optimization under regime switching: There is a growing body of literature that deals specifically with the issue of Portfolio Allocation under Regime Switching.

Nag and Bakery’s paper, “International Asset Allocation with Regime Shifts,” model the Dynamic Asset Allocation Problem in the resent of regime switches for investors with CRA preferences (Andrew and Beakers. ). They examine the effects of asymmetric correlation on the benefit of International Asset Diversification by modeling a US investor with CRA preference maximizing end of period wealth and dynamically refinancing in response to regime switches.

In the paper, “Optimal Portfolio Choice under Regime Switching, Skew and Kurtosis Preferences,”( Misaims & Attainment) Guideline and Attainment model a “Buy and Hold” investor’s choice of a Simple Stock Portfolio and Risk Free asset over a finite time horizon under a Markova Switching Vector Autoregressive Process and CRA utility. In, “Asset Allocation under Multivariate Regime Switching,” Guideline and Attainment expand on their previous work to explore the Asset Allocation in the presence of regimes in the Joint distribution of stock and bond returns Geoff A Bibles. They found that Optimal Asset Allocation varies significantly across regimes and length of the investment horizon. These papers provide a theoretical basis for our work. Research Gap: As quite evident from the section literature on regime switching behavior between economic and financial variables that the literature on the issue of integration of efferent economic and financial variables under different regime is quite emerging.

Till now huge literature was available on regime switching in context of single time series like interest rate,longitudinal production,or stock market movement etc but the empirical studies on their complement are still evolving. Additionally there is only a handful of studies that has studies dynamic portfolio optimization under regime switching environment. But these portfolio typically consist of only stock and bonds and commodities were not part of these portfolio. As different commodities (Energy, Metal, Agriculture) behave differently under different games therefore optimal portfolio will be different under different economic conditions.

Therefore the result of our study is supposed to be entirely different Lastly all the studies reviewed above in context of the diversification aspect of commodity futures were done in developed commodity markets typically US but in India Commodities market are still at a very nascent stage as compared to developed markets. Majority of the published research in India on commodity futures had focused on the issue of market efficiency by comparing volatility of the agricultural arrest before and after the introduction of commodity futures.

Some researchers also focused upon backdating and constant markets but the risk diversification aspect of commodity futures relatively remains unaddressed. Therefore in this study an attempt will be made to evaluate return and risk of commodity futures in India in relation to strategic asset allocation of investors in portfolio in context of regime switching environment. Research Objectives: 1 . To Investigate stand-alone and counter cyclical behavior of commodity futures with stocks and bond. 2.

To evaluate portfolio performance of commodity futures. . To validate that commodity futures and traditional financial markets display regime switching behavior and their risk/return characters will change under different market and economic conditions. 4. To evaluate whether economic variable can be used as the instrument to predict asset return dynamics and optimal portfolio allocation. Sample and Dataset: The main focus will be on overall investment performance of these asset classes not the individual security/component of the asset class.

Therefore for each investment asset class a composite index indicating the overall event and performance of a particular investment asset can be considered. Equity/Stock Returns: SENSES might be considered as a proxy for returns on equity investment. Bond Returns: Wholesale debt markets return of NOSE might be considered as a proxy for bond return Commodity Indices: One of the most attractive aspects of commodity investment today is that there are now a number of passive indexes that are fully invertible.

Agriculture Returns: Multi Commodity Exchange Agriculture index might be considered as a proxy of agriculture sector returns. Energy Returns: Multi Commodity Exchange Energy index might be considered as a proxy of energy sector returns. Metal Returns: Multi Commodity Exchange Metal index might be considered as a proxy of metal sector returns. Composite Commodity Returns: For composite commodity returns MIX COMDEX has been taken as a proxy. The MIX COMDEX is the simple weighted average of the three group indices – MIX AGAR, MIX METAL & MIX ENERGY. The group indices are computed based on Geometric Mean.

In order to predict the shifts in the economic environment data on economic variables like Interest rate and Exchange rate (on daily basis) and other variables like Industrial reduction, Money supply, and Inflation rate might be considered on monthly basis. Methodology: Objective 1 . Risk return Analysis, Correlation Analysis, Inflation hedge and downside risk protection.

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