Table of Contents
LIST OF FIGURES
LIST OF TABLES
LIST OF ABBREVIATIONS
LIST OF SYMBOLS
2 THEORETICAL BACKGROUND
2.1 REGULATION OF COMMODITY MARKETS
2.2 PECULIARITIES OF COMMODITY TRADING
2.2.1 Futures Trading for Hedging Purposes
2.2.2 Futures Trading for Speculative Purposes
3 LITERATURE REVIEW
4 EMPIRICAL ANALYSIS - HYPOTHESES AND METHODOLOGY
4.2.1 Definition of Fundamental Elements
4.2.2 Analysis Methodology
5 EMPIRICAL ANALYSIS - RESULTS
5.1 EFFECTS OF THE FIRST-TIME INTRODUCTION OF A DERIVATIVE
5.1.1 Volatility Analysis
5.1.2 Delta Analysis
5.1.3 Correlation Analysis
5.1.4 Regression Analysis
5.2 EFFECTS OF REGULATORY ACTS ON MARKET INTEGRATION AND COPPER PRICES
5.2.1 Commodity Futures Modernization Act
188.8.131.52 Volatility Analysis
184.108.40.206 Delta Analysis
220.127.116.11 Correlation Analysis
18.104.22.168 Regression Analysis
5.2.2 Increase in Speculative Position Limits
22.214.171.124 Volatility Analysis
126.96.36.199 Delta Analysis
188.8.131.52 Correlation Analysis
184.108.40.206 Regression Analysis
APPENDIX A: COMMODITY FUTURES TRADED IN THE UNITED STATES
APPENDIX B: TEST FOR NORMAL DISTRIBUTION
APPENDIX C: TEST OF THE MODEL CONDITIONS FOR CORRELATION ANALYSES
APPENDIX D: SCATTER PLOTS - COPPER SPOT VS. GSCI
APPENDIX E: SCATTER PLOTS - FITTED VS. RESIDUAL
APPENDIX F: PERCENTILES OF THE t-DISTRIBUTION
APPENDIX G: BME COPPER MODEL
List of Figures
Figure 1: Commodity Futures Market Sizep
Figure 2: Hedging and Speculation in 1998 and 2008
Figure 3: Spot Prices of Copper, GSCI, and S&P 500 between 1983 and 1993
Figure 4: Monthly Copper Spot Return Development between 1983 and 1993
Figure 5: Spot Prices of Copper, GSCI, and S&P 500 between 1995 and 2005
Figure 6: Monthly Copper Spot Return Development between 1996 and 2005
Figure 7: Spot Prices of Copper, GSCI, and S&P 500 between 2000 and 2010
Figure 8: Monthly Copper Spot Return Development between 2001 and 2010
Figure 9: Scatter Plot GSCI vs. Copper Spot (1983-88)
Figure 10: Scatter Plot GSCI vs. Copper Spot (1988-1993)
Figure 11: Scatter Plot GSCI vs. Copper Spot (1996-2000)
Figure 12: Scatter Plot GSCI vs. Copper Spot (2001-05)
Figure 13: Scatter Plot GSCI vs. Copper Spot (2006-10)
Figure 14: Scatter Plot - Fitted vs. Residual of GSCI and Copper Spot (1983-1988)
Figure 15: Scatter Plot - Fitted vs. Residual of GSCI and Copper Spot (1988-1993)
Figure 16: Scatter Plot - Fitted vs. Residual of GSCI and Copper Spot (1996-2000)
Figure 17: Scatter Plot - Fitted vs. Residual of GSCI and Copper Spot (2001-2005)
Figure 18: Scatter Plot - Fitted vs. Residual of GSCI and Copper Spot (2006-2010)
Figure 19: Copper Price: Actual vs. Modelled Price between 1990 and 2012 XVII
List of Tables
Table 1: Interpretation of ݎ-values as per Schlittgen
Table 2: Levene’s Variance Ratio Test - Copper Futures Introduction
Table 3: Unequal Variances Welch-Aspin Test - Copper Futures Introduction
Table 4: Pearson’s ݎ and Spearman’s Rho - Copper Futures Introduction
Table 5: Regression Results - Copper Futures Introduction
Table 6: Levene’s Variance Ratio Test - CFMA
Table 7: Unequal Variances Welch-Aspin Test - CFMA
Table 8: Pearson’s ݎ and Spearman’s Rho - CFMA
Table 9: Regression Results - CFMA - 31 -
Table 10: Levene’s Variance Ratio Test - Position Limit Increase
Table 11: Unequal Variances Welch-Aspin Test - Position Limit Increase
Table 12: Pearson’s ݎ and Spearman’s Rho - Position Limit Increase
Table 13: Regression Results - Position Limit Increase
Table 14: Commodity Futures Traded in the United States with Index Weights
Table 15: Skewness-Kurtosis-Test for all Sample Periods
Table 16: Percentiles of the ݐ-Distribution
List of Abbreviations
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List of Symbols
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"One of the chief dangers threatening growth today is the rising level of raw material prices. [...] It risks keeping millions of men and women in poverty. It could lead to social unrest [...] which would threaten the cohesion of the poorest countries. ”
- Nicolas Sarkozy, 2011, “Address by the President of the French Republic to the European Commission Conference on Commodities and Raw Materials”; p. 1
Commodity prices have been rising significantly since the early 2000s with price growth reaching its fastest pace between 2006 and 2008. While nearly all commodities were hit by the aforementioned price spikes, price spikes where particularly pronounced for mineral commodities. Platinum prices, for instance, nearly tripled from 2001 to 2008, whereas ferrous and non-ferrous scrap as well as copper prices even increased more than fivefold during that period. Particularly industries within the United States incurred large financial losses in the aftermath of the latest commodity price crisis due to a high dependency on imports of raw materials for industrial production (Cooney et al., 2008). In 2012, for example, US manufacturers were dependant on imports of raw material commodities for more than 40% of their production inputs (Perry, 2012). This dependence was particularly pronounced for gallium, cobalt, silicon, and copper (Salazar & McNutt, 2012). Since most of the aforementioned materials are heavily used for the production of consumer products, price spikes can significantly affect economic growth not only by increasing input costs but also by possibly increasing consumer prices. Combined with a world population growing by 80 million per year (World Bank, 2014) thereby causing an increasing demand for commodities for industrial production, the aforementioned developments demonstrate why it is important to fully understand the determinants of commodity price spikes.
According to Cooney et al. (2008), who analyze the determinants of mineral price spikes and increased price volatility, the recent price spikes have three main determinants: factors on the supply-side (such as increased pricing power of suppliers due to a consolidation of ownership of minerals companies), factors on the demand side (such as increased demand from emerging markets), and financial speculation on commodity markets. Multiple studies show that the price spikes of 2006-08 are much more severe than what could be explained by demand and supply side economics and are at least partially caused by market speculation. Tang and Xiong (2012), for instance, show that an increased presence of financial investors on commodity markets led to unprecedented market linkage between commodity and financial markets. The aforementioned development thereby induced significant volatility spillovers between both markets leading to price breaks independent from supply and demand fundamentals. This is backed by Nissanke (2012) who shows that increased interactions of commodity and financial markets led to significant price volatility in excess of what could be explained by supply and demand economics (see 3 Literature Review).
Pursuant to a World Bank Report (2011), the majority of the recent increase in financial transactions on commodity markets has occurred in the derivatives market. Commodity derivatives have originally been designed as a hedging instrument for producers who want to stabilize their future returns by ensuring a fixed price for their production in the future. However, due to the negative correlation of commodity futures returns to equity and bond returns (Gorton & Rouwenhorst, 2006) and the deregulation of commodity futures markets in 2000 (Schumann, 2011) commodity derivatives became increasingly attractive for index and portfolio investors; a process that is often referred to as the financialization of commodity markets (Cheng & Xiong, 2013). The aforementioned increasing presence of index investors on commodity markets also induced a rapidly growing integration between commodity and financial markets as well as between different commodity markets leading to an unprecedented spillover of volatility from financial markets to commodity markets (Tang & Xiong, 2012).
For the most part of recent research two different approaches are applied to measure the impact of speculation on price development. The first one examines if there is any change in commodity price development due to the aforementioned increased financialization of commodity markets (e.g. Tang and Xiong (2012)), whereas the second one compares the behavior of commodity prices with and without an existing futures market (e.g. Korniotis (2009)).
This thesis combines both approaches and tests the hypotheses that either the first-time introduction of derivatives or the introductions of regulatory governmental acts that facilitate speculative index investment in commodities have significant effects on commodity price development by the example of copper traded on US-based exchanges. For this purpose, relevant copper price characteristics will be analyzed before and after possibly speculation-conducive events (i.e. the introduction of copper futures trading and two selected acts) for the period from 1971 to 2010. Furthermore, following Tang and Xiong (2012), this thesis examines if the introduction of governmental acts of the aforementioned type induces an increased market integration of non-energy commodity markets. This is of particular interest as market integration can at times induce increased volatility spillovers between the respective markets (Tang & Xiong, 2012).
The first part of this work gives a short overview of the technical background necessary to understand the relationship between commodity prices and speculation. The second part provides a general review of related literature and research on the relationship between commodity prices and speculation. The third part focuses on the methodology of the empirical analysis. The fourth part of this work presents the results and is followed by the conclusion in part five.
For the chosen sample the results show that the first-time introduction of the analyzed derivative had no significant effects on the evaluated copper price characteristics. These results contrast the findings of Ray and Panda (2011) who found that the introduction of futures significantly increased spot return volatility and induced a disintegration of the underlying and its respective benchmark index. The results, furthermore, show that for the chosen sample no distinct conclusion can be drawn whether governmental acts induce structural changes in commodity price development. However, for the period after 2005 the results indeed indicate unprecedented market integration between non- energy commodity markets inducing a significant increase in copper spot return volatility and an increased correlation between copper and index spot returns for the chosen sample. The results thereby complement the findings of Tang and Xiong (2012) which show that both, volatility of commodity returns and market integration between energy and non-energy commodities, significantly increased since the early 2000s.
2 Theoretical Background
2.1 Regulation of Commodity Markets
Although increasingly used by traders, first exchange-based commodity futures markets were only poorly regulated (Williams, 1982). It was not until the Commodity Exchange Act was passed in 1936 that federal regulation was extended to all exchange-traded commodities. In 1974, the first national regulatory authority, the Commodity Futures Trading Commission (CFTC) was founded with the aim “to protect market users and the public from fraud, manipulation, and abusive practices related to the sale of commodity and financial futures and options” (CFTC, 2014a). Further amended by the Futures Trading Act in 1982, the CFTC also legalized options trading on commodities and imposed limitations on speculative trading activities by restricting the size of positions and restraining the possibility of short-selling (Santos, 2006).
However, beginning with the Commodity Futures Modernization Act (CFMA) of 2000 which offset all hitherto enforced regulations concerning over-the-counter (OTC) derivative trading on commodities and raised position limits for multiple commodities, U.S. authorities began to deregulate the market leading to an increased presence of financial investors on commodity markets (Schumann, 2011). The climax of deregulation was reached in 2005 when the CFTC sharply increased speculative position limits for corn, wheat, and soybeans thereby facilitating the participation of pension and other investment funds on a large scale (Staritz & Küblböck, 2013). The consequences of the aforementioned deregulation are shown by Büyüksahin, Haigh, and Robe (2010) who demonstrate that by 1999 investments in Goldman Sachs Commodity Index (GSCI)-related financial products amounted to $5 billion. By the third quarter of 2008, however, trading volume of financial products linked to the GSCI or one of five other prominent commodity-related indices rose to $140 billion.
Figure 1 shows that the aforementioned increase in trading volume in commodity indexrelated products is accompanied by a significant surge in the trading volume of commodity futures, measured by net open interest for the largest contracts in a data set of six commodities.
A graphical analysis of figure 1 shows the significant increase of the market size for all six commodities after the CFMA on December 21, 2000 (Dwyer, Gardner, & Williams, 2011)
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Figure 1: Commodity Futures Market Size(Illustration by Dwyer, Gardner and Williams (2011))
2.2 Peculiarities of Commodity Trading
There are two types of commodity futures contracts: contracts that induce physical delivery of the underlying commodity and contracts that induce a future cash settlement at expiration of the contract (Bose, 2008). In contrast to futures on stocks or bonds commodity futures represent claims on real assets and not claims on other financial products. More precisely, unlike stocks or bonds, the underlying commodity is physically deliverable. As reported by Bose (2008), the fact that traders can take or make physical delivery ensures that the settlement price incorporated in futures as the strike price will more accurately reflect the expected cash market value of the underlying commodity at the expiration date of the contract.1 Another important difference is that unlike most futures on conventional assets, commodity futures are subject to seasonal fluctuations in price levels and volatility depending on the development of the underlying commodity (Gorton & Rouwenhorst, 2006).
In general, trading in commodity futures can be divided into two broad categories: trading for hedging purposes and trading for speculative purposes.
2.2.1 Futures Trading for Hedging Purposes
According to Bose (2008), hedging describes the practice to offset the price of a position in the cash market or a position of physically held commodities with an equal but opposite position in the futures market. This definition is further developed by the Code of Federal Regulations in which trading for hedging purposes (‘bona fide hedging’) is defined as any transaction or position that secures a fixed price in the future for the value of a physical asset position, the value of liabilities, or the value of a service that a person offers in the future. ‘Bona fide hedging’, therefore, shall not describe any positions that are undertaken without the purpose “to offset price risks incidental to commercial cash or spot operations” (Code of Federal Regulations, 2014).
Another possibility to hedge against price fluctuations besides futures is a commodity swap. In contrast to a common interest rate swap in which traders swap a fixed interest rate with a floating or variable interest rate, the involved parties swap a fixed price with a floating or variable price for the underlying commodity. Similar to cash-settled futures, commodity swaps can only be used to hedge the eventual physical transaction of the commodity. Therefore, traders using a commodity swap to hedge commodities in their possession still have to sell the respective underlying to a possibly different party after the transaction (CME Group, 2009).
Since hedging market participants in most cases trade to ensure a fixed price for their commodities in the future, they typically deal on the short side of the market. According to the hedging pressure theory of Keynes (1923) and Hicks (1939, pp. 135-140), hedging market participants, therefore, have to offer risk premiums to motivate investor activity on the long side of the market. This pressure can be ameliorated by speculative market activity as found by Tang and Xiong (2012) who show that speculative activity inter alia increases market liquidity and risk-sharing between different market participants. However, research results of Domanski and Heath (2007) show that by 2005 the volume of exchange-traded derivatives reached a level that is approximately 30 times larger than physical production indicating that trading activity goes far beyond the volume needed for hedging and market liquidity purposes.
2.2.2 Futures Trading for Speculative Purposes
A common definition of commodity speculation is the one used by Pindyck and Knittel (2013, p. 4) who define speculation as “the purchase (or sale) of an […] asset with the expectation that the price of the asset will rise (or fall) to create the opportunity for a capital gain.” According to Garcia (2008), speculation is, furthermore, distinguishable from a non-speculative investment to the effect that speculation explicitly excludes a commitment to manage the underlying commodity and is limited to capital movement.
Following Vargas and Chantry (2011), speculation in commodities can be divided into three categories: Direct hoarding, speculation in futures markets, and speculation with complex financial products.
Direct hoarding describes the oldest form of commodity speculation. It consists of storing the commodity without an actual physical need for the respective commodity in the hope that the price of the commodity will rise in the future. As it is a commonplace operation it is hardly distinguishable from non-speculative trading activity and can be performed by all types of financial investors, for example stockbrokers, banks, or commercial companies (Vargas & Chantry, 2011).
Speculation in futures markets is the most common type of speculation and is characterized by financial investors who buy and sell futures contracts in the expectation of a single-sided price movement of the underlying. Capital gains can be accomplished either by buying or selling contracts for difference (CFDs)2 or offsetting futures contracts with physical delivery before or on the expiration date of the contract (Vargas & Chantry, 2011).
Speculation with complex financial products is similar to speculation in futures markets. However, this type of speculation is carried out by means of increasingly innovative financial products such as options and funds that explicitly track single commodities or commodity indices, e.g. Exchange-Traded Funds (ETFs) and Commodity Index Funds (Vargas & Chantry, 2011).
Figure 2 shows the development of the different types of speculation between 1998 and 2008 by the example of commercial and non-commercial wheat futures trading at the Chicago Board of Trade (CBOT). Commercial traders are typically producers who hedge their products against unexpected future price changes. Accordingly, the CFTC defines a commercial trader as a trader who “uses futures contracts in that particular commodity for hedging purposes” (CFTC, 2014b). Since they use futures solely for hedging purposes, they have drastically relaxed regulatory limits on traded quantities in comparison to non-commercial traders (CFTC, 2014c). As it is difficult to differentiate between commodities held for speculative purposes (hoarding) and commodities held for production or consumption purposes, only the second and third type of speculation are included in the analysis.
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Figure 2: Hedging and Speculation in 1998 and 2008 (Own Illustration based on Schumann (2011))
In 1998, the futures market for wheat was solely divided between commercial investors and speculative non-commercial investors with commercial investors accounting for approximately 70% of total trading. In 2008, however, commercial investors only accounted for one quarter of total trading activity with the remaining three quarters of the market being distributed between non-commercial traders and index funds (attributed to the third type of speculation). The graphic not only shows a significant increase in market share of speculative non-commercial trading but also a large market share for index funds, a type of investment activity that had no significant share in 1998.
3 Literature Review
In recent scientific literature there has been a controversial discussion about the relationship between speculation and extreme commodity price development.
Gorton and Rouwenhorst (2006) empirically study the characteristics of commodity futures as an asset class by constructing an equally weighted performance index of monthly returns of commodity futures for the period between 1959 and 2004. The focus of their study was to examine in which direction different macroeconomic variables influence commodity futures returns. Their main finding is that commodity futures returns are negatively correlated to stock and bond returns. A possible explanation for this negative correlation might be the different, seasonal-dependant behavior of commodities over the business cycle. Furthermore, the authors found a positive correlation of commodity futures to inflation, unexpected inflation, and changes in expected inflation.
These findings are further examined by Erb and Harvey (2006) who analyze the strategic value of commodity futures for portfolios. By showing that commodity futures have long-term average excess returns3 of approximately zero, the authors point out that portfolio investors cannot solely rely on the negative correlation of commodity futures returns to stock and bond returns. Instead, investors should rather focus on diversification returns to value the contribution of an investment in commodity futures to their portfolio. They, furthermore, show that momentum strategies and strategies based on information on term structures of futures prices can achieve abnormal positive returns while pointing out that there is no guarantee that historical patterns will persist in the future.
Silvennoinen and Thorp (2013) support the hypothesis that commodities are not necessarily a risk-minimizing portfolio element. More precisely, they show that stronger investor interest in commodity markets induces increasing positive correlations between bond, stock, and commodity returns through an increased integration of commodity markets with conventional asset markets over the period of 1990 to 2009. The assertion of increased market integration is further backed by the result that increasing stock market volatility measured by the CBOE volatility index and expanding financial traders’ short open interest positions induce increasing futures return volatility for several commodities.
Tang and Xiong (2012) demonstrate that the increase in investments in commodity indices due to an increased financialization of commodity markets lead to an unprecedented spillover of volatility from financial markets to commodity markets reinforcing the recent price spikes of commodities. Their study is based on an analysis of 28 commodities which they classify into indexed and non-indexed commodities. To measure the differences in price development of indexed and non-indexed commodities, they analyze the one-year rolling correlations between commodity returns and returns on West Texas Intermediate (WTI) crude oil. They show that for the sample period from 1973 to 2009 indexed non-energy commodity prices have a far greater volatility and a higher correlation with crude oil prices than non-indexed commodity prices. Furthermore, they found that both, volatility of commodity returns and correlation between commodity and crude oil returns, significantly increased since the early 2000s. This finding does not only reflect the recent financialization of commodity markets but also explains part of the 2006-08 price spikes and demonstrates that commodity markets are becoming increasingly integrated with each other. However, the authors also show that financialization of commodity markets induces an increase in long positions in commodity futures thereby increasing market liquidity and risk-sharing.
Domanski and Heath (2007) show that the integration of commodity markets with other conventional asset markets led to a partial assimilation of commodity markets to financial markets. More precisely, the authors found that financial investors are increasingly present on both sides of trades thereby creating a “financial trading sphere” (Domanski & Heath, 2007, p. 14). However, the authors also point out that basic characteristics of commodity markets as physical markets such as inventory levels and marginal costs of production remained unchanged. In contrast to the findings of Tang and Xiong (2012), the authors find that the financialization of commodity markets led to a lack of liquidity. This is particularly important as a lack of liquidity and physical limits to short-selling imposed upon commodity markets can at times significantly affect market dynamics.
Nissanke (2012) further examines the influence of the increasingly strong link between financial markets and commodity markets found by Silvennoinen and Thorp (2013) by analyzing the transmission of the global financial crisis of 2007-2009 on developing countries through immediate spillovers. By analyzing the price development of agricultural commodities, crude oil, and minerals during the 2000-2011 period, the author shows that the commodity price increase during 2002-2008 is predominantly attributed to an increased demand from industrial emerging economies as well as to an increase in inventories. However, the author also demonstrates that the increased presence of financial investors in commodity markets after the deregulations in 2000 and 2005 at least partially contributed to the recent rise in commodity prices.
Corbet (2012) further examines the aforementioned increase in index investment by analyzing the effects of the introduction of commodity-specific ETFs for various commodities. The author supports the assertion that the surge in index investment partially contributed to recent price spikes on commodity markets by showing that the introduction of large-scale commodity-specific ETFs significantly increased spot return volatility of the respective commodity. However, the study also indicates that for small commodity markets increased ETF investment proves to be beneficial in terms of increased efficiency and liquidity. These results are partially supported by Ray and Panda (2011) who show that the introduction of futures trading led to an increased volatility of the underlying stocks on Indian equity markets. They also demonstrate that most of the analyzed stocks show an increased disintegration with market benchmark indices after the introduction of futures trading.
On the other hand, Stoll and Whaley (2009) conclude that commodity index in- and outflows have little futures price impact by showing that there are no differences in price development of commodities known to be part of index investment programs and commodities known to be not part of index investment programs for the 2006-09 period. The authors, moreover, show that periodic future contract rolls necessary to mimic the respective commodity index correctly have no significant impact on futures price development. This supports their conclusion that index investment has no impact on futures prices.
Research by Bohl and Stephan (2013) supports this assertion by analyzing how conditional volatility of six agricultural and energy commodities, all of which characterized by an extraordinary high increase in speculative activity between 2002 and 2012, is affected by speculative open interest. To analyze if there is any impact of the recent financialization of commodity markets on the relationship between volatility and speculative open interest, the data set is split into two subsets ranging from 1992 to 2002 and 2002 to 2012. As conditional volatility does not change significantly between the two sub-periods, the authors conclude that financialization has no destabilizing influence on commodity prices. This is backed by Korniotis (2009) who shows that price correlation between metal commodities with and without an existing futures market has not weakened in recent years, although speculative activity has increased. Furthermore, the author demonstrates that financial speculation, in general, had no influence on the recent metal spot price inflation using GSCI spot returns as a proxy for speculative activity.
Pindyck and Knittel (2013) show that speculation has little, if any, effect on prices and volatility by the example of price development of crude oil. Their research is motivated by the theory of storage4 and is based on the assumption that inventory levels must increase with increasing speculative activity if speculation has significant influence on futures prices. Moreover, the authors build a model for a counterfactual commodity price that would have occurred without any speculation for the period between 1999 and 2012 and which is solely based on supply and demand in storage and cash markets. The results indicate that the price development of crude oil is consistent with data on production, inventory changes, and changes in convenience yields and that, therefore, speculation is not the main driver of crude oil price changes.
Irwin, Garcia, and Good (2007) analyze the influence of the change in position limits by the CFTC in 2005 for the futures contracts of corn, soybean, and wheat by comparing the behavior of futures contracts with respect to liquidity, volatility, and convergence before and after the change in speculative limits. Their analysis of market liquidity and depth for the 2001 to 2006 period shows a significant increase in speculative net open interest for corn, soybean, and wheat futures after the limit increase. While the analysis of price volatility, however, reveals no change in volatility after the increase in limits, the analysis of convergence for wheat markets indicates failure of the futures market in its function to provide reliable hedging instruments to market participants.
4 Empirical Analysis - Hypotheses and Methodology
The focus of this thesis is to determine if speculation has significant influence on price behavior of commodities. For this purpose two approaches will be employed in the following.
The first approach further develops the research approach of Ray and Panda (2011) who analyze the impact of the introduction of futures trading on relevant price characteristics of stocks listed on Indian equity markets. Since the approach was only applied to the Indian equity markets it will be modified to fit the special case of the introduction of commodity derivatives trading on US-based exchanges. More precisely, this thesis examines exemplarily for copper if there is any change in price behavior of a commodity that is directly induced by the first-time introduction of a derivative on the respective commodity. Therefore, the first hypothesis is:
H1. The first — time introduction of derivatives trading induces significant changes in the price development of the respective under-lying commodity
The second approach is based on research results of Tang and Xiong (2012) who show that an increased integration between commodity and financial markets initiated by a deregulation of commodity markets after 2000 led to a significant increase of speculative index investment volume and an increased correlation between prices of non-energy commodities with oil prices. To determine whether speculative activity and market integration between non-energy commodity markets have increased due to governmental acts that facilitate speculative index investment, a similar approach to the one of Irwin, Garcia, and Good (2007), who analyzed whether the position limit increase in 2005 induced significant changes in price characteristics, will be applied. More precisely, this thesis analyzes exemplarily for copper whether regulatory governmental acts that facilitate speculative index investment have a significant effect on market integration between non-energy commodities and thereby on commodity price development on US-based exchanges. Hence, the second hypothesis:
H2: Governmental acts that facilitate speculative commodity index investment have significant effects on market integration of non — energy commodities
4.2.1 Definition of Fundamental Elements
To further analyze possible structural changes induced by one of the aforementioned events, it is necessary to compare relevant price characteristics of the underlying commodity during a sample period before and after the respective event.
Based on Ray and Panda (2011), relevant price characteristics that might change due to one of the analyzed events are spot return volatility and the correlation between commodity spot returns and returns of a popular commodity index. Therefore, these characteristics will be analyzed in the following. Additionally, the development of the delta between commodity index returns and commodity spot returns will be analyzed as a further indicator for a possibly increased co-movement between the respective commodity and the analyzed commodity index. In a final step, a linear ordinary-least- squares (OLS) regression will be run with commodity spot returns as the dependant variable and commodity and equity index returns as the independent variables to account for eventual dependencies among the analyzed variables.
Since the United States account for the two largest derivatives exchanges (Acworth, 2014) and two of the three largest commodity exchanges of the world (UNCTAD Secretariat, 2004), the analysis will be focused solely on US-based commodity exchanges. Only monthly data will be used to prevent possible contortions by extreme daily returns.
However, before any specific analysis can be conducted it is important to define five fundamental elements for the analyses: the sample size, the type of derivative for the first-time introduction date, the commodity index that will be examined, the underlying commodity for the test of the first hypothesis, and the regulatory governmental acts for the test of the second hypothesis.
Selection of the Sample Size
Following the law of large numbers, a large sample yields more meaningful results for the examined characteristics (Etemadi, 1981). However, a too large data set would blur the specific effects of the respective event, which are likely to be more significant directly after the event. Therefore, a data set of ten years (60 monthly returns before the event and 60 monthly returns after the event) seems to be appropriate.
Selection of the Derivative Pivotal for the First-Time Introduction Date
Available instruments for speculation on commodities include spot trading, futures, forwards, and options on futures (National Futures Association, 2006). Since spot trading for speculation purposes can hardly be distinguished from spot trading for hedging or production purposes (Vargas & Chantry, 2011) and since forwards are only individually traded in OTC transactions (Hull, 2009, p. 39), only futures and options on futures are relevant for the analysis. Furthermore, a comparison of monthly average trading volumes of options and futures of the Chicago Mercantile Exchange Group (CME Group), the world’s largest derivatives exchange, shows that monthly average trading volume for metals, energies, and other commodities as of July 2014 is five times higher for futures than for options over the same period (CME Group, 2014). Additionally, due to the dependency on the existence of the underlying future, options on futures can only be introduced after or on the same date as futures on the same underlying. Therefore, this thesis will focus on possible effects of the introduction of futures trading on the price development of the underlying.
Selection of the Relevant Commodity Index
According to Tang and Xiong (2012), the two most popular commodity indices by market share are the GSCI and the DJ-UBSCI (BCOM as of July1, 2014). The main difference between the two indices is the applied weighting and selection scheme: the DJ-UBSCI is weighted by two thirds the relative amount of trading activity and one third the world production of each commodity (Bloomberg, 2014), while the GSCI is weighted by each commodity’s world production with market liquidity thresholds every commodity has to fulfill (Standard & Poor's, 2014).
1 Hansen and Hodrick (1980), however, have found that forward and futures exchange rates ignore risk considerations and conclude that forward and futures exchange rates do not accurately predict future spot rates. This finding is also applicable to commodity futures prices and future commodity spot prices.
2 According to the CFTC, a CFD is “an agreement to exchange the difference in value of an underlying asset between the time at which a CFD position is established and the time at which it is terminated” (CFTC, 2012). CFDs are not traded on exchanges within the US due to the possibility of speculative malpractice.
3 With regard to the risk-free rate of return
4 The theory of storage for commodity markets relates futures prices to inventory levels. It states that if inventory levels are high, futures prices tend to be in contango and the volatility of spot and futures prices tend to be low and approximately equal. Vice versa, if inventory levels are low, futures prices tend to be in backwardation and the volatility of short-term futures price increases with regard to the volatility for long-term futures prices (Working, 1933).
- Arbeit zitieren
- Nicolas Schreiber (Autor), 2014, The Influence of Commodity Speculation on Commodity Price Development, München, GRIN Verlag, https://www.grin.com/document/336450