Notes

Introduction

1. Based on the data of the Bank of International Settlements (www.bis.org).

1 Asset Allocation in Markets

1. The term “top-down” denotes a mode of investment decisions in which the portfolio manager makes the choice of instruments, starting with the broadest category (markets, industries) and ending with the most specific one (individual stocks, instruments). 2. Without taking taxes into account, since, when ignoring this assumption, the matters. 3. Another classification is referred to by Schneeweis, Crowder, and Kazemi (2010, pp. 91–108), who divide the allocation into strategic allocation, tactical alloca- tion, and dynamic allocation. 4. The concepts of active and passive investment strategies in relation to the com- modity markets have been used, for example, in studies by McFall Lamm (2005) and Engelke and Yuen (2008).

2 Passive Investment Strategies in Commodity Markets

1. The basic form of a futures contract is the same as of a forward contract. The difference is that the futures are settled at the end of each day. We can say that a forward contract is like a string of futures contracts. The credit risk of the futures contract is virtually eliminated (but, of course, there is a probabil- ity that the counterpart fails to complete the contract), because the parties to the transaction pay the initial margin (when the account balance falls below a certain agreed minimum, the investor must submit an additional margin; if he does not, his position will be closed before the margin is used). The futures con- tracts are standardized; the underlying assets, the maturity dates of the contracts (four per year), contract volumes; for example, only the price is negotiated, as opposed to the forward contracts, which are all negotiated (definition by http:// pl.wikipedia.org/wiki/Futures, accessed on August 12, 2013). 2. This means that the settlement of the contract was done through physical deliv- ery of the goods. 3. The term “contango” refers to a situation where the spot price is lower than the forward price or the forward curve is rising in a given part. The reverse situation is referred to as a backwardation. 212 Notes

4. Refer to chapter 4. 5. Rebalancing implies making periodic transactions that lead to restoring the original (defined) share of each instrument in the investment portfolio. 6. In the literature, there is no consistency in nomenclature, so, depending on a particular case, one can use both the terms “hypothesis” and “theory.” 7. The word “benchmark” derives from the English bench mark, which means a point of known elevation above sea level, which is an indicative sign; that is, a kind of reference point used for further leveling measurements. Today, the term “benchmark” is generally used in the field of finance as a reference portfolio that represents the properties of certain assets or instruments. Benchmarks are, among others, references in the investment efficiency assessment. In this book, benchmarks refer to the reference portfolios reflecting the characteristics of the investment in the commodity markets. 8. Detailed information on the methods of calculating the individual indices for commodity markets can be found in Blanch and Scheis (2006).

3 Active Investment Strategies in Commodity Markets

1. www.nfa.futures.org. 2. According to www.cftc.gov. 3. In the literature, the systems based on two moving averages are referred to as variable length moving averages (VMAs). 4. The following overview is made on the basis of Schneeweis, Gupta, and Remillard (2008), “CTA/Managed Futures Strategy Benchmarks. Performance and Review,” in F. J. Fabozzi, R. Fuss, D. G. Kaiser (eds), The Handbook of Commodity Investing, John Wiley, Hoboken, NJ. 5. The databases, which contain historical results entered into the databases upon entry of the fund to the database (backfilled returns), are, among others, HFR and Lipper TASS, of which the latter is larger. Unfortunately, this option is unavailable in the Barclays records and, even worse, CISDM, which currently keeps the largest CTA library. 6. The attrition rate (mortality rate) is normally calculated on an annual basis as a percentage of the funds that have ceased to operate. For example, if at the beginning of the year, there were 100 funds, and by the end of the year ten funds disappeared from the market, then “fund mortality” is 10 percent. 7. The managed futures indices are usually calculated once a month. 8. J. Lintner (1983), The Potential Role of Managed Commodity—Financial Futures Accounts (and/or Funds) in Portfolio of Stocks and Bonds, presented at the Annual Conference of the Financial Analyst Federation, May, Toronto, Canada, http://www.mikeennis.com/m_futures/original_linter.pdf, accessed September 7, 2014.

4 Financialization of Commodity Markets

1. It should be noted that the term “financialization” usually refers to three differ- ent phenomena, which are characterized by differences in their scope. The first Notes 213

two concepts are partially correlated (Bastourre et al. 2008). The first phenom- enon is the significant development of the derivatives markets in recent years and the second one is the increasing commitment of the investors to commod- ity futures markets, which took place at the same time. The third interpreta- tion is the broadest one; it is the overall increase of the importance of financial institutions in the economy (see, for example, Epstein (2006), Paley (2013), or Lapavitsas (2013)). 2. Nevertheless, it is necessary to point out that HMC’s 2013 annual report shows actually a reduced allocation to of 2 percent of the portfolio. 3. This issue is further investigated by Zaremba (2014b). 4. This section uses the considerations contained in the working paper by Zaremba (2014c). 5. http://www.barclayhedge.com/research/indices/cta/Money_Under_Management .html. 6. A broad overview of current research results on can be found, among others, in studies by: Irwin and Park (2007), Irwin and Park (2008) as well as Schneeweis, Kazemi, and Spurgin (2008). 7. The period shorter than the whole data period from December 1991 to June 2011, due to the correlation calculation based on three-year periods. 8. In table 4.3, the statistical significance of differences in the individual correla- tions of subperiods has been verified using z statistics with a normal distribution (7) (Brannic 2011): 1 1 r 1 1 r ln()1 ln()2 2 1 r 2 1 r z  1 2 (7) 1 1

N12 3 N 3

where r1 and r2 represent the respective estimations of correlation coefficients and N1 and N2 represent sample sizes. 9. The period shorter than the whole data period from December 1991 to June 2011, due to the correlation calculation based on three-year periods.

5 Performance Measurement of Commodity Investments

1. The presented classification is based on the proposals by Lhabitant (2004, 2008b). 2. In existing literature, the term “volatility” is usually meant to refer to a standard deviation of returns. Both terms are used in this book with the same meaning. 3. In the literature, there are two ratios referred to as a modified Sharpe ratio. In order to distinguish between them, we will use the terms “modified Sharpe ratio I” and “modified Sharpe ratio II,” respectively. 4. Treynor (1961, 1962) and Lintner (1965) also developed a similar model at the same time, so all of them are often considered to be the fathers of the CAPM model. 5. The names of these measures are used almost exclusively in their abbreviated forms, although, very occasionally, the references to their authors can be found; Modigliani and Modigliani as well as Graham-Harvey. 214 Notes

6. The first draft of the absolute futures ratio (AFR) has been presented by Zaremba (2011c). 7. The first draft of the relative futures ratio (RFR) has been suggested by Zaremba (2011d). 8. This method is also described in Lee and Lee (2004). 9. An identical procedure is desribed and employed by Zaremba (2014a). 10. The tangency portfolios are characterised in this case by the maximum attain- able Sharpe ratios.

6 Commodity Investments in Financialized Markets—a Study

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Absolute Futures Ratio, 178 Commodity Trading Advisor, 51 active indices, 80 companies related with commodities, 5 adaptive moving averages, 61 conditional Sharpe ratio, 173 adjusted Sharpe ratio, 162 correlations, 122, 143 asset class, 1 countertrend strategies, 66 assets under management - managed CPO, 52. See Commodity Pool futures, 49, 50 Operator Credit Suisse indices, 83, 85, 87, 88 Bache Commodity Index, 41, 42 CTA, 52. See Commodity Trading backfill bias, 90, 97 Advisor Barclay BTOP50 Index, 84, 85, 87, 88 CX Commodity Index, 41, 42 Barclay Group, 83, 85, 87, 88 bear market truncation, 66 Deutsche Bank Liquid Commodity behavioral models, 77 Index, 37, 40, 42 benchmark asset allocation, 4 Diapason Commodity Index, 37, 41, 42 benchmarks for commodity markets, 35 diversification return, 20 benchmarks for managed futures, 78 Dow Jones Credit Suisse Blue Chip biases in databases, 88, 97 Managed Futures Hedge Fund Index, biofuels, 121 83, 85, 87, 88 black box, 55 Dow Jones UBS Commodity Index, 38, breakout strategies, 62, 63 40, 42 Burke ratio, 176 downside risk, 171 business cycle, 132 drawdown ratios, 175 dynamic asset allocation, 23 Calmar ratio, 176 candlestick patterns, 66 Efficient Market Hypothesis, 76 candlesticks, 65 electronic transactions, 107 Capital Asset Pricing Model, 27, 164 emerging economies, 121 capital assets, 2 excess return indices, 36 CAPM. See Capital Asset Pricing Model exponential moving average, 61 CASAM/CISDM, 82, 85, 87, 88 CFTC. See Commodity Futures Trading FCM. See Futures Commission Commission Merchant chaos theory, 78 financial crisis, 126 Chicago Board of Trade, 51 financialization, 99 closed investment bias, 95, 97 financialization - definition, 102 collateral yield, 19, 129 fixed length moving average, 61 Commodities Corporation, 48 fundamental analysis, 55 Commodity Futures Trading Futures Commission Merchant, 51 Commission, 50 commodity indices, 35, 36 GH1, 169 Commodity Pool Operator, 51 GH2, 170 248 Index

Graham and Harvey measures, 169, momentum strategies, 130 170 Mount Lucas Management Index, 39, Grain Futures Administration, 51 41, 42, 84 moving average, 58, 59, 60 Harvard Management Company, 125 moving average signals, 61 head and shoulders, 64 hedging pressure, 32 National Futures Association, 50 herd behavior, 77 Newedge CTA Index, 83, 85, 87, 88 high-low moving average, 62 new-manager bias, 89, 97 history of futures markets, 9 NFA. See National Futures Association noise traders, 76, 77 IB, Introducing Broker noisy rational expectations, 76 Index, 37, 41, 42 non-exchange traded commodities, 120 index funds, 112 non-trend following strategies, 64 inflation, 127 normal backwardation, 30 information ratio, 161 normal contango, 34 International Monetary Market, 53 Introducing Broker, 51 omega ratio, 171 option models, 34 Jensen’s alpha, 165 oscillators, 66 JP Morgan Commodity Curve Index, 38, 41, 42 passive indices, 82 pattern recognition, 64 leverage, 73, 74 performance measurement, 159 liquidity preference, 33 physical commodities, 5 look-back bias, 89, 97 portfolio diversification, 72 Lyxor CTA Long Term Index, 84, 85, price bubbles, 115 87, 88 prospect ratio, 172 Lyxor CTA Short Term Index, 84, 85, 87, 88 rational expectations hypothesis, 29 rebalancing bias, 94, 97 M2, 168 rebalancing return, 20 M3, 168 regulations - managed futures, 50 managed futures, 47 Relative Futures Ratio, 178 managed futures indices Relative Strength Index, 67 managed futures performance, 98 relative value strategies, 68 managed futures strategies, 57 Reuters/Jefferies-CRB Index, 36, 38, managed futures styles, 55 40, 42 market microstructure, 78 Reward to Value at Risk, 173 market participants, 108 Rind Index, 120 Masters’ hypothesis, 102 risk management, 73 maturity of contracts, 23 risk premium, 43, 128 maximum drawdown, 175 Rogers International Commodity Index, mean-variance spanning test, 181 37, 38, 40, 42 Merrill Lynch Commodity Index eXtra, roll return, 16, 129 41, 42 RSI. See Relative Strength Index minimum acceptable return, 171 RVaR. See Reward to Value at Risk modified Sharpe ratio, 162 modified Sharpe ratio II, 174 S&P Managed Futures Index, 83, 85, modified VaR, 183 87, 88 Index 249

SEC. See Security and Exchange tactical asset allocation, 4 Commission technical analysis, 55, 75 sectoral indices, 82 technical analysis - effectiveness, 136, 146 Security and Exchange term-structure strategies, 131 Commission, 53 theories of storage, 27 Security Market Line, 165 total return indices, 36 segmented market hypothesis, 33 trading system, 56 selection bias, 88, 97 trading system calibration, 69 semi-strong efficiency, 76 trading system validation, 69 Sharpe ratio, 159 trend following, 57 Sharpe-Israelsen ratio, 163 Treynor ratio, 167 Sortino ratio, 171 triple moving average, 62 sources of profits in commodities, 14 turnover in commodity markets, 103 spot indices, 36 spot return, 15, 129 UBS Bloomberg Constant Maturity Standard & Poors Goldman Sachs Commodity Index, 38, 41, 42 Commodity Index, 36, 38, 40, 42 Sterling ratio, 176 value at risk, 73, 172 store of value, 2 variable moving average, 61 strategic asset allocation, 4 VIX Index, 127 stress test, 73 volatility in commodity markets, 113 strong efficiency, 76 volatility underestimation, 94, 97 structural changes, 103 subindices, 81 weak efficiency, 76 survivorship bias, 91, 97 weighting methods, 21, 81