# Does “Paper Oil” Matter? Energy Markets’ Financialization and Co-Movements with Equity Markets

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## Abstract

**:**

## 1. Introduction

## 2. Three Decades of Energy–Equity Return Correlation

#### 2.1. Return Data

#### 2.2. Dynamic Conditional Correlations

## 3. The Financialization of Energy Futures Markets, 2000–2010

#### 3.1. Trader Position Data

#### 3.1.1. Public Information

#### 3.1.2. Non-Public Information

#### 3.2. Overall Speculative Intensity

#### 3.3. Breaking down the Overall Increase in Speculation, 2000–2010

#### 3.3.1. Commodity Index Trading (CIT)

#### 3.3.2. Hedge Fund Activity

#### 3.3.3. Cross-Market Trading

- Number of Cross-Market Traders

- 2.
- Open Interest Share of Cross-Market Traders

_{i,t}, CMSA _AS

_{i,t}and CMSA _ALL

_{i,t}, respectively, the shares of the open interest (average of long and short positions) in the i

^{th}commodity held by cross-trading hedge funds (MMT), energy swap dealers (AS), and all energy-futures traders (ALL) (i = 1, 2, 3). We then use the annual GSCI index weights to calculate the weighted-average market share of several trader types (xxx = MMT, AS or ALL) across the three energy futures markets in our sample:

## 4. Linking Fundamentals, Speculation, and Commodity–Equity Co–Movements

#### 4.1. Macroeconomic, Physical-Market and Financial-Market Conditions

#### 4.1.1. Macroeconomic Fundamentals

#### 4.1.2. Physical-Market Fundamentals

#### 4.1.3. Financial Stress and Lehman Crisis

#### 4.2. Methodology

#### 4.3. Regression Results

#### 4.3.1. Real Sector and Financial Stress Variables (Table 6)

#### 4.3.2. Speculation, including Hedge Funds Activity (Table 7)

#### 4.3.3. Interaction between Hedge Funds and Financial Stress

#### 4.3.4. Cross-Market Trading

#### 4.4. Robustness

#### 4.4.1. Commodity Indexing Activity

#### 4.4.2. The 2008–2010 Financial Crisis

#### 4.4.3. Hedge Fund Activities in Near-Dated Commodity Futures vs. across the Maturity Curve

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Weekly correlations between returns on passive energy and equity investments, 1991–2020. Notes: Figure 1 plots the dynamic conditional correlations (DCC and ADCC) between the weekly unlevered rates of return (precisely, the changes in log prices) on the S&P GSCI–Energy total return index (“GSENTR”) and the S&P 500 equity index (“SP”). We estimate time-varying correlations using return data from 3 January 1991 to 28 July 2020. We plot DCC estimated by log-likelihood for the mean-reverting model [24]; in dark blue) as well as asymmetric ADCC [25]; in red).

**Figure 2.**Financialization of energy futures markets (2000–2010). (

**A**) Panel A: speculative intensity and commodity swap activity (incl. Commodity Index Trading). (

**B**) Panel B: hedge fund share of the energy futures open interest (incl. Cross-Market Traders). Notes: Figure 2A plots the weighted-average speculative pressure index (“Working’s T”) in three U.S. energy paper markets linked to the GSCI-Energy index across all maturities (red,

**WSIA**) or in near-dated futures (orange,

**WSIS**) from 26 June 2000 through 26 February 2010. Indices are rescaled so that a value of 0 means speculative positions exactly offset the net hedging demand from market participants holding underlying exposures to energy price risk. A value greater than 0 is the fraction of speculative activity in excess of this net hedging demand. The dark green line shows the aggregate share of the short-term open interest held by commodity (including index traders) in the same energy markets (

**WMSS_AS**). The lighter gray line shows the share of the overall energy futures open interest held by commodity swap dealers (

**WMSA_AS**). Figure 2B plots the proportion of the short-term (SS, in red) or overall (SA, in blue) open interest made up by hedge funds (MMT), including those active in both energy and equity markets (

**WCMSA**, in purple).

Working’s “T” | ||
---|---|---|

All Contract Maturities (WSIA) | Short-Term Contracts (WSIS) | |

Mean | 1.2376 | 1.2611 |

Median | 1.2581 | 1.2548 |

Maximum | 1.4639 | 1.5768 |

Minimum | 1.0547 | 1.0453 |

Std. Dev. | 0.1154 | 0.1384 |

Skewness | 0.0656 | 0.1272 |

Kurtosis | 1.6870 | 1.7494 |

Jarque-Bera | 36.6388 *** | 34.2706 *** |

Sum | 624.9958 | 636.8481 |

Sum Sq. Dev. | 6.7067 | 9.6484 |

Observations | 505 | 505 |

ADF (Level) | −1.4100 | −1.5807 |

ADF (1st Diff) | −24.6943 *** | −16.8223 *** |

**Note:**Table 1 provides summary statistics of the intensity of speculative activity in the NYMEX’s WTI light sweet crude oil, New York Harbor No.2 heating oil, and Henry Hub natural gas futures markets. We compute “excess” commodity speculation for the three nearest-term futures (

**WSIS**) and all contract maturities (

**WSIA**) as the weighted–average “excess speculation” index (Working’s “T” index) for the three U.S. energy futures markets in the GSCI-Energy index (Sources: CFTC, S&P and authors’ calculations); annual weights equal the average of the daily GSCI weights that year (Source: Standard & Poor). For the augmented Dickey–Fuller (ADF) tests, *** indicates the rejection of non-stationarity at the 1 percent level of statistical significance; critical values are from [28]. The optimal lag length K is based on the Akaike Information Criterion (AIC). Sample period: 26 June 2000 to 26 February 2010.

Panel A: Weighted-Average Market Shares in Short-Term Energy Futures | |||||
---|---|---|---|---|---|

Hedge Funds (WMSS_MMT) | Swap Dealers (WMSS_AS) | Traditional Commercials (WMSS_TCOM) | |||

Mean | 0.2042 | 0.2086 | 0.3669 | ||

Median | 0.2244 | 0.2108 | 0.3435 | ||

Maximum | 0.3631 | 0.3008 | 0.627804 | ||

Minimum | 0.0460 | 0.1182 | 0.1688 | ||

Std. Dev. | 0.0810 | 0.0350 | 0.1144 | ||

Skewness | −0.3245 | −0.0850 | 0.4797 | ||

Kurtosis | 1.8469 | 2.6898 | 2.1504 | ||

Jarque-Bera | 36.8430 | 2.6331 | 34.5549 | ||

Sum | 103.14 | 105.32 | 185.28 | ||

Sum Sq. Dev. | 3.3102 | 0.6158 | 6.5960 | ||

Observations | 505 | 505 | 505 | ||

ADF (Level) | −1.7212 | −2.7133 * | −1.4170 | ||

ADF (1st Diff) | −16.5738 *** | −11.5193 *** | −18.6483 *** | ||

Panel B: Total Open Interest Sharesin All Energy Futures | |||||

Weighted-average Market Shares across All energy futures Maturities (WMSA) | Weighted-average OI share of Cross-Market tradersacross All energy futures maturities (WCMSA) | ||||

Hedge Funds (WMSA_MMT) | SwapDealers(WMSA _AS) | Traditional Commercials(WMSA _TCOM) | Hedge Funds (WCMSA_MMT) | Swap Dealers (WCMSA_AS) | |

Mean | 0.1736 | 0.2855 | 0.3412 | 0.1015 | 0.2228 |

Median | 0.2025 | 0.2928 | 0.3161 | 0.1072 | 0.2239 |

Maximum | 0.3274 | 0.3715 | 0.6133 | 0.2027 | 0.2934 |

Minimum | 0.0309 | 0.1826 | 0.1571 | 0.0129 | 0.1548 |

Std. Dev. | 0.0900 | 0.0393 | 0.1172 | 0.0530 | 0.0237 |

Skewness | −0.0883 | −0.4282 | 0.4745 | −0.0952 | −0.3348 |

Kurtosis | 1.6142 | 2.6932 | 2.2679 | 1.6976 | 3.3322 |

Jarque-Bera | 41.0650 | 17.4126 | 30.2299 | 36.4574 | 11.7582 |

Sum | 87.66 | 144.18 | 172.33 | 51.23 | 112.50 |

Sum Sq. Dev. | 4.0814 | 0.7784 | 6.9248 | 1.4181 | 0.2837 |

Observations | 505 | 505 | 505 | 505 | 505 |

ADF (Level) | −1.3245 | −1.4956 | −0.9740 | −1.4659 | −1.4956 |

ADF (1st Diff.) | −22.2460 *** | −10.5792 *** | −21.9711 *** | −20.9203 *** | −10.5792 *** |

**Notes:**In

**Panel A**,

**WMSS_MMT**,

**WMSS_AS**, and

**WMSS_TCOM**stand, respectively, for the weighted-average shares of the short-term open interest in the three nearest-dated futures with non-trivial open interest (for the three U.S. energy commodities in the GSCI-Energy index) of the following types of traders: hedge funds (MMT, “managed money traders” only), commodity swap dealers (AS, including CIT—commodity index traders), and traditional commercial traders (TCOM, excluding commodity swap dealers). In

**Panel B**,

**WMSA_MMT**,

**WMSA_AS**, and

**WMSA_TCOM**stand, respectively, for the MMT, AS, and TCOM weighted-average shares of the open interest across all futures contract maturities, for the same three U.S. energy futures markets (Source: CFTC Large Trader Reporting System (LTRS) and authors’ computations). We set the weights each year equal to the average of the GSCI weights for those three commodities that year and rescale the figures to account for GSCI-Energy markets for which no LTRS position data are available (Source: S&P). For MMT and AS traders, the

**WCMSA**variables in the rightmost two columns of Panel B measure the proportion of energy futures traders who also hold positions in the S&P 500 e-Mini equity futures (“cross-market traders”

**CM**). For the augmented Dickey–Fuller (ADF) tests in both panels of the table, stars (* and ***) indicate the rejection of non-stationarity at standard levels of statistical significance (10% and 1%, respectively); critical values are from [28]. The optimal lag length is based on the Akaike Information Criterion (AIC). Sample period for all statistics: 26 June 2000 to 26 February 2010.

Commodity Futures Market Classifications | Equity Futures Classification | |||||||
---|---|---|---|---|---|---|---|---|

Commodity | All Cross-Market Traders | Commodity Swap Dealers | Hedge Funds | Hedge Funds | ||||

Count | Percent of All Traders | Count | Percent of All Cross-Traders | Count | Percent of All Cross-Traders | Count | Percent of All Cross-Traders | |

Crude Oil | 1108 | 28.0% | 63 | 5.7% | 363 | 32.8% | 274 | 24.7% |

Heating Oil | 335 | 8.5% | 26 | 7.8% | 170 | 50.8% | 138 | 41.2% |

Natural Gas | 743 | 18.8% | 49 | 6.6% | 300 | 40.4% | 235 | 31.6% |

**Notes**: For the three main energy futures markets for which trader-level position data are available for the entire 2000–2010 period, Table 3 provides information on the number and relative importance of the subset of large commodity futures traders who also held, at some point in the sample period (1 July 2000 through 26 February 2010), positions in the S&P500 e-Mini equity futures contract. Source: CFTC and authors’ computations.

Return Correlations | Macroeconomic Fundamentals | Financial Market Conditions | |||
---|---|---|---|---|---|

DCC S&P500 -GSENTR | REA Index | SPARE (mb/day) | TED (%) | UMD | |

Mean | 0.0486 | 0.1281 | 0.9116 | 0.4877 | 0.0030 |

Median | 0.0429 | 0.1561 | 0.4357 | 0.2965 | 0.0900 |

Maximum | 0.5022 | 0.5530 | 4.9900 | 4.3306 | 4.5500 |

Minimum | −0.3627 | −0.5250 | −0.2608 | 0.0275 | −6.5600 |

Std. Dev. | 0.2192 | 0.2632 | 1.1531 | 0.5180 | 1.1271 |

Skewness | 0.1919 | −0.4634 | 1.6975 | 2.9511 | −0.7008 |

Kurtosis | 2.0384 | 2.32942 | 5.1367 | 14.6372 | 8.1539 |

Jarque-Bera | 22.5550 *** | 27.53235 *** | 338.5880 *** | 3582.557 *** | 600.2571 *** |

Sum | 24.56 | 64.69 | 460.35 | 246.31 | 1.52 |

Sum Sq. Dev. | 24.2069 | 34.9119 | 670.1042 | 135.2274 | 640.2358 |

Observations | 505 | 505 | 505 | 505 | 505 |

ADF (Level) | −1.9230 | −1.9284 | −1.9592 | −2.8809 ** | −24.2610 *** |

ADF (1st Diff.) | −23.0292 *** | −6.6142 *** | −5.7425 *** | −12.8887 *** | −12.6374 *** |

**Note:**We estimate dynamic conditional correlation (

**DCC**) using the Tuesday-to-Tuesday unlevered rates of return (precisely, changes in log prices) on the S&P GSCI Energy Total Return index (GSENTR) and the S&P 500 equity index (SP). We use a log-likelihood for mean-reverting model 24.

**REA**is a measure of worldwide economic activity [31].

**ADS**is a measure of U.S. economic activity (Aruoba, Diebold and Scotti, 2009).

**SPARE**measures the daily crude oil spare production capacity outside of Saudi Arabia (Source: International Energy Agency).

**TED**is the 90-day annualized Ted spread (Source: Bloomberg).

**UMD**is the [32] momentum factor for U.S. equities. For the augmented Dickey–Fuller (ADF) tests, ** and *** indicate the rejection of non-stationarity at the 5 and 1 percent levels of statistical significance, respectively; critical values are from [28]. The optimal lag length K is based on the Akaike Information Criterion (AIC). Sample period for all statistics: 26 June 2000 to 26 February 2010.

DCC | REA | TED | SPARE | UMD | WSIA | WSIS | WMSA_AS | WMSA_MMT | WMSA_TCOM | WCMSA_MMT | WCMSA_AS | WMSS_AS | WMSS_MMT | WMSS_TCOM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

DCC | 1 | ||||||||||||||

REA | (0.42) | 1 | |||||||||||||

TED | 0.06 | 0.19 | 1 | ||||||||||||

SPARE | 0.45 | (0.71) | (0.29) | 1 | |||||||||||

UMD | (0.00) | (0.04) | (0.10) | 0.08 | 1 | ||||||||||

WSIA | 0.20 | 0.54 | 0.55 | (0.47) | (0.08) | 1 | |||||||||

WSIS | 0.19 | 0.57 | 0.55 | (0.51) | (0.09) | 0.97 | 1 | ||||||||

WMSA_AS | (0.07) | 0.54 | 0.36 | (0.50) | (0.11) | 0.69 | 0.68 | 1 | |||||||

WMSA_MMT | 0.13 | 0.60 | 0.53 | (0.54) | (0.07) | 0.98 | 0.95 | 0.68 | 1 | ||||||

WMSA_TCOM | (0.09) | (0.58) | (0.46) | 0.50 | 0.09 | (0.94) | (0.91) | (0.86) | (0.93) | 1 | |||||

WCMSA_MMT | (0.01) | 0.64 | 0.20 | (0.59) | (0.03) | 0.81 | 0.80 | 0.52 | 0.88 | (0.79) | 1 | ||||

WCMSA_AS | (0.09) | 0.35 | 0.37 | (0.42) | (0.11) | 0.52 | 0.51 | 0.93 | 0.50 | (0.72) | 0.34 | 1 | |||

WMSS_AS | (0.02) | 0.50 | 0.37 | (0.42) | (0.08) | 0.71 | 0.69 | 0.85 | 0.70 | (0.79) | 0.59 | 0.77 | 1 | ||

WMSS_MMT | 0.08 | 0.63 | 0.47 | (0.53) | (0.07) | 0.93 | 0.95 | 0.69 | 0.97 | (0.92) | 0.88 | 0.51 | 0.68 | 1 | |

WMSS_TCOM | (0.05) | (0.63) | (0.45) | 0.54 | 0.09 | (0.94) | (0.94) | (0.82) | (0.94) | 0.98 | (0.83) | (0.67) | (0.81) | (0.96) | 1 |

Panel A: Long-Run DCC Determinants. | ||||
---|---|---|---|---|

Model 1 | Model 2 | |||

Constant | −0.0244 | −0.1775 | ** | |

(0.0682) | (0.0788) | |||

REA | −0.3997 | ** | ||

(0.1780) | ||||

SPARE | 0.0929 | ** | ||

(0.0373) | ||||

UMD | 0.1159 | * | 0.0998 | * |

(0.0663) | (0.0599) | |||

TED | 0.4734 | 0.2142 | ** | |

(0.1380) | (0.1079) | |||

DUM | 0.4734 | *** | 0.4630 | *** |

(0.1380) | (0.1252) | |||

F-Bounds Test | 3.7832 | ** | 4.3382 | ** |

Panel B: ECM (Error Correction Model). | ||||

Model 3 | Model 4 | |||

ECM(-1) | −0.0384 | *** | −0.0420 | *** |

(0.0080) | (0.0082) | |||

ΔREA | −0.0922 | |||

(0.0853) | ||||

ΔSPARE | 0.0008 | |||

(0.0173) | ||||

ΔUMD | 0.0012 | 0.0011 | ||

(0.0011) | (0.0011) | |||

ΔTED | 0.0154 | (0.0142 | ||

(0.0111) | (0.0110) |

**Notes:**The dependent variable is the dynamic conditional correlation (DCC) between passive energy and equity investments. The dependent and explanatory variables are described in Table 4, except for DUM—a time dummy variable equal to 0 prior to 1 September 2008 and 1 afterwards (“Lehman dummy”). Long- and short-run estimates from ARDL(1,1) are based on the two-step approach of [43,44]. Standard errors are in parentheses; statistical significance at the 1, 5, and 10% levels is denoted with ***, **, and * respectively. The critical values for F statistics in the bounds test are taken from [44]. The sample period is = 1 July 2000 to 26 February 2010.

Panel A: Long-Run DCC Determinants. | ||||||||
---|---|---|---|---|---|---|---|---|

Model 3 | Model 4 | Model 5 | Model 6 | |||||

Constant | −3.0125 | ** | −3.3465 | ** | −3.6263 | ** | −3.4924 | ** |

(1.2545) | (1.4069) | (1.5914) | (1.7288) | |||||

REA | −0.3200 | −0.3249 | * | |||||

(0.2144) | (0.1972) | |||||||

SPARE | 0.1157 | *** | 0.1030 | *** | ||||

(0.0362) | (0.0341) | |||||||

UMD | 0.0678 | * | 0.0666 | * | 0.0865 | * | 0.0876 | * |

(0.0395) | (0.0370) | (0.0503) | (0.0464) | |||||

TED | 1.5394 | *** | 3.8754 | ** | 1.1266 | ** | 2.9083 | * |

(0.5015) | (1.5357) | (0.5547) | (1.6944) | |||||

WMSS_MMT | 6.0958 | *** | 6.9791 | *** | ||||

(1.8312) | (2.3462) | |||||||

WMSS_AS | 1.1985 | −1.0915 | 1.9657 | −1.1314 | ||||

(1.8793) | (1.4069) | (2.3663) | (1.7507) | |||||

WMSS_TCOM | 3.3910 | ** | 1.0665 | 4.6530 | ** | 1.4358 | ||

(1.5979) | (1.0069) | (2.0145) | (1.2268) | |||||

WSIA | 2.3843 | *** | 2.5500 | *** | ||||

(0.7947) | (0.9788) | |||||||

INT_TED_MMT | −4.8653 | *** | −3.5930 | * | ||||

(1.6538) | (1.8499) | |||||||

INT_TED_WSIA | −2.7309 | ** | −2.0723 | * | ||||

(1.0915) | (1.2123) | |||||||

DUM | 0.4817 | *** | 0.3985 | *** | 0.5589 | *** | 0.4347 | *** |

(0.1056) | (0.0942) | (0.1453) | (0.1265) | |||||

F-Bounds Test | 4.6189 | *** | 3.9630 | *** | 3.6087 | ** | 3.1871 | ** |

Panel B: ECM (Error Correction Model). | ||||||||

Model 3 | Model 4 | Model 5 | Model 6 | |||||

ECM(-1) | −0.0600 | *** | −0.0663 | *** | −0.0483 | *** | −0.0543 | *** |

(0.0087) | (0.0104) | (0.0080) | (0.0095) | |||||

ΔREA | −0.1062 | −0.1461 | * | |||||

(0.0822) | (0.0829) | |||||||

ΔSPARE | −0.0164 | −0.0094 | ||||||

(0.0171) | (0.0173) | |||||||

ΔUMD | 0.0010 | 0.0014 | 0.0010 | 0.0015 | ||||

(0.0010) | (0.0011) | (0.0011) | (0.0011) | |||||

ΔTED | 0.1738 | *** | −0.0061 | 0.1487 | *** | −0.1013 | ||

(0.1738) | (0.1551) | (0.0331) | (0.1538) | |||||

ΔWMSS_MMT | 0.5412 | *** | 0.5082 | *** | ||||

(0.1646) | (0.1650) | |||||||

ΔWMSS_AS | −0.1252 | −0.2962 | ** | −0.1123 | −0.2807 | ** | ||

(0.1267) | (0.1376) | (0.1275) | (0.1380) | |||||

ΔWMSS_TCOM | 0.0759 | −0.0612 | 0.0884 | −0.0500 | ||||

(0.1095) | (0.1029) | (0.1104) | (0.1032) | |||||

ΔWSIA | −0.0303 | −0.0351 | ||||||

(0.1858) | (0.1864) | |||||||

ΔINT_TED_MMT | −0.5846 | *** | −0.4970 | *** | ||||

(0.1205) | (0.1199) | |||||||

ΔINT_TED_WSIA | 0.0153 | 0.0842 | ||||||

(0.1127) | (0.1119) |

**Notes:**The dependent variable is the DCC between the weekly unlevered rates of return on passive equity and energy investments. All variables are described in Table 1, Table 2, Table 3 and Table 4, except for DUM (a “Lehman” time dummy that takes the value 0 prior to 1 September 2008 and 1 afterwards) and INT_TED_xxx (interaction terms of the TED spread with position variables). Long-run (Panel A) and short-run (Panel B) estimates are based on the ARDL(p,q) estimation approach of [43,44]. The Schwarz information criterion suggests optimal lag lengths p = 1 and q = 1. Standard errors are in parentheses; statistical significances at the 1, 5, and 10% levels is denoted with ***, **, and *. The critical values for F statistics in the bounds test are taken from [44]. Sample period: 26 June 2000 through 26 February 2010.

**Table 8.**Cross-market trading as a long-run contributor to the GSCI-S&P500 dynamic conditional correlation.

Panel A: Long-Run DCC Determinants. | ||||||||
---|---|---|---|---|---|---|---|---|

Model 7 | Model 8 | Model 9 | Model 10 | |||||

Constant | 0.3106 | −1.0356 | 0.9441 | ** | −0.5501 | |||

(0.4612) | (0.9034) | (0.4128) | (0.8844) | |||||

REA | −0.3454 | * | −0.3595 | ** | ||||

(0.1861) | (0.1707) | |||||||

SPARE | 0.1253 | *** | 0.0976 | *** | ||||

(0.0430) | (0.0362) | |||||||

UMD | 0.0674 | 0.0721 | * | 0.0715 | 0.0825 | * | ||

(0.0436) | (0.0395) | (0.0486) | (0.0441) | |||||

TED | 1.1271 | ** | 3.6601 | ** | 0.5261 | 2.4862 | ||

(0.4538) | (1.5669) | (0.4447) | (1.5252) | |||||

WCMSA_MMT | 4.4161 | *** | 2.3962 | * | ||||

(1.6648) | (1.4561) | |||||||

WCMSA_AS | −4.3646 | ** | −3.2476 | * | −5.5186 | *** | −4.2827 | ** |

(1.7799) | (1.7119) | (1.9298) | (1.7406) | |||||

WSIA | 1.2165 | ** | 1.1671 | * | ||||

(0.5526) | (0.6097) | |||||||

INT_TED_CMMTA | −7.8848 | ** | −3.1154 | |||||

(3.5308) | (3.4797) | |||||||

INT_TED_WSIA | −2.5382 | ** | −1.7333 | |||||

(1.1224) | (1.0980) | |||||||

DUM | 0.3998 | *** | 0.431396 | *** | 0.5283 | *** | 0.4825 | *** |

(0.1224) | (0.1017) | (0.1242) | (0.1268) | |||||

F-Bounds Test | 4.5784 | *** | 4.3372 | *** | 3.4774 | ** | 3.7098 | ** |

Panel B: ECM (Error Correction Model). | ||||||||

Model 7 | Model 8 | Model 9 | Model 10 | |||||

ECM(-1) | −0.0557 | *** | −0.0627 | *** | −0.0510 | *** | −0.0572 | *** |

(0.0086) | (0.0100) | (0.0090) | (0.0098) | |||||

ΔREA | −0.0658 | −0.1361 | ||||||

(0.0839) | (0.0829) | |||||||

ΔSPARE | −0.0131 | −0.0108 | ||||||

(0.0171) | (0.0173) | |||||||

ΔUMD | 0.0010 | 0.0014 | 0.0008 | 0.0015 | ||||

(0.0010) | (0.0011) | (0.0010) | (0.0011) | |||||

ΔTED | 0.1591 | *** | −0.0130 | 0.1384 | *** | -0.1005 | ||

(0.0341) | (0.1535) | (0.0343) | (0.1522) | |||||

ΔWCMSA_MMT | 0.9529 | *** | 0.8632 | *** | ||||

(0.3072) | (0.3086) | |||||||

ΔWCMSA_AS | −0.4094 | * | −0.5181 | ** | −0.4273 | * | −0.5354 | ** |

(0.2223 | (0.2295) | (0.2239) | (0.2298) | |||||

ΔWSIA | −0.0025 | −0.0120 | ||||||

(0.1620) | (0.1621) | |||||||

ΔINT_TED_CMMTA | −1.2468 | *** | −1.0851 | *** | ||||

(0.2930) | (0.2950) | |||||||

ΔINT_TED_WSIA | 0.0220 | 0.0852 | ||||||

(0.1116) | (0.1107) |

**Notes:**The dependent variable is the DCC between the weekly unlevered rates of return on passive equity and energy investments. DUM (a “Lehman” time dummy that takes the value 0 prior to 1 September 2008 and 1 afterwards) and INT_TED_xxx (interaction terms of the TED spread with position variables). INT_TED_CMMTA is an interaction terms of the TED spread with the shares of open interest held weekly by cross-market trading hedge funds (MMT). The other variables are described in Table 1, Table 2, Table 4, and Table 7. Long-run (Panel A) and short-run (Panel B) estimates are based on the ARDL(p,q) estimation approach of [43,44]. The Schwarz information criterion suggests optimal lag lengths p = 1 and q = 1 in our case. Standard errors are in parentheses; statistical significance at the 1, 5, and 10% levels is denoted with ***, **, and *. The critical values for F statistics in the bounds test are taken from [44]. The sample period is 26 June 2000 through 26 February 2010.

Variable | Model 3 2000–2008 | Model 3b 2000–2008 |
---|---|---|

Constant | −2.4958 ** | −4.4461 *** |

(1.205) | (1.491) | |

REA | −0.7603 *** | −0.6764 *** |

(0.1639) | (0.1446) | |

UMD | 0.0242 | 0.0184 |

(0.0363) | (0.0313) | |

TED | 1.3782 *** | 1.0994 *** |

(0.3954) | (0.3476) | |

WMSS_AS | 0.7225 | 1.2839 |

(1.817) | (1.597) | |

WMSS_MMT | 6.7724 *** | 6.3014 *** |

(2.000) | (1.710) | |

WMSS_TCOM | 2.5937 * | 3.7444 *** |

(1.408) | (1.375) | |

INT_TED_MMT | −4.3087 *** | −3.5321 *** |

(1.481) | (1.279) | |

WSIA | 1.2395 * | |

(0.6362) | ||

Observations | 436 | 436 |

**Notes:**Model 3 in Table 9 is the same as Model 3 in Table 7, estimated after excluding the post-Lehman period from the sample. Model 3b is similar but WSIA is added as an explanatory variable. In all models, the dependent variable is the time-varying conditional correlation (DCC) between the weekly unlevered rates of return on the S&P 500 (SP) equity index and the S&P GSCI-Energy total return (GSENTR) index. DCC estimated by log-likelihood for the mean reverting model 24. Explanatory variables are described in Table 1, Table 2, Table 3 and Table 4. Long-run estimates in Table 9 are from the two step ARDL(p,q) estimation approach of [43]. The Schwarz information criterion suggests that the optimal lag lengths are p = 1 and q = 1 in our case. Standard errors are in parentheses; statistical significances at the 1, 5, and 10% levels are denoted with ***, **, and *. The sample period is 4 July 2000 to 11 November 2008.

Variable | Model 3c 2000–2008 | Model 3d 2000–2008 |
---|---|---|

Constant | 0.7722 | 2.7811 |

(1.249) | (2.716) | |

REA | −0.5934 *** | −0.5889 *** |

(0.1746) | (0.1763) | |

UMD | 0.0325 | 0.0213 |

(0.0380) | (0.0386) | |

TED | 1.1399 *** | 1.2577 *** |

(0.3968) | (0.4404) | |

WMSA_AS | −3.3830 * | −4.6060 * |

(2.178) | (2.571) | |

WMSA_MMT | 1.8172 | 2.6090 |

(1.519) | (1.836) | |

WMSA_TCOM | −0.7522 | −1.7066 |

(1.412) | (1.832) | |

INT_TED_MMTA | −3.4831 ** | −3.8345 ** |

(1.419) | (1.533) | |

WSIA | −1.2107 | |

(1.492) | ||

Observations | 436 | 436 |

**Notes:**Model 3c in Table 10 is the same as Model 3 in Table 7, with two differences: it is estimated using trader positions across all maturities, after excluding the post-Lehman period from the sample. Model 3c is similar to Model 3c but WSIA is added as an explanatory variable. In all models, the dependent variable is the time-varying conditional correlation (DCC) between the weekly unlevered rates of return on the S&P 500 (SP) equity index and the S&P GSCI-Energy total return (GSENTR) index. DCC estimated by log-likelihood for mean reverting model [24]. Explanatory variables are described in Table 1, Table 2, Table 3 and Table 4. Long-run estimates in Table 9 are from the two step ARDL(p,q) estimation approach of [43]. The Schwarz information criterion suggests that the optimal lag lengths are p = 1 and q = 1 in our case. Standard errors are in parentheses; statistical significance at the 1, 5, and 10% levels is denoted with ***, **, and * respectively. The sample period is 4 July 2000 to 11 November 2008.

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## Share and Cite

**MDPI and ACS Style**

Büyükşahin, B.; Robe, M.A.
Does “Paper Oil” Matter? Energy Markets’ Financialization and Co-Movements with Equity Markets. *Commodities* **2024**, *3*, 197-224.
https://doi.org/10.3390/commodities3020013

**AMA Style**

Büyükşahin B, Robe MA.
Does “Paper Oil” Matter? Energy Markets’ Financialization and Co-Movements with Equity Markets. *Commodities*. 2024; 3(2):197-224.
https://doi.org/10.3390/commodities3020013

**Chicago/Turabian Style**

Büyükşahin, Bahattin, and Michel A. Robe.
2024. "Does “Paper Oil” Matter? Energy Markets’ Financialization and Co-Movements with Equity Markets" *Commodities* 3, no. 2: 197-224.
https://doi.org/10.3390/commodities3020013