# The Role of Canceled Warrants in the LME Market

## Abstract

**:**

## 1. Introduction

**Hypothesis**

**1 (H1).**

## 2. Literature Review

## 3. Research Model and Data

#### 3.1. Research Model

#### 3.2. Data

#### 3.3. Stationarity Check: Unit Root Tests

## 4. Empirical Results

#### 4.1. Regression Results

#### 4.2. VAR Model Specifications and Impulse Response Results

## 5. Summary and Conclusions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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1 | The LME opened in 1877; it has continued strengthening since then, and became recognized as the global benchmark for the metal market. Major producers and consumers use LME contracts for hedging price risks, and for the reference price for spot and future prices. Park and Lim (2018) provided a good discussion of the LME. They found that the LME was an inefficient market due to financialization by institutional investors. |

2 | See details in Metal Bulletin (http://www.metalbulletin.com/Glossary.html). The CWs are the ratio of CWs to total warrants, where total warrants are the on warrants and canceled warrants. |

3 | This information is a valuable investment factor of consumption trends, which can provide a meaningful implication of possible future price directions. |

4 | The LME does not report intra-day data and tick data. It is not possible to compute daily realized volatility measures for LME markets (Figuerola-Ferretti and Gilbert 2008). They used monthly rather realized volatility data. |

5 | Figuerola-Ferretti and Gilbert (2008) concluded that the LME aluminum and copper scedastic processes are both highly persistent. They argued that the strong symmetry of the two processes implied that the processes may be the outcome of common market microstructure factors. |

6 | Koitsiwe and Adachi (2018) reported that commodity assets under management by financial investors dramatically increased in value, from about $13 billion in 2003 to $450 billion in 2011. Irwin and Sanders (2012) reported that a total of $161.2 billion was invested in commodity index investments as of 31 March 2010. A total of 78% of index investments were in the U.S. futures market, which invited major index funds with rising trading volumes. |

**Figure 1.**Plot of impulse response results: tin and nickel. Notes: The solid line is the orthogonalized impulse response, while the grey area is the 95% confidence interval. The responses of spot returns to a CW shock show that they are very short lived within the second period.

**Figure 2.**Plot of impulse response results: aluminum and zinc. Notes: The solid line is the orthogonalized impulse response, while the grey area is the 95% confidence interval. The responses of spot returns to a CW shock show that they are very short lived within the third period. al_cw_D1 and zn_cw_D1 indicate the first differenced variables.

Inventories | CW Ratio | Effect on Metal Price |
---|---|---|

Up | Down | Negative |

Down | Up | Positive |

**Table 2.**Summary statistics. The total observation contains 3284 obs. (3 January 2006–31 December 2018; daily data). The natural logarithm data are applied in the returns calculations. The distributions for all returns and CWs are skewed and fat-tailed, as indicated by the high significance of the Jarque–Bera test for normality.

Variable | Mean | Standard Deviation | Skewness | Maximum | Minimum | Kurtosis | Jarque-Bera | |
---|---|---|---|---|---|---|---|---|

copper | cu_cw | 0.1836 | 0.1564 | 1.0898 | 0.6742 | 0.0013 | 3.3818 | 458.75 (0.00) |

cu_r | 0.0002 | 0.0182 | 0.1461 | 0.1244 | −0.0980 | 6.8328 | 307.12 (0.00) | |

aluminum | al_cw | 0.2194 | 0.1821 | 0.3844 | 0.5996 | 0.0037 | 1.7159 | 7548.3 (0.00) |

al_r | 0.0005 | 0.0148 | −0.0069 | 0.0660 | −0.0752 | 4.7523 | 133.47 (0.00) | |

lead | pb_cw | 0.1827 | 0.1792 | 0.9114 | 0.7752 | 0.0004 | 2.6438 | 364.71 (0.00) |

pb_r | 0.0004 | 0.0221 | −0.0028 | 0.1389 | −0.1236 | 5.8982 | 228.62 (0.00) | |

zinc | zn_cw | 0.1845 | 0.1797 | 1.1336 | 0.7610 | 0.0019 | 3.1896 | 474.37 (0.00) |

zn_r | 0.0003 | 0.0209 | −0.0039 | 0.1046 | −0.1083 | 4.9927 | 154.59 (0.00) | |

tin | sn_cw | 0.1435 | 0.1283 | 1.3391 | 0.6780 | 0.0000 | 4.4941 | 702.47 (0.00) |

sn_r | 0.0005 | 0.0182 | 0.0856 | 0.1663 | −0.1082 | 9.3753 | 443.00 (0.00) | |

nickel | ni_cw | 0.1840 | 0.1504 | 0.5176 | 0.8586 | 0.0029 | 2.4871 | 197.66 (0.00) |

ni_r | 0.0002 | 0.0237 | 0.1358 | 0.1423 | −0.1284 | 5.6834 | 221.95 (0.00) |

**Table 3.**Unit root test results. The sample consists of 3284 observations. Table 3 presents the unit root tests for all the variables (null hypothesis: there is a unit root). The reported numbers represent test statistics. ***, ** and * indicate the rejection of the null hypothesis at the 1%, 5% and 10% level of significance, respectively.

Variables | ADF | PP | Order of Integration | |||
---|---|---|---|---|---|---|

Level | 1st Diff | Level | 1st Diff | |||

copper | cu_cw | −2.560 | −3.155 ** | not determine | ||

cu_r | −55.75 *** | −55.70 *** | I(0) | |||

aluminum | al_cw | −0.993 | −39.74 *** | −0.675 | −39.91 *** | I(1) |

al_r | −53.19 *** | −53.20 *** | I(0) | |||

lead | pb_cw | −2.582 * | −2.729 ** | I(0) | ||

pb_r | −49.23 *** | −49.22 *** | I(0) | |||

zinc | zn_cw | −2.013 | −36.20 *** | −1.602 | −36.01 *** | I(1) |

zn_r | −52.48 *** | −52.56 *** | I(0) | |||

tin | sn_cw | −5.383 *** | −5.380 *** | I(0) | ||

sn_r | −50.18 *** | −50.19 *** | I(0) | |||

nickel | ni_cw | −3.158 ** | −3.271 ** | I(0) | ||

ni_r | −51.66 *** | −51.75 *** | I(0) |

**Table 4.**Parameter estimates of Equation (1). The total observation contained 3284 obs. (January 2006–December 2018; daily data). The natural logarithm data were applied in the returns data. The reported numbers in parentheses represent t-statistics. *** and ** indicate statistical significance at the 1% and 5% levels. All the returns data use the level, and all the CW data employ the level, except aluminum and zinc, reflecting the unit root test results.

Commodity | ${\mathsf{\beta}}_{0}$ | ${\mathsf{\beta}}_{1}$ | ${\mathbf{R}}^{2}$ | F-Statistics | MSE |
---|---|---|---|---|---|

Copper | 0.0001 | 0.0009 | 0.0001 | 0.21 | 0.0182 |

(0.15) | (0.46) | ||||

Aluminum | 0.0002 | 0.1212 | 0.0032 | 8.37 *** | 0.0147 |

(0.91) | (2.89) *** | ||||

Lead | 0.0004 | −0.0002 | 0.0001 | 0.01 | 0.0222 |

(0.84) | (–0.11) | ||||

Zinc | 0.0004 | 0.0829 | 0.0036 | 9.33 *** | 0.0211 |

(–0.45) | (3.05) *** | ||||

Tin | −0.0002 | 0.0052 | 0.0013 | 4.39 ** | 0.0182 |

(–0.52) | (2.10) ** | ||||

Nickel | −0.0007 | 0.0055 | 0.0012 | 3.97 ** | 0.0236 |

(–1.22) | (2.01) ** |

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**MDPI and ACS Style**

Park, J. The Role of Canceled Warrants in the LME Market. *Int. J. Financial Stud.* **2019**, *7*, 10.
https://doi.org/10.3390/ijfs7010010

**AMA Style**

Park J. The Role of Canceled Warrants in the LME Market. *International Journal of Financial Studies*. 2019; 7(1):10.
https://doi.org/10.3390/ijfs7010010

**Chicago/Turabian Style**

Park, Jaehwan. 2019. "The Role of Canceled Warrants in the LME Market" *International Journal of Financial Studies* 7, no. 1: 10.
https://doi.org/10.3390/ijfs7010010