# Price Discovery and Learning during the German 5G Auction

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Description of the Auction, Market, and Hypotheses

#### 2.1. Auction Design

#### 2.2. Auction Participants

#### 2.3. Frequency Bands and the 5G Technology

#### 2.4. Hypotheses: Do Participants Learn during the Auction?

#### 2.4.1. The Noise Hypothesis

#### 2.4.2. The Learning Hypothesis

## 3. The Data

## 4. Price Discovery

#### 4.1. Estimation

#### 4.2. Learning during the Auction

#### 4.3. Robustness Checks and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Notes

1. | We follow the 3GPP (3rd Generation Partnership Project) suggested naming of frequency bands (www.3gpp.org, accessed May 2021). |

2. | Telekom’s EBITDA was EUR 21.8 billion in 2018 (Deutsche Telekom 2019) in contrast to 722 million of 1&1 (1&1 Drillisch 2019). |

3. | |

4. | The amount is calculated based on the total size of Germany of 357,581 km${}^{2}$ out of which 14% or 49,983 km${}^{2}$ are covered land according to the BNetzA definition. |

5. | The fact that we consider here the highest bid only is similar to the setting in Biais et al. (1999) where only executable orders are used to calculate a tentative price. Bids deeper in the book which are not matched are akin to lower bids in the 5G auction and also not considered for a tentative final price. |

6. | The data are split on multiple pages on https://www.bundesnetzagentur.de/_tools/FrequenzXml/Auktion2019_XML/XXX.html (accessed October 2019), where XXX ranges from 001 to 497, covering each round of the auction. |

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**Figure 1.**

**Evolution of the Sum of Bids**. The graph illustrates the development of the sum of submitted bids for the entire auction and for each individual frequency band. Withdrawn high bids still had to be paid, which is why a reduction of an individual band’s sum of bids does not affect the overall revenue of the auction.

**Figure 2.**

**Frequency Blocks per Company**. This graph plots the development of the distribution of the 41 frequency blocks among the four bidding companies over the course of the 497 auction rounds.

**Figure 3.**

**OLS estimate for ${\widehat{\beta}}_{t}$**. The graph shows the OLS estimate for ${\widehat{\beta}}_{t}$ for each point during the auction. The two blue lines indicate the noise hypothesis ($\beta =0$) and the learning hypothesis ($\beta =1$). The dotted lines represent the 95%-confidence interval (based on robust standard errors) and demonstrate that the noise hypothesis cannot be rejected in the early rounds whereas the learning hypothesis cannot be rejected towards the end of the auction. The informativeness of prices increases the fastest between round 100 and round 187 when a high number of bids for both frequency bands was submitted.

**Figure 4.**

**Regression RMSE and R**. The graph shows in Panel (

^{2}across auction rounds**a**) the development of the standard error of the regression residual (RMSE) across the auction rounds. It is measured as the absolute deviation of returns (in percentage points). The blue line indicates the standard error of the dependent variable returns (which are also calculated in percentage terms). In Panel (

**b**), the round-by-round evolution of the model ${R}^{2}$ is depicted.

**Figure 5.**

**Sample Split by Company**. The graph shows the OLS estimate for ${\widehat{\beta}}_{t}$ for each point during the auction for the Vodafone (

**a**), Telekom (

**b**), Telefónica (

**c**), and Drillisch/1&1 (

**d**). The two blue lines indicate the noise hypothesis ($\beta =0$) and the learning hypothesis ($\beta =1$). The dotted lines represent the 95%-confidence interval based on robust standard errors.

**Figure 6.**

**Sample Split by Frequency Band**. The graph shows the OLS estimate for ${\widehat{\beta}}_{t}$ for each point during the auction for the n1 (2 GHz) Band in Panel (

**a**) and the n78 (3.6 GHz) Band in Panel (

**b**). The two blue lines indicate the noise hypothesis ($\beta =0$) and the learning hypothesis ($\beta =1$). The dotted lines represent the 95%-confidence interval based on robust standard errors.

**Figure 7.**

**Estimate for ${\widehat{\beta}}_{t}$ using log returns**. The graph shows the results from estimating $\widehat{\beta}$ using log returns. The two blue lines indicate the noise hypothesis ($\beta =0$) and the learning hypothesis ($\beta =1$). The dotted lines represent the 95%-confidence interval based on robust standard errors.

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**MDPI and ACS Style**

Dimpfl, T.; Reining, A.
Price Discovery and Learning during the German 5G Auction. *J. Risk Financial Manag.* **2021**, *14*, 274.
https://doi.org/10.3390/jrfm14060274

**AMA Style**

Dimpfl T, Reining A.
Price Discovery and Learning during the German 5G Auction. *Journal of Risk and Financial Management*. 2021; 14(6):274.
https://doi.org/10.3390/jrfm14060274

**Chicago/Turabian Style**

Dimpfl, Thomas, and Alexander Reining.
2021. "Price Discovery and Learning during the German 5G Auction" *Journal of Risk and Financial Management* 14, no. 6: 274.
https://doi.org/10.3390/jrfm14060274