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|Title||Bitcoin Inelasticity Hypothesis|
|Institution||University of Zurich|
|Faculty||Faculty of Business, Economics and Informatics|
|Abstract Text||The thesis uses bitcoin data to estimate parameters, with which it is then tried to assess whether the model proposed in Gabaix and Koijen (2021) can explain the mean and standard deviation of bitcoin returns. The model uses the price elasticity of demand to generate observed prices and is stated in perturbations around a baseline. The results suggest that the model does not account for the full picture of return patterns observed. The moments resulting from the simulation are far larger in absolute terms than the observed moments and the estimation results are hardly statistically signi cant. Albeit, a parameter of the model, the speed of mean reversion of flows, has not been estimated, a back-of-the-envelope calculation implies a negative and very large value to counter the effects of the estimation results. A sensible estimation of this parameter is beyond the scope of this thesis. Bitcoin is well suited for price elasticity estimations because it is only marginally influenced by supply shocks i.e., all the shocks can be ascribed to the demand side. Moreover, the full record of transactions is in theory available to the public. A wide range of studies point to the effects trading has on prices, yet only recently has literature emerged arguing for inelastic financial markets. The model presented in Gabaix and Koijen (2021) uses flows to funds to drive asset prices away from their fundamental values. This mechanism influences the stochastic discount rate in the model. For the estimations, two datasets are used. One dubbed `daily data' is provided by Stütz et al. (2020).1 This dataset consists of bitcoin transaction data aggregated on a daily and an entity level, transactions of bitcoin holders with only one address are omitted. The second called `block-level data' has been downloaded through the GraphSense API (Haslhofer et al., 2021). This dataset has been aggregated on a block-level and contains all transactions. Both datasets entail information about on-chain transactions. With this information, the estimations are performed on the transformed data. The data used is stated in deviations from its rolling averages. The rolling averages serve as a baseline. The results of the estimations suggest a positively inelastic demand and contrarian investing agents. The simulations suggest that the model overpredicts the volatility in bitcoin markets and overstates the premium paid by investors, in absolute terms.|