This paper aims to enhance the understanding of which factors affect the price development of Bitcoin in order for investors to make sound investment decisions. Previous literature has covered only a small extent of the highly volatile period during the last months of 2017 and the beginning of 2018. To examine the potential price drivers, we use the Autoregressive Distributed Lag and Generalized Autoregressive Conditional Heteroscedasticity approach. Our study identifies the technological factor Hashrate as irrelevant for modeling Bitcoin price dynamics. This irrelevance is due to the underlying code that makes the supply of Bitcoins deterministic, and it stands in contrast to previous literature that has included Hashrate as a crucial independent variable. Moreover, the empirical findings indicate that the price of Bitcoin is affected by returns on the S&P 500 and Google searches, showing consistency with results from previous literature. In contrast to previous literature, we find the CBOE volatility index (VIX), oil, gold, and Bitcoin transaction volume to be insignificant.

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In the Norwegian real estate market, used dwellings are normally sold through an auction process similar to the standard English (open ascending-bid) auction. Using survey results (N = 1,803), we define jump bids and investigate the motivations behind the use of such strategies. We find that most bidders tend to consider intimidation and signalling as the main motivations for applying a jumpbidding strategy, and intimidation strategies applied by competing bidders appear to be an important reason for bidders withdrawing early from an auction. We also use a sample of 1,142 auction journals and find that, on average, auctions containing jump bids achieve 2.8-9.3 percent higher price premiums compared to strictly straightforward-bidding auctions. The premium is higher when the intimidation strategy fails and competing bidders counter with jump bids. Additionally, this paper provides evidence that jump bids are usually placed at the earliest stage of the auction and have a stronger intimidation effect the earlier they are placed, despite having an overall positive effect on the premium. The results are robust to different valuation approaches and omitted variable bias controls. Our findings have important implications for sellers and buyers in auction settings, and for regulators of auction processes.

Following the European Union’s implementation of Energy Performance Certificates (EPCs) for buildings, the capitalization of energy efficiency in transaction prices and rents has been subject to much research. This paper uses different identification strategies for the Norwegian residential sales (N = 750,000) and rental (N = 670,000) markets to highlight the endogeneity and methodological limitations associated with assessing the price effects of energy efficiency and the signaling effect of label adoption. We find that the valuation of energy efficiency is subject to unobserved location and quality bias, that labeling has immediate, short-run, and long-run price effects and that different effects are observed in different submarkets. We provide evidence that sample selection issues related to location and time, with methodological and data limitations, are essential factors that must be considered when assessing the effects of the EPC implementation.

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This paper is the first to investigate the relationship between the energy efficiency of dwellings, measured by the energy performance certificate (EPC), and utility cost inclusion in rental prices. First, we investigate potential drivers behind the decision to include utility costs in rents. We find that labeled dwellings are more likely to include utility costs and that this likelihood is higher among energy-efficient dwellings than among inefficient dwellings. Next, we surprisingly find that utility costs seem to be under-capitalized in energy-inefficient dwellings. These results are confirmed with the counterfactual decomposition approach. Overall, the findings indicate that the EPC labeling policy may be important for both landlord and tenant decision-making and may enhance market efficiency.