autoregressive conditional duration model
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2020 ◽  
Vol 13 (7) ◽  
pp. 157
Author(s):  
Thomas Dimpfl ◽  
Stefania Odelli

An important aspect of liquidity is price risk, i.e., the risk that a small transaction leads to a large price change. This usually happens in a thin market, when trading opportunities are scarce and the time between subsequent trades is long. We rely on an autoregressive conditional duration model to extract the probability of a substantial price event in a particular time interval and, thus, an intraday risk profile. Our findings show that price risk is highest at times when European and U.S. investors do not trade. In a second step, we relate daily aggregates to characteristics of the Bitcoin blockchain and investigate whether investors account for features like confirmation time or fees when timing their orders.


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