scholarly journals Forecasting Transaction Rates: The Autoregressive Conditional Duration Model

10.3386/w4966 ◽  
1994 ◽  
Author(s):  
Robert Engle ◽  
Jeffrey Russell
Keyword(s):  
Duration Model ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

Time Varying Trade Intensities and the Deutsche Telekom IPO / Zeitvariable Handelsintensitaten und die Deutsche Telekom IPO

2000 ◽  
Vol 220 (6) ◽  
Author(s):  
Reinhard Hujer ◽  
Joachim Grammig ◽  
Stefan Kokot
Keyword(s):  
Initial Public Offering ◽  
Structural Breaks ◽  
Duration Model ◽  
Time Varying ◽  
Threshold Autoregressive ◽  
Public Offering ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

SummaryWe apply the Threshold Autoregressive Conditional Duration Model (TACD) as proposed by Zhang, Russell, and Tsay (1999) to model the after market trading duration process associated with the initial public offering of the Deutsche Telekom AG share in November of 1996. Special emphasis is devoted to the empirical specification of intra-day seasonality and to the detection of non-stationarity and structural breaks in the trading process.

scholarly journals Bitcoin Price Risk—A Durations Perspective

2020 ◽  
Vol 13 (7) ◽  
pp. 157
Author(s):  
Thomas Dimpfl ◽  
Stefania Odelli
Keyword(s):  
Price Change ◽  
Risk Profile ◽  
Second Step ◽  
Price Risk ◽  
Time Interval ◽  
Duration Model ◽  
Thin Market ◽  
Autoregressive Conditional Duration ◽  
Autoregressive Conditional Duration Model

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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