Event Uncertainty

2011 ◽  
Keyword(s):  
Event Uncertainty

Computing the Top Event Uncertainty with Binary Decision Diagrams

2004 ◽  
pp. 2036-2041
Author(s):  
Zdenko Šimić ◽  
Reni Banov ◽  
Igor Vukovic ◽  
Vladimir Mikulicic
Keyword(s):  
Binary Decision Diagrams ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Event Uncertainty

The Effects of Temporal and Event Uncertainty in Determining the Waveforms of the Auditory Event Related Potential (ERP)

1976 ◽  
Vol 13 (6) ◽  
pp. 581-590 ◽  
Author(s):  
Gregory McCarthy ◽  
Emanuel Donchin
Keyword(s):  
Event Related Potential ◽  
Auditory Event ◽  
Event Uncertainty

scholarly journals Effects of time and event uncertainty upon sequential information processing

1968 ◽  
Vol 3 (3) ◽  
pp. 177-184 ◽  
Author(s):  
Ira H. Bernstein ◽  
R. Randolph Blake ◽  
Michael H. Hughes
Keyword(s):  
Information Processing ◽  
Event Uncertainty ◽  
Sequential Information

Impact of Weather Event Uncertainty upon an Optimum Ground-Holding Strategy

1993 ◽  
Vol 1 (1) ◽  
pp. 59-84 ◽  
Author(s):  
John W. Andrews
Keyword(s):  
Weather Event ◽  
Event Uncertainty

scholarly journals Estimating a Structural Model of Herd Behavior in Financial Markets

2014 ◽  
Vol 104 (1) ◽  
pp. 224-251 ◽  
Author(s):  
Marco Cipriani ◽  
Antonio Guarino
Keyword(s):  
Financial Markets ◽  
Structural Model ◽  
Expected Value ◽  
Herd Behavior ◽  
Transaction Data ◽  
Financial Transaction ◽  
Information Event ◽  
Event Uncertainty ◽  
Using Data

We develop a new methodology to estimate herd behavior in financial markets. We build a model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate the model using data on a NYSE stock (Ashland Inc.) during 1995. Herding occurs often and is particularly pervasive on some days. On average, the proportion of herd buyers is 2 percent; that of herd sellers is 4 percent. Herding also causes important informational inefficiencies in the market, amounting, on average, to 4 percent of the asset's expected value. (JEL C58, D82, D83, G12, G14)

Accounting for global warming risks: Resource management under event uncertainty

1996 ◽  
Vol 20 (6-7) ◽  
pp. 1289-1305 ◽  
Author(s):  
Yacov Tsur ◽  
Amos Zemel
Keyword(s):  
Global Warming ◽  
Resource Management ◽  
Event Uncertainty

scholarly journals Bacteria-Oil Microaggregates Are an Important Mechanism for Hydrocarbon Degradation in the Marine Water Column

mSystems ◽  
2021 ◽  
Author(s):  
Amanda M. Achberger ◽  
Shawn M. Doyle ◽  
Makeda I. Mills ◽  
Charles P. Holmes ◽  
Antonietta Quigg ◽  
...  
Keyword(s):  
Water Column ◽  
Biological Response ◽  
Deepwater Horizon ◽  
Marine Water ◽  
Hydrocarbon Degradation ◽  
Marine Snow ◽  
Event Uncertainty

Vast quantities of oil-associated marine snow (MOS) formed in the water column as part of the natural biological response to the Deepwater Horizon drilling accident. Despite the scale of the event, uncertainty remains about the mechanisms controlling MOS formation and its impact on the environment.

Efficiency of Price Discovery during Nighttime Trading Session : Evidence from KOSPI200 Global Futures

2013 ◽  
Vol 21 (2) ◽  
pp. 169-202
Author(s):  
Woo-Baik Lee ◽  
Min-Cheol Woo
Keyword(s):  
Stock Market ◽  
Price Discovery ◽  
Price Change ◽  
Time Span ◽  
Tick Data ◽  
Close Time ◽  
Event Uncertainty ◽  
Global Futures

Trading of KOSPI 200 futures on CME Globex platform, which was launched in November 2009, starts at 18:00 and closes at 05:00 in the next morning. This paper examines how price of KOSPI200 Global futures is discovered during nighttime trading session by using tick data. The overall results of this study can be summarized as follows; First, we find that the weighted price contribution (WPC) exhibits asymmetric ‘W’-shaped curve during session. This finding is interpreted as that information is consequently transmitted from Globex and NYSE with ‘U’-shaped curve of intradaily price discovery to KOSPI 200 Global futures. Meanwhile, the weighted volume contribution (WVC) also shows ‘W’-shaped curve but weighted price contribution per volume contribution (WPCV) indicates asymmetric ‘U’-shaped curve. This finding that a trade is more (less) informative when trading intensity is higher (lower) provides evidence of partially supporting the “Event Uncertainty Hypothesis” over “Hot Potato Hypothesis”. Second, the price change of closing to opening time significantly contributes to price change during the close-to-close time span. This result explains information during regular daytime trading of KOSPI200 futures is efficiently incorporated in opening price of nighttime session. Third, nighttime traders of KOSPI200 futures recognize volatility of US stock market as more valuable information than the price of futures on CME Globex.

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