Spencer Wheatley

ETH Zürich

Rämistrasse 101

ZUE F7

Zürich, 8092

Switzerland

SCHOLARLY PAPERS

7

DOWNLOADS
Rank 18,817

SSRN RANKINGS

Top 18,817

in Total Papers Downloads

2,745

SSRN CITATIONS

6

CROSSREF CITATIONS

3

Scholarly Papers (7)

1.

Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model

Swiss Finance Institute Research Paper No. 18-22
Number of pages: 22 Posted: 16 Mar 2018 Last Revised: 22 Mar 2018
ETH Zürich, ETH Zürich - Department of Management, Technology, and Economics (D-MTEC), ETH Zürich, ETH Zürich and D ONE Solutions AG
Downloads 1,471 (13,069)
Citation 7

Abstract:

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Bitcoin, crypto-currencies, bubble, prediction, Metcalfe law, log-periodic power law singularity, LPPLS, Johansen-Ledoit-Sornette

2.

The British Pound on Brexit Night: A Natural Experiment of Market Efficiency and Real-Time Predictability

Swiss Finance Institute Research Paper No. 17-12
Number of pages: 38 Posted: 29 Mar 2017
Ke Wu, Spencer Wheatley and Didier Sornette
Southern University of Science and Technology, ETH Zürich and ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)
Downloads 691 (39,295)
Citation 2

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Brexit, efficient market hypothesis, response function, one factor model, prediction, market failure

3.

Estimation of the Hawkes Process with Renewal Immigration Using the EM Algorithm

Swiss Finance Institute Research Paper No. 14-53
Number of pages: 37 Posted: 08 Aug 2014 Last Revised: 08 Nov 2014
Spencer Wheatley, Vladimir Filimonov and Didier Sornette
ETH Zürich, Swiss Federal Institute of Technology Zurich (ETH Zurich) and ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)
Downloads 166 (192,925)
Citation 2

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Expectation-maximization algorithm; Branching process models; Renewal Cluster process models; Point process models; non-parametric estimation; Hawkes process; immigration; branching structure.

4.

Multiple Outlier Detection in Samples with Exponential & Pareto Tails: Redeeming the Inward Approach & Detecting Dragon Kings

Swiss Finance Institute Research Paper No. 15-28
Number of pages: 35 Posted: 19 Aug 2015 Last Revised: 23 Oct 2015
Spencer Wheatley and Didier Sornette
ETH Zürich and ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)
Downloads 152 (206,814)
Citation 3

Abstract:

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Outlier Detection, Exponential sample, Pareto sample, Dragon King, Extreme Value Theory

5.

The Endo-Exo Problem in High Frequency Financial Price Fluctuations and Rejecting Criticality

Swiss Finance Institute Research Paper No. 18-57
Number of pages: 24 Posted: 27 Aug 2018 Last Revised: 29 Aug 2018
Spencer Wheatley, Alexander Wehrli and Didier Sornette
ETH Zürich, ETH Zürich and ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)
Downloads 129 (235,754)
Citation 2

Abstract:

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mid-price changes, trade times, Hawkes process, endogeneity, criticality, Expectation- Maximization, BIC, non-stationarity, ARMA point process, spurious inference, external shocks

6.

The Fair Reward Problem: The Illusion of Success and How to Solve It

Swiss Finance Institute Research Paper No. 19-25
Number of pages: 43 Posted: 25 Apr 2019 Last Revised: 29 Apr 2019
Didier Sornette, Spencer Wheatley and Peter Cauwels
ETH Zürich - Department of Management, Technology, and Economics (D-MTEC), ETH Zürich and ETH Zürich
Downloads 114 (258,370)
Citation 1

Abstract:

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success, reward, luck, merit, measurement, scenario analysis, Darwinian, evolutionary learning, equality

7.

Scale-, time- and asset-dependence of Hawkes process estimates on high frequency price changes

Swiss Finance Institute Research Paper No. 20-39
Number of pages: 38 Posted: 16 May 2020 Last Revised: 18 May 2020
Alexander Wehrli, Spencer Wheatley and Didier Sornette
ETH Zürich, ETH Zürich and ETH Zürich - Department of Management, Technology, and Economics (D-MTEC)
Downloads 22 (549,696)

Abstract:

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Hawkes process; Integer-valued autoregressive process; Econometrics; High frequency financial data; Market microstructure; Spurious inference; Nonstationarity; EM algorithm