Brexit or Bremain ? Evidence from bubble analysis

📝 Abstract

We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd June 2016. Our implementation includes an enhanced model calibration using Genetic Algorithms. We selected a few historical financial series sensitive to the Brexit/Bremain scenario, representative of multiple asset classes. We found that equity and currency asset classes show no bubble signals, while rates, credit and real estate show super-exponential behaviour and instabilities typical of bubble regime. Our study suggests that, under the JLS model, equity and currency markets do not expect crashes or sharp rises following the referendum results. Instead, rates and credit markets consider the referendum a risky event, expecting either a Bremain scenario or a Brexit scenario edulcorated by central banks intervention. In the case of real estate, a crash is expected, but its relationship with the referendum results is unclear.

💡 Analysis

We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd June 2016. Our implementation includes an enhanced model calibration using Genetic Algorithms. We selected a few historical financial series sensitive to the Brexit/Bremain scenario, representative of multiple asset classes. We found that equity and currency asset classes show no bubble signals, while rates, credit and real estate show super-exponential behaviour and instabilities typical of bubble regime. Our study suggests that, under the JLS model, equity and currency markets do not expect crashes or sharp rises following the referendum results. Instead, rates and credit markets consider the referendum a risky event, expecting either a Bremain scenario or a Brexit scenario edulcorated by central banks intervention. In the case of real estate, a crash is expected, but its relationship with the referendum results is unclear.

📄 Content

Brexit or Bremain ? Evidence from bubble analysis

Marco Bianchetti, Intesa Sanpaolo, University of Bologna1 Davide Galli, Università degli Studi di Milano, Physics Dept. Camilla Ricci Angelo Salvatori, Università degli Studi di Milano, Physics Dept. Marco Scaringi, Università degli Studi di Milano, Physics Dept.

First version 21 June 2016, this version: 22 June 2016

Abstract

We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd June 2016. Our implementation includes an enhanced model calibration using Genetic Algorithms. We selected a few historical financial series sensitive to the Brexit/Bremain scenario, representative of multiple asset classes. We found that equity and currency asset classes show no bubble signals, while rates, credit and real estate show super-exponential behaviour and instabilities typical of bubble regime. Our study suggests that, under the JLS model, equity and currency markets do not expect crashes or sharp rises following the referendum results. Instead, rates and credit markets consider the referendum a risky event, expecting either a Bremain scenario or a Brexit scenario edulcorated by central banks intervention. In the case of real estate, a crash is expected, but its relationship with the referendum results is unclear.

JEL classifications: C13, C32, C53, G1.

Keywords: JLS, Johansen-Ledoit-Sornette, bubble, crash, crisis, Brexit, Bremain, UK, UE, referendum, forecast, polls, odds, historical series, super-exponential, Log-Periodic Power Law, LPPL, calibration, genetic algorithm, fit.

1 Corresponding author, marco.bianchetti(at)unibo.it.

Table of contents

Brexit or Bremain ? …………………………………………………………………………….. 2 2. Methodology …………………………………………………………………………………….. 2 3. Results ……………………………………………………………………………………………. 4 4. Conclusions ………………………………………………………………………………………. 8 5. References ……………………………………………………………………………………….. 9 6. Disclaimer and acknowledgments……………………………………………………………. 9

1. Brexit or Bremain ? On Dec. 17, 2015 the UK Parliament approved the European Union Referendum Act 2015 to hold a referendum on whether the United Kingdom should remain a member of the European Union (EU). The referendum will be held2 on Jun. 23, 2016, with the following Q&A:
    Q: ”Should the United Kingdom remain a member of the European Union or leave the European Union?  A1: “Remain a member of the European Union”  A2: “Leave the European Union” The two scenarios above were called “Bremain” and “Brexit”, respectively. In case of Brexit decision, there is no immediate withdrawal. Instead, a negotiation period begins to establish the future relationship between UK and EU. The negotiation length is two years, extendible upon agreement between the two parties. For example, the agreements between EU and Switzerland took 10 years of negotiations. Referendum campaigning has been suspended on 16th June 2016 following the shooting of Labour MP Jo Cox. This event has had a strong impact on the public opinion, rapidly changing the opinion polls and possibly the attitude of the country.

Forecasting the results of the 23rd June 2016 referendum, given the apparent parity between Bremain and Brexit supporters and the high percentage of undecided voters observed until the week before, is clearly a very challenging task, with a high error probability. Nevertheless, there exist at least three sources of data supporting forecast analysis: opinion polls [8] [10], bookmakers betting odds [9], and market data [10]. In this paper we recur to a different forecasting approach, described in the next section.

2. Methodology We applied a forecasting methodology based on the Johansen-Ledoit-Sornette (JLS) model, developed since the 90s at ETHZ by D. Sornette and co-authors (see e.g. [1]-[4] and refs. therein). The JLS model has been extensively applied to bubbles, crashes and crisis analysis in many fields. For applications in finance see e.g. the Financial Crisis Observatory [5].

The JLS model assumes that, during a bubble regime, the asset mean value follows the so- called Log-Periodic Power Law (LPPL) function,

𝐿𝑃𝑃𝐿(𝑡) = 𝐴+ 𝐵(𝑡𝑐−𝑡)𝑚+ 𝐶(𝑡𝑐−𝑡)𝑚𝑐𝑜𝑠(𝜔𝑙𝑜𝑔(𝑡𝑐−𝑡) + 𝜙),

2 We stress that this paper was delivered before the UK referendum scheduled for 23rd June 2016.

𝐿𝑃𝑃𝐿(𝑡) = 𝑙𝑛[𝔼𝑡[𝑝(𝑇)]] = 𝑙𝑛[𝑝(𝑡)],

(1)

where 𝑝(𝑡) is the asset

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