Robust Prediction of Triangular Currency Arbitrage with Liquidity and Realized Risk Measures: A New Wavelet-Based Ultra-High-Frequency Analysis

40 Pages Posted: 16 Aug 2017

See all articles by Nikola Gradojevic

Nikola Gradojevic

University of Guelph, Department of Economics and Finance; University of Bologna - Rimini Center for Economic Analysis (RCEA)

Ramazan Gencay

Simon Fraser University

Deniz Erdemlioglu

IESEG School of Management and CNRS - France

Date Written: February 11, 2017

Abstract

We develop a new framework to characterize the dynamics of triangular (three-point) arbitrage in electronic foreign exchange markets. To examine the properties of arbitrage, we propose a wavelet based regression approach that is robust to estimation errors, measurement bias and persistence. Relying on this wavelet-based (denoising) inference, we consider various liquidity and market risk indicators to predict arbitrage in a unique ultra-high-frequency exchange rate data set. We find strong empirical evidence that limit order book, realized volatility and cross-correlations help forecast triangular arbitrage profits. The estimates are statistically significant and relevant for investors such that on average 80−100 arbitrage opportunities exist with a short duration (100−500 milliseconds) on a daily basis. Our analysis also reveals that triangular arbitrage opportunities are counter-cyclical at ultra-high-frequency levels: arbitrage returns tend to increase (decrease) in periods when volatility risk and correlations are relatively low (high). We show that liquidity-driven microstructure measures, however, appear to be more powerful in exploiting arbitrage profits compared to market-driven factors.

Keywords: Investment analysis, Exchange rates, Triangular arbitrage, Limit order book, Wavelets.

JEL Classification: G15, G17, F31

Suggested Citation

Gradojevic, Nikola and Gencay, Ramazan and Erdemlioglu, Deniz, Robust Prediction of Triangular Currency Arbitrage with Liquidity and Realized Risk Measures: A New Wavelet-Based Ultra-High-Frequency Analysis (February 11, 2017). Available at SSRN: https://ssrn.com/abstract=3018815 or http://dx.doi.org/10.2139/ssrn.3018815

Nikola Gradojevic (Contact Author)

University of Guelph, Department of Economics and Finance ( email )

50 Stone Road East
Guelph, Ontario N1G 2W1
Canada

HOME PAGE: http://https://www.uoguelph.ca/economics/users/nikola-gradojevic

University of Bologna - Rimini Center for Economic Analysis (RCEA) ( email )

Via Patara, 3
Rimini (RN), RN 47900
Italy

Ramazan Gencay

Simon Fraser University ( email )

Department of Economics
8888 University Drive
Burnaby, British Columbia V5A 1S6
Canada

Deniz Erdemlioglu

IESEG School of Management and CNRS - France ( email )

3 rue de la Digue
Lille, 59000
France

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