Trading price jump clusters in foreign exchange markets

https://doi.org/10.1016/j.finmar.2015.03.002Get rights and content

Highlights

  • We investigate trading opportunities of price jump clustering in the FX markets.

  • We show that price jumps carry a tradable signal for all currencies.

  • High bid–ask spread removes all excess profit for emerging market currencies.

  • Combination of the Euro and yen is robust to holding period and U.S.-related risk.

Abstract

We investigate trading opportunities of price jump clusters in the FX markets. We identify clusters for eight FX rates against the U.S. dollar from March 1, 2013 to June 6, 2013 sampled at a 5-minute frequency. We propose a high-frequency jump cluster-based trading strategy and show that jumps carry a tradable signal for all currencies; however, when incorporating the bid-ask spread, the only profitable currencies are the euro, yen and rand. From the portfolio perspective, a combination of the euro and yen represents a strategy robust to the holding period, minimizes the transaction costs, and diversifies out the U.S.-related risk.

Introduction

There is a huge body of evidence indicating that price jumps play a crucial role in the dynamics of pricing of all types of financial assets (e.g., Todorov, 2010, Lee, 2012). In particular, Lahaye, Laurent, and Neely (2011) show that jumps are linked to macro-announcements including real economy indicators and monetary policy decisions. Jiang, Lo, and Verdelhan (2011) investigate the large price movements for T-bonds. Brunnermeier and Pedersen (2009) and Mancini, Ranaldo, and Wrampelmeyer (2013) show that in the foreign exchange markets, price jumps are related to a lack of liquidity. Shleifer and Vishny (1997) focus on the liquidity around periods of significant financial markets news announcements.3 Finally, Broadie and Jain (2008), among others, show that price jumps are important for pricing derivatives and calculating risk profiles.

An important feature of price jumps, which has attracted scarce attention in the literature, is that jumps occur in clusters, a feature similar to volatility clustering. Osler (2005) explains the presence of clusters in the currency markets as a result of technical trading with stop-losses4 and the reaction of investors with different investment horizons ranging from algorithmic traders to central bankers and pension funds. Brand, Buncic, and Turunen (2010) report that clustering may be implied by processing of news announcements including the “soft” communications after the announcements, as in the case of the European Central Bank (ECB) communications, which are relevant for the money market.

The contributions of this paper are threefold. First, we propose a formal procedure to identify clusters of price jumps and employ it for the foreign exchange spot prices of eight currencies (euro, Japanese yen, Hungarian forint, Mexican peso, Polish zloty, Russian ruble, Turkish lira, and South African rand) against the U.S. dollar observed at high-frequency over the period from March 1, 2013 to June 6, 2013. This unique and novel data set, provided by Morningstar, covers the rise of “Abenomics” and periods when the U.S. liquidity tapering expectation starts to materialize.

Second, we explore whether the presence of clusters provides trading opportunities. We propose a high-frequency trading strategy based on the framework of Obizhaeva and Wang (2013), where a marginal trader goes one unit long/short for a fixed period of time based on the occurrence of price jumps. In fact, such a strategy can be interpreted as a form of delta trades, as it is volatility based. The results of the trading strategy indicate that the two most traded currency pairs in our sample, the euro and yen, bring a positive profit even after accounting for the bid–ask spread and slippage. In addition, the South African rand provides a positive outcome for some of the holding horizons. The Sharpe ratio is rather low for all the profitable trades with the profit and loss (P/L) ratio being well below two. Further, including the slippage into the trading strategy helps provide support for the claim that jumps carry a signal, as the slippage decreases the profitability of trades. Such seemingly inefficient behavior can be interpreted as the presence of over-hedging during the distressed period. It is a well-documented fact among practitioners that before scheduled news announcements and significant policy changes, the market makers mark-up the short-term (overnight) implied volatility at the foreign exchange markets as the uncertainty increases and the impact of the announcements may increase the realized volatility.5

Third, we evaluate the performance and risk profile for a portfolio of currencies. For the market-wide portfolio, the trader accumulates a loss due to the large bid–ask spreads. However, for a portfolio consisting of the developed market currencies, those with very low transaction costs, the portfolio is stable with respect to the holding period. Even for the portfolio, however, both the Sharpe and the P/L ratios indicate the fragility of the trading strategy. Then, following Lustig, Roussanov, and Verdelhan (2011), we construct the U.S. dollar index, an equally weighted basket of all currencies, capturing the risk factors tracking the global (U.S.-related) market. We estimate the beta for the jump trading strategies of individual currencies. The euro and the yen have a very different risk profile with respect to the holding period, thus trading the Euro and the yen simultaneously also hedges U.S. exposure. Finally, we discuss the hedging of all individual trends.

This paper is related to the existing literature in several directions. First, it contributes to the literature on modeling the spot prices in foreign exchange markets. Shleifer and Vishny (1997) point out that liquidity in the foreign exchange markets is crucial for arbitrage trading and market efficiency. Periods when markets expect ground-breaking news may suffer liquidity shocks as fund managers tend to rather over-hedge and thus are more reluctant to trade. Therefore, a period with heavily interventions by central banks may imply temporal market inefficiencies. Boudt and Petitjean (2014) show the link between price jumps and liquidity for the equity markets. Further, Lyons (2001) documents that foreign exchange markets are of limited transparency, their market participants are very heterogeneous with different scope and volume of operations, and trading occurs under the decentralized dealership structure.

Second, this paper extends the momentum trading strategy literature into the intraday scale. Menkhoff, Sarno, Schmeling, and Schrimpf (2012) investigate momentum strategies in foreign exchange markets using daily data and show the presence of a persistent and profitable trend for some currency portfolios, mainly minor currencies with high transaction costs. This is in line with what is found for stocks with high-credit risk, as identified by Avramov, Chordia, Jostova, and Philipov (2007) and Eisdorfer (2008), or for non-investment grade corporate bonds, by Jostova, Nikolova, Philipov, and Stahel (2013). In the case of equities, Chan, Jegadeesh, and Lakonishok (1996) suggest that analysts tend to gradually incorporate information into the price and thus a trend can emerge. On the other hand, Hong, Lim, and Stein (2000) find the presence of strong momentum for stocks with weak coverage. Korajczyk and Sadka (2004) report that the momentum is very often present for assets with high transaction costs, which wipes out any profit. Baillie and Chang (2011) further relate the momentum strategy to uncovered interest rate parity.

Finally, this paper contributes to the literature on profitable trading strategies. Verdelhan (2010) shows that around the news announcement, the risk aversion of traders such as hedge funds, large investors, central banks, and institutional investors rises. This implies that traders are less likely to exploit investment opportunities (i.e., the realized Sharpe ratio is higher than the subjective Sharpe ratio around announcements). Griffin, Harris, and Topaloglu (2003) show that limit orders can make contrarian strategies profitable. The authors use NASDAQ 100 stocks sampled at a 5-minute frequency to explore the trading activity around the excess returns. They show that individual and institutional activities after large returns are, on average, higher than prior to the announcements. The period of increased activity lasts between 15 and 30 minutes. Further, Hendershott, Jones, and Menkveld (2011) analyze the impact of automated trading at the NYSE since its introduction in 2003 and find that it reduces trade-related price discovery. This strengthens the significance of limit-order book effects in foreign exchange markets, which are liquid and involve many algorithm-based traders. Brandt, Kishore, Santa-Clara, and Venkatachalam (2008) show that post-announcement trading produces significant cumulative returns. Using stocks at quarterly frequency, the authors find a low-frequency arbitrage type of opportunity similar to our results and suggest that markets do not react efficiently to the earnings announcements. Finally, Brunnermeier, Nagel, and Pedersen (2008) report that carry trades are subject to crash risk, while our trading strategy is de facto profiting on crash risk.

The remainder of the paper is organized as follows: in Section 2, we describe the high-frequency data set we use, the price jump estimation procedure, and report some descriptive statistics. In Section 3, we propose a novel approach to identify clusters of price jumps. In Section 4, we introduce a trading strategy based on the price jump clusters, and discuss the dollar index and diversification of jumps in the portfolio of foreign currencies. We conclude in Section 5.

Section snippets

Data description and price jumps

In this section, we describe the high-frequency data set as well as the filtering procedure to clean the data. We also provide summary statistics of the log-returns. Next, we focus on the identification and properties of price jumps.

Clusters of price jumps in foreign exchange markets

The clustering of price jumps implies that if we observe a jump, the probability of observing another jump in close proximity is higher relative to the case of no jumps. Fig. 1 further illustrates this point reporting the euro for March 7, 2013, a representative day with a cluster of price jumps. The left-hand panel of the figure shows that there is both volatility clustering during the trading day and the clustering of price jumps. Five jumps occur around midday in London. On that day, the

Trading the clusters

For the purpose of the trading exercise, we assume that the trader enters the position by buying the currency at spot prices, holds it for a certain time horizon, and then unwinds the position. We also assume that the trader is marginal, and does not have the power to significantly affect the prices. Such traders are genuine to the foreign exchange markets with their large daily turnover and presence of large institutional investors and central banks. In fact, the absolute amount of currency

Conclusions

In this paper, we investigate the trading opportunities of price jump clusters in the foreign exchange markets. We propose a formal procedure to identify clusters of price jumps and validate it empirically using the tick-by-tick foreign exchange spot prices for the euro, yen, forint, peso, zloty, ruble, lira, and rand denominated in U.S. dollars over the period ranging from March 1, 2013 to June 6, 2013, sampled at a 5-minute frequency and pre-averaged to control for market microstructure

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    We wish to thank participants in the Finance Research Workshop at Cass Business School, in particular Ana-Maria Fuertes, Simon Hayley, and Meziane Lasfer for useful suggestions. Special thanks to Saeed Amen, Richard Payne, and Lucio Sarno for very useful comments on a previous version of the paper. We are greatly in debt with the Co-editor, Amit Goyal, and two anonymous referees for providing us with very insightful comments and suggestions that greatly helped to improve the paper. We are grateful to Morningstar, in particular to Richard Barden, for having made available the rich data set used in this study. Jan Novotný acknowledges funding from the European Community׳s Seventh Framework Program FP7-PEOPLE-2011-IEF under Grant agreement number PIEF-GA-2011-302098 (Price Jump Dynamics), from the Centre for Econometric Analysis and GAČR Grant 14-27047S. Dmitri Petrov of Nomura has joint authored this paper in his personal capacity and any views expressed herein are those of the authors and do not reflect in any way the views of Nomura.

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