Data

Our empirical tests use the exchange rates of AUD, NZD, CAD, EUR, GBP and JPY against USD as a common denominator.5Under this definition, changes in (logarithmic) exchange rates can be interpreted as the USD return for holding one unit of foreign currency. The order flow of a currency measures the difference between buyer-initiated trades and seller-initiated trades of the currency. Absolute order flow differentials calculate the absolute values of the differences between two currencies’ order follows.

We obtain data on the raw currency transactions and quotes from Thomson Reuters Tick History (TRTH) provided by the Securities Industry Research Centre of Asia-Pacific (SIRCA). TRTH covers all FX trades conducted in the D2000-2 electronic FX brooking system, a brokered inter-dealer trading platform run by Thomson Reuters. D2000-2 is one of the two main electronic brokers in this market, the other being Electronic Brooking Services (EBS). EBS is the dominant trading platform used for the USD–EUR and USD–JPY rates, but most of the trading under the USD–AUD, USD–NZD and USD–GBP rates is done via Thompson Reuters D2000-2 (Smyth, 2009). However, although we only have access to the D2000-2 data on the USD–EUR and USD–JPY rates as well as other rates, previous studies (e.g., Danielsson and Payne, 2012) show that both databases have the same patterns and prices as well as order flows moving in the same direction.6

5

We exclude the three Nordic currencies (Danish krone, Swedish krona and Norwegian krone) and the Swiss franc because of a lack of data availability over the sample period. The definition of order flow is equal to the definition used in Evans and Lyons (2002a).

6 Due to the decentralized nature of the foreign exchange market, order flow data is available from end-user transactions (proprietary customer transactions from major banks), interdealer transactions (Thomson Reuters D2000-1), or brokered interdealer transactions (Thomson Reuters D2000-2 and EBS). In theory, the data from all sources should have similar characteristics and predictive content, as customer demand is the primary reason for trading. For a review of the different electronic trading systems we refer to Bjonnes and Rime (2005). Other studies using order flow data from Thomson Reuters D2000-2 include Payne (2003), Carlson and Lo (2006) and

Each transaction record contains a time stamp for the trade and a variable indicating the trade as a market buy or sell and the transaction price. This makes it unnecessary to use potentially inaccurate algorithms to assign the direction of the trade. A limitation of the data is the lack of information about the monetary value of each trade, as only the sign of each trade is given. However, studies on the information content of order flow data (e.g., Lyons and Moore, 2009) show that the signed order flow volume leads to the same conclusions as the absolute monetary volume of trades. This might be caused by the high degree of standardisation in the interdealer market, with a minimum trade volume of 1 million units of a base currency.

In addition to individual transactions, TRTH also provides intraday summary statistics. The database reports an opening (closing) bid and an opening (closing) ask price over certain time intervals ranging from 1 minute to 1 hour, which TRTH constructs using the bid and ask quote closest to the beginning (end) of the fixed time interval. After filtering out outliers, we calculate the exchange rate return as the difference between two log-mid quotes with the mid-quote calculated as the average of the closing bid and the closing ask quote. We add up tick-by-tick buying orders within a predetermined intraday time interval to obtain one aggregated value for that interval and do the same for selling orders. We then take the difference between the two values to yield the order flow for the time interval.

It is well documented that trading activity in FX markets slows down remarkably during weekends’ and certain holidays’ (Andersen, Bollerslev, Diebold, and Vega, 2003; Bauwens, Omrane, & Giot, 2005). Thus, we exclude a number of sparse trading periods. Following Danielsson, Luo and Payne (2012) and Frömmel, Kiss, and Pintér (2011), we

Danielsson and Payne (2012). The studies of Kileen, Lyons and Moore (2006) and Berger et al. (2008) use data from EBS. The initial studies of Evans and Lyons (2002a, 2002b) use data from Thomson Reuters D2000-1, a direct interdealer trading platform. Proprietary data from end-user trades are used in the studies of Evans and Lyons (2005b) and Cerrato et al. (2015).

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remove the overnight period7, weekends and some world–wide public holidays. Furthermore, following Selçuk and Gençay (2006) we eliminated any days that indicated no market activity. Specifically, we eliminated the days within which there were at least 12 consecutive 5-minute zero returns.

The total sample period spans from 3 January 2002 to 29 December 2013. It contains 361,977 observations for the 5-minute frequency, 124,000 observations for 15-minute frequency and over 30,000 observations for 60-minute frequency on the log returns as well as order flows for each exchange rate. Our sample period starts January 3, 2002 for two reasons: it was when the euro was physically introduced and it is the first date in Thomson Reuters D 2000-2 on which the required intraday data on the euro become available. Table 2.1 provides descriptive statistics for the return series.8