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Currency Trading

Currency trading (forex or FX) offers investors a way to trade on country or regional fiscal/monetary situations and tendencies. Are there reliable ways to exploit this market? Does it represent a distinct asset class?

Good Currency, Bad Currency?

Can currency carry traders improve performance by excluding “bad” currencies? In the April 2015 version of their paper entitled “Good Carry, Bad Carry”, Geert Bekaert and George Panayotov investigate the differences between good and bad carry trades (long high-yield and short low-yield) constructed from G-10 currencies. They define good (bad) trades as those with relatively high (low) Sharpe ratios and slightly negative or positive (more negative) skewness. Their benchmark portfolio is long (short) the equally weighted five G-10 currencies with the highest (lowest) yields. Their process for dynamically and progressively enhancing the currency carry trade universe is to isolate currencies associated with bad carry trades by each month: (1) experimentally excluding currencies one at a time from the benchmark and dropping the one that most depresses inception-to-date Sharpe ratio (inception December 1984); and, (2) repeating until they have eliminated seven currencies. The number of long positions is equal to the number of short positions in all test portfolios, with positions equally weighted. Monthly performance calculations are net (exploiting availability of bid and ask quotes). Using one-month forward quotes on the last trading day of each month and spot quotes on the last day of the next month for all G-10 currencies during December 1984 through June 2014 (354 months), they find that: Keep Reading

Year-end Global Growth and Future Asset Class Returns

Does fourth quarter global economic data set the stage for asset class returns the next year? In their February 2015 paper entitled “The End-of-the-year Effect: Global Economic Growth and Expected Returns Around the World”, Stig Møller and Jesper Rangvid examine relationships between level of global economic growth and future asset class returns, focusing on growth at the end of the year. Their principle measure of global economic growth is the equally weighted average of quarterly OECD industrial production growth in 12 developed countries. They perform in-sample tests 30 countries and out-of-sample tests for these same 12 countries (for which more data are available). Out-of-sample tests: (1) generate initial parameters from 1970 through 1989 data for testing during 1990 through 2013 period; and, (2) insert a three-month delay between economic growth data and subsequent return calculations to account for publication lag. Using global industrial production growth as specified, annual total returns for 30 country, two regional and world stock indexes, currency spot and one-year forward exchange rates relative to the U.S. dollar, spot prices on 19 commodities, total annual returns for a global government bond index and a U.S. corporate bond index, and country inflation rates as available during 1970 through 2013, they find that: Keep Reading

Best Currency Value Strategy?

Which method of relative currency valuation works best for currency trading? In their February 2015 paper entitled “Currency Value Strategies”, Ahmad Raza, Ben Marshall and Nuttawat Visaltanachoti run a horse race of four currency value strategies:

  1. Real Exchange Rate: nominal spot exchange rate with the U.S. dollar times the ratio of local consumer prices in local currency to U.S. consumer prices in U.S. dollars.
  2. Real Exchange Rate Change: one minus the ratio of the average real exchange rate between 5.5 and 4.5 years ago to the real exchange rate three months ago.
  3. Purchasing Power Parity: from the Organization for Economic Co-operation and Development (OECD).
  4. Big Mac Index: raw version from the Economist.

Their approach is to calculate excess returns in U.S. dollars from a portfolio that is iteratively long (short) the fifth of currencies that are most undervalued (overvalued) per each of these four metrics and hold the positions over periods ranging from one week to 12 months. Using weekly and monthly spot and forward foreign exchange rate data for 39 developed and emerging market currencies versus the U.S. dollar during January 1972 through July 2013, they find that: Keep Reading

Currency Carry Trade Over the Long Run

Does the currency carry trade, financing short-term deposits in currencies with high interest rates with short-term loans in currencies with low interest rates (or being long and short forward contracts in currencies with high and low interest rates) generate a reliably attractive return? In the November 2014 version of their paper entitled “Empirical Evidence on the Currency Carry Trade, 1900-2012”, Nikolay Doskov and Laurens Swinkels measure annual nominal and real carry trade returns for a large sample of currencies over a long period covering multiple currency regimes. They use yields on local Treasury bills (T-bills) or equivalents to approximate short-term interest rates and make some adjustments to account for government defaults. To estimate carry trade returns, they sort currencies each year based on associated T-bill yields and take equally weighted long (short) positions in the four currencies with the highest (lowest) yields. Using annual exchange rates and associated T-bill yields for 20 currencies during 1900 through 2012 (19 currencies before 1925 and 12 currencies after 1998), they find that: Keep Reading

Comprehensive, Long-term Test of Technical Currency Trading

Does quantitative technical analysis work reliably in currency trading? If so, where does it work best? In their May 2013 paper entitled “Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-Scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market”, Po-Hsuan Hsu and Mark Taylor test the effectiveness of a broad set of quantitative technical trading rules as applied to exchange rates of 30 currencies with the U.S. dollar over extended periods. They consider 21,195 distinct technical trading rules: 2,835 filter rules; 12,870 moving average rules; 1,890 support-resistance signals; 3,000 channel breakout rules; and, 600 oscillator rules. They employ a test methodology designed to account for data snooping in identifying reliably profitable trading rules. They also test whether technical trading effectiveness weakens over time. In testing robustness to trading frictions, they assume a fixed one-way trading cost of 0.025%. Using daily U.S. dollar exchange rates for nine developed market currencies and 21 emerging market currencies during January 1971 through July 2011, they find that:

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Stash Some Cash in Bitcoins?

In his August 2014 paper entitled “Bitcoin Myths and Facts”, Campbell Harvey examines eight claims about bitcoin. One of these claims is that bitcoin is currently too volatile to serve as a store of value. Using daily data for the dollar-bitcoin exchange rate during mid-July 2010 through mid-August 2014, he finds that: Keep Reading

Exploiting Exchange Rate SMA Signals

Are simple moving averages (SMA) effective in generating signals for short-term currency trading? In the April 2014 draft of his paper entitled “ANANTA: A Systematic Quantitative FX Trading Strategy”, Nicolas Georges investigates the effectiveness of fast (2-day) and slow (15-day) SMAs as indicators of currency exchange rate evolutions when applied to ten G10 currency pairs and aggregated. His objective is to buy (sell) currencies expected to appreciate (depreciate) based on aggregation of binary signals (see the first chart below). He rebalances the portfolio twice daily when liquidity is high at the London and New York closes. He uses market orders and includes actual trading costs unique to each currency pair, based on bid-ask spreads ranging from 0.0036% to 0.035%. He does not use stop-losses. He compiles results in U.S. dollars. Using twice daily exchange rates for G10 currency pairs during January 2003 through December 2013, he finds that: Keep Reading

Best Way to Trade Trends?

What is the best way to generate price trend signals for trading futures/forward contracts? In their December 2013 paper entitled “CTAs – Which Trend is Your Friend?”, Fabian Dori, Manuel Krieger, Urs Schubiger and Daniel Torgler compare risk-adjusted performances of three ways of translating trends into trading signals:

  1. Binary signals (up or down) trigger 100% long or 100% short trades. When trends are strong (ambiguous), this approach generates little trading (whipsaws/over-commitment to weak trends). The price impact of trading via this approach may be substantial for large traders.
  2. Continuously scaled signals trigger long or short trades with position size scaled according to the strength of up or down trend; the stronger the trend, the larger the position. Changes in trend strength generate incremental position adjustments.
  3. Empirical distribution signals trigger long or short trades with position size scaled according to the historical relationship between trend strength and future return. The strongest trend may not indicate the strongest future return, and may actually indicate return (and therefore position) reversal. Changes in trend strength generate position adjustments.

They test these three approaches for comparable trends exhibited by 96 futures/forward contract series, including: 30 currency pairs, 19 equity indexes, 11 government bond indexes, 8 short-term interest rates (STIR) and 28 commodities. They consider two risk-adjusted return metrics: annualized return divided by annualized volatility, and annualized return divided by maximum drawdown. They ignore trading frictions. Using prices for these 96 series from 1993 to 2013, they find that: Keep Reading

Effects of Commodities and Stocks on Currency Carry Trades

Are currency traders the last ones to know? In the February 2014 draft of their paper entitled “Cross-Asset Return Predictability: Carry Trades, Stocks and Commodities”, Helen Lu and Ben Jacobsen investigate whether commodity and stock index returns predict currency carry trade performance. They consider equally weighted carry trade strategies that each month buy (sell) one-month forward contracts for the one, two or three currencies with the highest (lowest) beginning-of-month interest rates and hold to maturity. They account for bid-ask spreads and express profits in U.S. dollars. They evaluate the power of three commodity indexes (CRB Spot, CRB Raw Industrials Spot and CRB Metals Spot) and three total return equity indexes (MSCI All Country, MSCI World and S&P 500) to predict carry trade profitability. Using monthly levels of the commodity and stock indexes and monthly one-month forward rates and spot rates for the G-10 currencies during February 1988 through December 2011, they find that: Keep Reading

Using Economic Fundamentals to Predict Currency Exchange Rates

Do country economic fundamentals provide exploitable information about future changes in associated currency exchange rates? In the June 2013 version of their paper entitled “Currency Risk Premia and Macro Fundamentals”, Lukas Menkhoff, Lucio Sarno, Maik Schmeling and Andreas Schrimpf investigate the usefulness of economic fundamentals in currency trading by measuring the performance of multi-currency hedge portfolios formed by sorting on lagged economic variables across 35 countries. They take the perspective of a U.S. investor by measuring all exchange rates versus the U.S. dollar. The country economic variables they consider are: (1) interest rates; real Gross Domestic Product (GDP) growth; real money growth (from currency in circulation); and, real exchange rates. They calculate growth rates based on 20-quarter rolling averages. They form hedge portfolios from extreme fourths (quartiles) of ranked currencies, rebalanced annually at year end, and calculate returns in excess of short-term interest rates. Using quarterly currency exchange rate, short-term interest rate, real GDP, Consumer Price Index (CPI) and currency in circulation for 35 countries/currencies for out-of-sample testing from the first quarter of 1974 through the third quarter of 2010, they find that: Keep Reading

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