Currency Trading

Do currency exchange factor strategies usefully diversify a set of conventional asset classes? In their May 2018 paper entitled “Currency Management with Style”, Harald Lohre and Martin Kolrep investigate the systematic harvesting of currency exchange carry, value and momentum strategies, specified as follows and applied to the G10 currencies:

• Carry – buy (sell) the three equally weighted currency forwards with the highest (lowest) short-term interest rates, reformed monthly.
• Momentum – buy (sell) the three equally weighted currency forwards with the greatest (least) appreciation over the past three months, reformed monthly.
• Value (long-term reversion) – buy (sell) the three equally weighted currency forwards with the lowest (highest) change in their real exchange rates, based on purchasing power parity, over the past 60 months, reformed monthly.

They examine in-sample (full-sample) mean-variance relationships for these strategies to assess their value as diversifiers of five conventional asset classes (U.S. stocks, commodities, U.S. Treasury bonds, U.S. corporate investment-grade bonds and U.S. corporate high-yield bonds). They also look at potential out-of-sample benefits of these strategies based on information available at the time of each monthly rebalancing as additions to a risk parity portfolio of the five conventional assets from the perspective. For this out-of-sample test, they consider both minimum variance (tail risk hedging) and mean-variance optimization (return seeking) for aggregating the three currency strategies. Using monthly data for the selected assets from the end of January 1999 through December 2016, they find that: Keep Reading

Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

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Should investors consider adding crypto-assets to portfolios of traditional assets? In their April 2018 paper entitled “Cryptocurrencies as an Asset Class?”, Sinan Krueckeberg and Peter Scholz investigate whether cryptocurrencies (crypto-assets) qualify as a distinct asset class, attractively diversifying portfolios of traditional asset classes. They distinguish between cryptographic coins (with their own blockchains) and tokens (using third party blockchains). They focus on the 10 coins and tokens with the largest market capitalizations as of December 8, 2017 that have at least three months of prices. Their investigation involves:

• Computing three types of pairwise correlations between crypto-assets and between crypto-assets and traditional assets to measure crypto-asset uniqueness.
• Comparing trading volumes and ratios of trading volume to market capitalization to those for several large stocks to assess liquidity.
• Measuring frequencies of crypto-asset market circuit breaker trips and limit up/down triggers to assess stability.
• Adding crypto-assets to traditional portfolios with quarterly rebalancing to test impact on Sharpe ratio with ex-post (perfect foresight) optimization and three approaches to ex-ante allocation (the simplest a fixed 1% allocation to crypto-assets). Traditional portfolios consist of stocks and bonds, plus (progressively) real estate, gold and oil.

Using daily price data for the top 10 coins/tokens and for traditional asset class proxies, and tick-by-tick crypto-asset price data to assess stability, during late April 2013 through early November 2017, they find that:

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What is the interplay among investable proxies for the U.S. dollar, gold and crude oil? Do changes in the value of the dollar lead those in hard assets? To investigate, we relate the return series of three exchange-traded funds: (1) the futures-based PowerShares DB US Dollar Index Bullish (UUP); (2) the spot-based SPDR Gold Shares (GLD); and, (3) the spot-based United States Oil (USO). Using monthly, weekly and daily prices for these funds during March 2007 (limited by inception of UUP) through April 2018 (134 months), we find that: Keep Reading

Is there a way to predict the value of a crypto-asset like Bitcoin? In their March 2018 paper entitled “An Equilibrium Valuation of Bitcoin and Decentralized Network Assets”, Emiliano Pagnotta and Andrea Buraschi model the value of Bitcoin and similar blockchain network tokens via a model that characterizes:

• Demand, described by current/future number of users and their strength of preference for privacy (trustworthiness), reflecting the economic strength of the network and its tokens.
• Supply, described by number of miners in the associated proof-of-work competition and cost of mining, reflecting network trustworthiness.

They consider a quantitative version of the model calibrated to the properties of the Bitcoin network at the end of 2017. Based on mathematical derivation/interpretation and the properties of Bitcoin at the end of 2017 (price $14,200), they find that:

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Are technical rules applied to pairs trading attractive after correcting for data snooping bias? In their March 2018 paper entitled “Pairs Trading, Technical Analysis and Data Snooping: Mean Reversion vs Momentum”, Ioannis Psaradellis, Jason Laws, Athanasios Pantelous and Georgios Sermpinis test a variety of technical trading rules for long-short trading of 15 commodity futures, equity indexes and currency pairs (all versus the U.S. dollar) frequently used on trading websites or offered by financial market firms. Specifically, they test 18,412 trend-following/momentum and contrarian/mean-reversion rules often applied by traders to past daily pair return spreads. They consider average excess (relative to short-term interest rate) return and Sharpe ratio as key metrics for rule selection and performance measurement. They use False Discovery Rate (FDR) to control for data snooping bias, such that 90% of the equally weighted best rules in FDR-corrected portfolios significantly outperform the benchmark. Most tests are in-sample. To test robustness of findings, they: (1) account for one-way trading frictions ranging from 0.02% to 0.05% across assets; (2) consider five subperiods to test consistency over time; and, (3) perform out-of-sample tests using the first part of each subperiod to select the best rules and roughly the last year to measure performance of these rules out-of-sample. Using daily prices of specified assets and daily short-term interest rates for selected currencies during January 1990 (except ethanol starts late March 2006) through mid-December 2016, they find that:

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What is the body of academic research on crypto-assets? In their March 2018 paper entitled “Cryptocurrencies as a Financial Asset: A Systematic Analysis”, Shaen Corbet, Brian Lucey, Andrew Urquhart and Larisa Yarovaya review available research on cryptocurrencies as financial assets. They define crypto-assets as peer-to-peer electronic transaction systems which allow payment by one party directly to another without an intermediary. Such assets are therefore infinitely divisible and have no physical representation or association with higher authority. Theses assets derive value from the security of an algorithm that records all transactions. The authors segment research into five areas: (1) bubble dynamics, (2) regulation, (3) cybercriminality, (4) diversification, and (5) market efficiency. Based on 87 papers published during 2013 through early 2018 (accelerating in frequency), they conclude that: Keep Reading

Does Bitcoin have a bright future, or is it only a transitional proof of concept? In their March 2018 paper entitled “Bitcoin: A Revolution?”, Guillaume Haeringer and Hanna Halaburda review incentive mechanisms that make Bitcoin work and discuss current and potential uses of Bitcoin and related technologies. They view Bitcoin as the first successful digital medium of exchange without a trusted third party based on combining cryptographic tools and incentive systems that prevent double-spending. They define a Bitcoin user as any entity holding or receiving Bitcoins and a Bitcoin miner as any entity recording and validating transactions. From the perspective of users, the Bitcoin system is similar to an online banking system that supports only deposits and transfers. From the perspective of miners, the Bitcoin system is a source of rewards from adding new blocks to the blockchain (the only source of new Bitcoins) and from transaction validation fees within their blocks. Based on the body of information about Bitcoin, they conclude that: Keep Reading

How do cryptocurrencies work, and how can investors acquire and hold them? In their January 2018 paper entitled “Crypto-Assets Unencrypted”, Seoyoung Kim, Atulya Sarin and Daljeet Virdi survey cryptocurrency history and technology. They summarize cryptocurrency market sizes, trading volumes and volatilities, with comparisons to major fiat currencies and commodities. They further discuss crypto-asset valuation, regulation and the mechanics of investing in them. Based on cryptocurrency data for January 2013 through early December 2017, with focus on the 20 largest cryptocurrencies, they find that: Keep Reading

Are simple moving averages (SMA) useful for timing difficult-to-value Bitcoin? In their January 2018 paper entitled “Bitcoin: Predictability and Profitability Via Technical Analysis”, Andrew Detzel, Hong Liu, Jack Strauss, Guofu Zhou and Yingzi Zhu investigate the use of 5-day, 10-day, 20-day, 50-day or 100-day SMAs to predict Bitcoin returns. Specifically, they test a trading strategy that holds Bitcoins (cash) when current Bitcoin price is above (below) a selected SMA. They assume cash earns the U.S. Treasury bill (T-bill) yield. Using daily Bitcoin prices and T-bill yield, along with data for other variables/assets for comparison, during July 18, 2010 through December 12, 2017, they find that: Keep Reading