Currency Trading

Do crypto-asset trading volumes usefully predict returns? In the August 2018 draft of their paper entitled “Trading Volume in Cryptocurrency Markets”, Daniele Bianchi and Alexander Dickerson investigate the power of crypto-asset trading volumes to predict future returns. They calculate volumes and returns based on either 12-hour or 24-hour intervals. They process these inputs as follows:

• To detect volume abnormalities, they estimate its log deviation from trend over a rolling 21-interval window. To put different crypto-assets on an equal footing, they then standardized by dividing by its log standard deviation over the same window.
• They measure past returns over the same interval, denominated in bitcoins, (thereby including Bitcoin only indirectly). To emphasize the most liquid exchanges, they weight returns by volume when aggregating.

To assess economic significance of findings, they double-sort crypto-assets first into two to four groups ranked by the return metric and then within each group into three or four subgroups ranked by the volume metric. Using intraday (10-minute) price and volume data for 26 crypto-assets from over 150 exchanges (90% of total crypto-asset market capitalization), each denominated in bitcoins, during January 1, 2017 through May 10, 2018, they find that:

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How do the major crypto-assets (Bitcoin, Ripple, and Ethereum) stack up against conventional asset classes? In their August 2018 paper entitled “Risks and Returns of Cryptocurrency”, Yukun Liu and Aleh Tsyvinski apply standard tools of asset pricing to measure crypto-asset exposures to:

• 160 equity factors.
• Macroeconomic factors (non-durable consumption growth, durable consumption growth, industrial production growth, and personal income growth).
• Major non-U.S. currencies (Australian Dollar, Canadian Dollar, Euro, Singapore Dollar and UK Pound).
• Precious metals (gold, platinum and silver).

They also investigate potential predictors for cryptocurrency returns analogous to those of traditional asset classes (momentum, investor attention, price-to-“dividend” ratio, realized volatility and supply). Finally, they measure exposures of various industries to crypto-asset returns. Using daily crypto-asset prices for Bitcoin since January 2011 and for Ripple and Ethereum since early August 2013, all through May 2018, along with contemporaneous data for other variables as outlined above, they find that: Keep Reading

Should investors consider Bitcoin as a safe haven from turbulent financial markets? In their June 2018 paper entitled “Bitcoin as a Safe Haven: Is It Even Worth Considering?”, Lee Smales and Dirk Baur assess the potential for Bitcoin as a safe haven, focusing on considerations beyond its low return correlations with other assets during times of market stress. Their comparison set of assets consists of gold (GLD) and bonds (10-year U.S. Treasury futures) as traditional safe havens, a proxy for the U.S. stock index (SPY) and mature (Apple) and immature (Twitter) individual stocks. They match samples by removing Bitcoin data for weekends and holidays. Using daily returns for Bitcoin and the comparison set of assets during August 2011 through May 2018, they find that: Keep Reading

Should investors pursue initial coin offerings (ICO), special-purpose crypto-tokens? In their May 2018 paper entitled “Digital Tulips? Returns to Investors in Initial Coin Offerings”, Hugo Benedetti and Leonard Kostovetsky study the market for crypto-tokens, focusing on: initial pricing; returns from buying at ICO and selling at date of listing on an exchange; and, returns from buying at listing date and holding for various fixed intervals. ICOs typically originate with an offeror’s prospectus detailing a goal, plan, team and offering schedule. Interested parties then register for the offering, with execution typically in stages over several months, some restricted to preferred users, angel investors, venture capitalists and/or accredited investors. The authors also employ Twitter accounts of ICO offerors to test the relationship between Twitter activity and price and to measure post-ICO attrition rate of offerors. Using data for 2,390 ICOs completed by May 2018, including offeror Twitter histories as of May 8, 2018, they find that:

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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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