Editor’s Letter

Understanding relationships and connections among asset returns is one of the most critical factors in constructing “optimal” portfolios. Since the appearance of the modern portfolio, asset allocators have primarily relied on various estimates of correlations and covariances among asset returns to measure those relationships. The problem could arise if asset allocators ignore the fact that correlation (i.e., Pearson version) is an accurate measure of association only if the relationship between two random variables is linear. The other problem that should be considered is that while the relationship between random returns may appear linear during specific periods, a regime change could lead to a breakdown, and the relationship could quickly become non-linear. For example, the negative relationship between changes in volatility (e.g., VIX) and equity returns could be almost linear during “normal” periods. But when there are significant adverse events, the relationship because of a non-linear decline in equity prices is associated with much more significant changes in volatility. The recent fluctuations in asset prices resulting from the invasion of Ukraine are good examples. This issue of JAI has a perspective by Ronald Lagnado and Nassim Taleb on the dangers of relying on correlation to construct a portfolio. The risk is that the estimated correlations will fail to capture the relationships among asset returns in the presence of tail events. Therefore, the tail risk may be ignored when constructing portfolios.

In “The Role of Cryptocurrencies in Investor Portfolios,” Megan Czasonis, Mark Kritzman, Baykan Pamir, and David Turkington focus on the role of cryptocurrencies in long-term investors’ portfolios. They explain that full-sample correlations reveal little about an asset class’s diversification potential. They do not distinguish upside correlations from downside correlations, nor do they consider the magnitude of returns. Correlations estimated using different windows length are likely to differ, reflecting that correlations are time-varying and there is significant noise in the short-term movement of asset prices. Thus, correlations estimated from shorter-interval returns do not necessarily reflect the co-movement of longer-interval returns. The authors argue that in evaluating the diversification potentials of cryptocurrencies, investors should consider the direction and magnitude of returns over the short term and the extent to which cryptocurrencies move synchronously or drift apart from other asset classes over the long term.

The article by Mieszko Mazur titled “Misperceptions of Bitcoin Volatility” documents that the perceived Bitcoin’s excessive volatility is essentially a misperception. Using recent data, the author finds that the realized volatility of bitcoin is lower than the volatility of approximately 900 stocks in the S&P1500 and roughly 190 stocks in the S&P500. If one considers Bitcoin as a single security rather than an asset class, its volatility will be in line with a typical listed security’s volatility. Given the market cap of Bitcoin, it is reasonable to argue that, at least for now, it should be treated as single security rather than an asset class. For example, the article reports that bitcoin return fluctuates less strongly than returns to publicly listed equity of Citigroup, Wells Fargo, Ford Motor, Goldman Sachs, Apple, Netflix, Twitter, and hundreds of others. In a separate test, the author analyzes bitcoin volatility during the March 2020 stock market crash triggered by COVID-19.

In “Understanding Oil Price Movement: Short versus Long Run Using the Leapfrog Model,” Yosef Bonaparte, Frank Fabozzi and David Koslowsky examine historical oil prices and report that the price of oil jumps from one state to another, remains stable for some time, and then jumps again to a new state, a phenomenon similar to a “leapfrog.” The recent events in the oil market appear to validate this observation. The article argues that there are different inferences for different time horizons: the shorter the time horizon, the greater the number of states the price may jump, the shorter the duration in each state, and the smaller the price jump. Motivated by these observations, the authors present a model to estimate each price state’s probability, price, and duration to predict oil price at a forward time horizon. They find that analyzing oil prices for different time horizons (weekly, monthly, and quarterly) conveys different inferences. The authors study the co-movement between real economic activity and oil prices and find it varies by time horizon, which has implications for measuring the economic significance of shocks to oil prices.

Takashi Kanamura analyzes the changes in the roles of crude oil futures in investment portfolios in the article titled “Role of Crude Oil Futures in Financial Portfolios under Financialization.” The author focuses on the financialization of oil markets and proposes a new dynamic market model between stock and crude oil futures prices and a new dynamic allocation model of optimal portfolios. Before crude oil financialization, the optimal positions of the S&P 500 varied with time. In contrast, those of WTI and Brent crude oil futures stayed almost at the same levels—implying the diversification effects of crude oil futures to financial assets. After the financialization, the optimal positions of crude oil, particularly WTI, futures decreased, indicating fewer diversification benefits from allocations to oil.

In “Does Rare Whisky Add Value in Multi-Asset Portfolios?” Lars Tegtmeier examines the importance of rare whisky as an alternative asset class, with particular attention to its price driving factors, risk-return characteristics, and diversification potential. These driving factors differ significantly from traditional and alternative assets, making rare whisky attractive as an investment. Because of its specific risk-return characteristics, rare whisky may qualify as a new asset class. While the size of the market is too small for most institutional investors, rare whisky could provide some diversification benefits for high net worth and family office portfolios. The author compares the specific risk-return characteristics of rare whisky other asset classes, demonstrating that the addition of rare whisky to an international multi-asset portfolio leads to statistically significant performance improvements for various investment strategies.

The article by Thomas Healey, Michael McDonald, and Thea Haley explores a niche alternative investment, litigation financing. In the article titled “Litigation Finance Investing: Alternative Investment Returns in the Presence of Information Asymmetry,” the authors report that the market for litigation finance has developed mainly since 2008, and as with many types of private funds, data on investments and performance are not generally available to the public. Using data collected by Morning Investments, the authors find that in-sample returns of this investment have been more than 20% above treasuries annually with little correlation to other investment areas. This apparent excess return may be due to information asymmetry and barriers to entry into the space. Their findings highlight the opportunities and risks for investors in this nascent asset class and suggest such excess returns may not disappear because markets for this asset class are not efficient.

In “Dynamic Hedge Fund Portfolio Construction: Exponentially Weighted Returns Approach,” Wei Kuang proposes an exponentially weighted returns approach for constructing portfolios of hedge funds. The exponentially weighted returns approach, which gives more weight to recent observations, allows for volatility and higher moment dynamics in portfolio construction. Using monthly hedge fund index returns for January 1990 to December 2020, the author finds that the proposed approach significantly improves portfolio performance in terms of enhanced returns, reduced risk, and improved risk-adjusted returns compared to the benchmark equally weighted approach. However, the exponentially weighted method generally requires more frequent portfolio rebalancing to capture the return distribution dynamics, and the turnover varies across different portfolio optimization models. Therefore, the strategy shows real promise when applied to liquid alternative assets and replication products.

Hossein Kazemi

Editor-in-Chief

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