Traditional Asset Classes Research Articles

Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets

In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coefficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coefficients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.

Does Factor Investing Improve Investor Welfare? A Multi-Asset Perspective

Investment decisions in delegated money management are typically made in two stages. In the first stage, a centralized decision maker allocates capital to the different asset classes. In the second stage, asset managers decide how to allocate available wealth to the individual securities within the corresponding asset class. Although commonly used in investment practice, this approach potentially involves a loss of efficiency compared with a single-step optimization procedure. The authors show that this welfare loss is zero if, and only if, the benchmarks completely explain the cross section of expected returns of the securities. This theoretical proposition justifies factor investing as an investment method that constructs investable proxies for pricing factors. Mean-variance spanning tests confirm that investment opportunities offered by standard equity, bond, and commodity factor benchmarks are not spanned by corresponding broad asset class benchmarks. The authors then conduct out-of-sample tests with these factors and find that they improve the risk-return profile of multiclass portfolios with respect to the case in which these portfolios are invested in broad asset class benchmarks. TOPICS:Wealth management, factor-based models, portfolio theory, portfolio construction Key Findings • A set of benchmark portfolios made of individual securities offers the same investment opportunities as these securities if, and only if, their returns are asset pricing factors, meaning that they collectively explain differences in expected returns. • Mean–variance spanning tests lead the authors to reject the hypothesis that traditional broad equity, bond, and commodity asset class benchmarks generate the same efficient frontier as factor benchmarks drawn from these three classes. • Out of sample, introducing factor benchmarks in multi-asset portfolios that contain traditional asset class benchmarks improves their Sharpe ratio.

Art as a Financial Investment

The comparatively poor performance of traditional asset classes in recent years has driven the search for greater returns via alternative asset classes. The desire to reap higher risk adjusted returns from diversification into assets which offer low and even negative correlation with equities and bonds is extremely desirable. There has been a huge growth in the traditional alternative investments such as real estate, commodity futures, private equity and hedge fund investments. Additionally, a number of funds specialising in art have recently emerged. These also appear to offer a highly beneficial diversification strategy with extremely low correlation with traditional asset classes. It is important for investors to understand the risk and return characteristics of this new alternative asset class. In this paper we take a closer look at art as an alternative asset, and look specifically at how this new alternative asset is expected to perform, also during bear markets, when the benefits of diversification are most needed. We look at the risk and return characteristics of art using art market indices, and the prospects for portfolio diversification in the art market using a variety of data across art market sectors, including the Old Master, European Impressionist, Modern and Contemporary art markets. Due to the low correlation of art with other asset classes, we find opportunities for portfolio diversification across art markets and across asset classes. The results hold, even allowing for the high transaction costs, which are encountered when trading art, when spread over a longer time horizon.

Do Well And Do Good: Impact Investing For Endowments

As the calls for responsible investing grow, universities will be asked about their endowments’ investment policies regarding impact investing, wherein endowments seek opportunities that provide investment returns and social impact. A common concern for the investment committees, however, is how to incorporate impact investments without compromising the goal of maximizing asset values and the benefit to its university. This paper offers a way forward. Using standard portfolio optimization models and a broader way to consider return, this paper suggests that an endowment can still meet the goal of maximizing expected value while investing with a purpose. It also offers examples of how to incorporate impact investing opportunities throughout the traditional asset classes in an endowment’s portfolio. The paper also addresses some reasons why endowments have been hesitant to incorporate impact investments. While other papers have addressed aspects of impact investing, this paper seeks to offer an accessible and holistic approach to the topic.

Cross-Asset Style Premia Asset Allocation Process

We aim to compare different allocation models to build a portfolio that includes a popular set of alternative risk premia, common to most traditional asset classes. Firstly, we review alternative risk premia, mainly Carry, Value and Momentum, then we create sub-styles and styles portfolios. On each asset class we try to compare in a unified framework different style's definitions, to assess how each choice affects the outcome. Finally we aggregate styles in a composite portfolio. All these steps require several decisions on whether and which risk targeting method to utilize and which allocation model to adopt. We show the main differences and consequences of each decision and how they may affect the final portfolio. Lastly, we cluster different solutions according to a dissimilarity criteria, determining which are the key steps that make strategies actually different from one another.

Performance Dispersion Risk Assessment in Alternatives and Active Strategies

Alternatives have gained significant investor attention recently. Unfortunately, the lack of a holistic investment framework to incorporate alternatives in a traditional portfolio poses a challenge. Traditional risk–return based approaches, alone, over allocate to alternatives—a result of underestimation of risks resulting from (a) significant manager dispersion and (b) smoothness in the pooled return indexes used for alternatives. Much research corrects for smoothness of returns; however, the risk from performance dispersion remains mostly unaddressed. In this study, the author discusses a methodology to quantify performance dispersion risk and incorporate it in the portfolio construction process. TOPICS:Portfolio management/multi-asset allocation, real assets/alternative investments/private equity, private equity, performance measurement Key Findings • Alternative investments exhibit a much higher degree of performance dispersion among managers than traditional asset classes. • Traditional risk measurement approaches on pooled return indices utilized for alternatives underestimate the actual risk of an investment in alternative asset classes. • Performance dispersion risk of investing in an alternative strategy can be measured similar to the idiosyncratic risk of a stock and should be included in any risk-based portfolio construction process.

Forex Carry, Value, & Momentum Factors Boosted by Sentiment

Currency factors, such as momentum, carry and value, are well-known candidates for alternative risk premia strategies. Benefiting from low correlations with traditional asset classes, FX factors can provide portfolio diversification advantages, while delivering idiosyncratic returns. In this paper, we construct the basic currency factors and apply performance enhancements using real-time sentiment analytics from RavenPack. We construct cross-sectional, dollar-neutral long-short portfolios as our benchmarks for carry, value, and momentum factors based on seven major currencies: AUD, CAD, CHF, EUR, GBP, JPY, NZD. By tilting the weights of a benchmark factor based on economic news sentiment, we are able to enhance the performance that is shown to be robust across all factors and periods. A composite portfolio that equally invests in three enhanced factors delivers an Annualised Return of 210 bps with an Information Ratio (IR) of 0.70, over 3x better risk-adjusted returns than the benchmark strategy. Moreover, hedging the sentiment enhanced portfolio by shorting the benchmark against it yields an Annualised Return of 156 bps with an IR of 1.05.

Connectedness of cryptocurrencies and gold returns: Evidence from frequency-dependent quantile regressions

This paper explores the symmetric and asymmetric dependency structure of decomposed return series of Gold and eight cryptocurrencies to establish the hedging and diversification potentials of these asset classes. Daily data spanning 30 April 2013 to 18 April 2019 are employed within the Ensemble Empirical Mode Decomposition and Quantile-in-Quantile regression techniques. Our empirical results provide evidence that cryptocurrencies and Gold can both hedge and diversify for each other at different conditional distributions of their returns. We also find that cryptocurrencies are not purely speculative but can be driven by medium- and long-term fundamentals. In addition, both Gold and cryptocurrencies can be hedge and diversifiers for other traditional asset classes such as crude oil, fiat currencies, and other commodities.

Cryptocurrencies: applications and investment opportunities

PurposeThis study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios.Design/methodology/approachThe mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios.FindingsThe authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios.Originality/valueThis is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.

Return and volatility spillovers between Bitcoin and other asset classes in Turkey

Purpose With a substantial return and volatility characteristic of Bitcoin, which may be seen as a new category of investment assets, better understanding of the nature of return and volatility spillover can help investors and regulators in achieving the potential goal from portfolio diversification. The paper aims to discuss these issues. Design/methodology/approach This paper explores the return and volatility transmission between the Bitcoin, as the largest cryptocurrency, and other traditional asset classes, namely stock, bond and currencies from the standpoint of Turkey over the period July, 2010–June, 2018 using the newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation. Findings The empirical results reveal the existence of the positive unilateral return spillovers from the bond market to Bitcoin market. Regarding the results of shock and volatility spillovers, there exists strong evidence of bidirectional cross-market shock and volatility spillover effects between Bitcoin and all other financial asset classes, except US Dollar exchange rate. Originality/value The important extention is the adoption of a newly developed multivariate econometric technique, VAR–GARCH, in mean framework with the BEKK representation, proposed by Engle and Kroner (1995), which is employed for the first time specifically to examine the extent of integration in terms of volatility and return between Bitcoin and key asset classes. Second, Bitcoin has experienced a rapid growth since around a decade and a number of investors are showing interest in its potential as an integrative part of portfolio diversification. The information provided by empirical results gives empirical bases from which to address topics concerning hedging purposes and optimal portfolio allocation. It is also increasingly important to analyze the current behavior of Bitcoin in relation to other assets to provide policy makers and regulatory bodies with guidance on the role of the Bitcoin as an investment asset in Turkey. Thus, this is the first serious attempt at exploring the potential for Bitcoin to offer diversification opportunities in the context of Turkey.

Crypto-assets: Crossing the chasm?

The challenge crypto-assets face for an effective breakthrough is this: How can an infrastructural safe and efficient environment be put in place whereby the broader investor community feels sufficiently comfortable, in a similar manner to other more traditional asset classes such as equities, exchange-traded funds and bonds? Remarkably, the novelty of crypto-assets lies in the distributed nature of the blockchain-based networks they use rather than their investment characteristics, many of which they share many with traditional financial assets. Central securities depositories (CSDs) as infrastructures of the financial markets — and rightfully so — have them on their radar screen.

Investments in Cryptocurrencies: Handle with Care!

Asset pricing models and investment styles have been researched intensively in equities, bonds, FX, and commodities. However, a new asset class has emerged since the end of 2008, namely, cryptocurrencies such as Bitcoin and Ethereum, among others. The author uses an extensive data set of over 1,500 cryptocurrencies and shows that almost none of the traditional investment styles such as momentum or defensive appear to be successful in this young asset class. Cryptocurrencies are also independent from the macroeconomic environment and cannot be explained by a standard asset pricing model. A cryptocurrency specific model yields clearly better results. In addition, the whole cryptocurrency space is dominated by only a few individual digital coins. Equally weighted mean monthly returns appear to be random with low or even no correlation with traditional asset classes such as US equities and global FX. TOPICS:Real assets/alternative investments/private equity, performance measurement, portfolio management/multi-asset allocation

Analysis on the Risk Return Profile of Alternative Assets Under Reference Portfolio Concept

This paper provides the methodology of estimating the risk-return relationship of alternative asset investments within the mean-variance framework. While conducting strategic asset allocation, most of the institutional investors do not take into account the risk-return relationship of alternative assets, or use arbitrary policy numbers that do not properly reflect the characteristics of alternative assets. This paper borrows the concept of reference portfolio in developing the methodology of estimating the risk-return relationship of alternative investments. The reference portfolio is the benchmark portfolio used in strategic asset allocation by pension funds. This can serve as the opportunity costs of alternative investments. We use the realized IRR’s from actual investments, and estimate the risk-return characteristics of alternative investments. We find that by properly estimating the mapping relationship between the reference portfolio and alternative asset classes, we can incorporate the risk-return profile of these non-market assets within the mean-variance framework together with the other traditional asset classes.

The evolution of Belgium REITs

PurposeThe Belgium Real Estate Investment Trust (REIT) market was created primarily to facilitate a transparent, professionally managed and fiscally efficient market, providing access to the European property markets. Being the 2nd oldest REIT market in Europe, it has undergone many evolutionary changes over the years that add to its considerable stature as a sophisticated investment opportunity. This includes an increased recent focus on the social infrastructure property sectors such as healthcare, care facilities and nursing homes, consistent with the evolving investment mandates requiring stronger integration of environmental, social and governance (ESG) aspects in the investment strategy formulation. The purpose of this paper is to highlight the strategic transformation of Belgium REITs and empirically assess their performance attributes over 1995–2018. Sub-period performance dynamics of Belgium REITs in the pre-global financial crises (GFC) (1995–2007) and post-GFC (2009–2018) contexts are provided.Design/methodology/approachIn total, 23-year monthly total returns over 1995–2018 were used to analyse the risk-adjusted performance and portfolio diversification potential of Belgium REITs. The traditional mean-variance portfolio optimisation model using the ex-post returns, risk and correlation coefficient of Belgium REITs and other financial assets was developed to determine the added-value benefits of Belgium REITs in a diversified investment framework. The analysis was further extended to cover the sub-periods of pre-GFC (1995–2007) and post-GFC (2009–2018).FindingsThe results of the analysis provide a strong investment case for Belgium REITs, as they are able to deliver a discernible premium in the total return performance, superior risk-adjusted returns and strong diversification benefits with the stock market in a long-term investment horizon. Broadly consistent results are similarly observed in the sub-period analysis over varying market conditions. Importantly, the role of Belgium REITs in a diversified investment framework was also empirically validated, as they enhanced the mixed-asset portfolio performance comprised of the traditional asset classes of stocks and bonds across a broad portfolio risk-return spectrum. Dividend yield was also found to be a key component of the financial performance of Belgium REITs.Practical implicationsThe Belgium REIT market has evolved to become the 5th largest market in Europe by the capitalisation volume. This is mainly due to the robust regulatory support and innovations since its debut which have resulted in a polished framework that is both supportive and attractive to financial players and investors. The broad direct consequence of this paper is to highlight the performance attributes of Belgium REITs, adding clarity to the ongoing discussion regarding the viability of European REITs as a liquid and tax transparent route for institutional investors to obtain their property exposure. The strong dividend yield and ESG/social infrastructure focus of Belgium REITs sees Belgium REITs well-placed going forward to meet the evolving investment mandates from major investors.Originality/valueThis paper is the first empirical investigation that elucidates the risk-adjusted performance and role of Belgium REITs as an important property investment opportunity. It equips investors and practitioners with an independent and comprehensive empirical validation of the strategic role of Belgium REITs in a portfolio. Well-informed and practical property investment decision making regarding the use of Belgium REITs for access to the property asset class is the main outcome of this paper.

Quality in the Selection of REITs

Quality real estate has historically offered an attractive return, but that return sits behind high barriers to entry. Physical property is costly to own and illiquid to trade. Publicly listed real estate investment trusts (REITs) were conceived as a means for shareholders to own property in a more divisible and tradable way. Over time, REIT shares have grown into a more than trillion dollar market. Historically, REIT shares have tracked long term real estate returns while also outperforming the stock market. They have combined bond-like regular payouts with the capital appreciation aspects of ordinary stocks. As such, they offer a long-term diversification benefit versus other traditional asset classes. However, REIT investing comes with two specific risk characteristics. One: an asymmetric risk profile, i.e. stable revenue and an absence of windfall profit possibilities limit the reward for taking incremental risk. Two: a high debt burden relative to companies in other sectors. In this paper, we investigate quality-based characteristics that can improve the risk-return profile of REIT investing. Specifically, we study portfolios of REITs formed based on four characteristics: profitability, leverage, valuation and yield. We find that a portfolio that removes low quality REITs has historically outperformed the broad REIT benchmark. We highlight an approach of using these four characteristics to build a broad, statistically robust REIT portfolio, with each characteristic being additive to the REIT selection process. Importantly, the approach is based on fundamental real estate investment principles.

Do cryptocurrencies and traditional asset classes influence each other?

This paper studies the asymmetric transmission mechanisms of shocks between the most liquid representatives of traditional asset classes, including commodities, foreign exchange, stocks and financials, and cryptocurrencies, represented by Bitcoin. Our results suggest that the unconditional connectedness between cryptocurrencies and traditional assets is negligible. However, conditional analysis uncovers periods of substantial shock transmission between Bitcoin and traditional assets. This finding undermines the potential of Bitcoin as a hedge to traditional assets and shows that market disruptions can spread from Bitcoin to the traditional economy. The increasing market capitalization of cryptocurrencies further strengthens the importance of such findings.

Quantifying Endogeneity of Cryptocurrency Markets

We construct a ‘reflexivity’ index to measure the activity generated endogenously within a market for cryptocurrencies. For this purpose, we fit a univariate self-exciting Hawkes process with two classes of parametric kernels to high-frequency trading data. A parsimonious model of both endogenous and exogenous dynamics enables a direct comparison with exchanges for traditional asset classes, in terms of identified branching ratios. We also formulate a ‘Hawkes disorder problem,’ as generalization of the established Poisson disorder problem, and provide a simulation-based approach to determining an optimal observation horizon. Our analysis suggests that Bitcoin mid-price dynamics feature long-memory properties, well explained by the power-law kernel, at a level of criticality similar to fiat-currency markets.

Asset Allocation vs. Factor Allocation—Can We Build a Unified Method?

There is increasing interest in the idea of allocating across factors instead of across traditional asset classes. Allocating across factors has the intuitive appeal of allocating across building blocks that are in theory purer sources of return. In practice, factor-based allocation is not easy: Factors are unobservable and must be specified. However, the authors believe there is merit in integrating insights from factors with traditional asset allocation. Information and views about factors and asset classes can be a powerful combination. In this article, the authors present a framework for combining the two paradigms in an innovative way, resulting in optimal allocations that blend insights from both paradigms. Specifically, their approach derives asset class return prediction from factor-based asset allocation, which allows construction of portfolios for various investment objectives from a unified framework.

Real Natural Assets: The Real Green Investment Alternative

The authors present an analysis of green investment performance grouped into two broad categories: non-real green assets and real green assets. They seek to identify which green investment alternatives offer greater financial benefit and are thus more attractive to investors and investigate some of the intrinsic risks associated with this asset class. The performance analysis is based on the study of the expected returns, volatility, diversification potential, downside risk, association with inflation, and exposure to liquidity shocks in stock markets of real and non-real green assets, comparing them with those of traditional asset classes (such as equity, bonds, and real estate) and other non-traditional assets, namely infrastructure. The findings indicate that real green assets, in particular, may represent the appropriate alternative in the adoption of green investments.

Modelling the Government Sukuk Market Volatility Evidence from Malaysia

The financial crises caused a collapse in prices of most asset classes, fueling interest in alternatives to traditional asset classes that might be less affected by large market gyrations such as Sukuk, fast growing but often misunderstood market. In this light, we investigate the statistical properties of the Thomson Reuters BPAM Government Sukuk Index and explore its volatility with their conterparts the Thomson Reuters BPAM government bond index. We found that the TR BPMA Government Sukuk index shares all stylized facts of traditional asset classes, and estimations results are also in line with prevalent findings in the literature. As we detected heteroscedasticity effect, we modeled the returns both of the TR BPMA Government Sukuk and its conventional counterpart, and we compared their volatility using the GARCH model. The results show that the volatility is more persistent for the government Sukuk to its conventional counterpart. But, the Government Sukuk index was slightly less volatile than the Government Bond index at a long run. The study also provides evidence for the existence of the asymmetric effect (leverage) captured by the parameter of EGARCH (1, 1) model. It shows that negative shocks have significant effect on conditional variance (volatility) for the Government Sukuk. Which means that bad news had better impact on volatility than good news in this market. It indicate also that the Malaysian Government Sukuk market moves to more efficient condition.