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Koutsouri A, Petch M, Knottenbelt W, 2021, Performance of the CoinShares Gold and Cryptoassets Index under different market regimes, Cryptoeconomic Systems, Vol: 1
Regime-switching models are frequently used to explain the tendency of financial markets to change their behavior, often abruptly. Such changes usually translate to structural breaks in the average means and volatilities of financial indicators, and partition their time-series into distinct segments, each with unique statistical properties. In this paper, we address the problem of identifying the presence of such regimes in the constituents of diversified, cryptoasset-containing portfolios, ultimately to define high-risk market conditions and assess portfolio resilience. For each portfolio component, we first consider a Gaussian Hidden Markov Model (HMM) in order to extract intermediate trend-related states, conditional on the weekly returns distributions. We further apply a Markov-switching GARCH model to the demeaned daily returns to describe changes in the conditional variance dynamics and isolate volatility-related states. We combine the former approaches to generate a number of price paths for each constituent, simulate the portfolio allocation strategy and obtain a risk profile for each combination of the trend and volatility regimes. We apply the proposed method to the CoinShares Gold and Cryptoassets Index, a diversified, monthly-rebalanced index which includes two main risk-weighted components; a cryptoassets basket and physical gold. Results demonstrate an overall stable risk-reward profile when compared against the individual components and suggest a superior performance in terms of Omega ratio for investors that target wealth preservation and moderate annual returns. We detect underperformance regions in bear-low volatility market regimes, where diversification is hindered.
Koutsouri A, Petch M, Knottenbelt W, 2021, Diversification benefits of commodities for cryptoasset portfolios, 2021 International Conference on Blockchain and Cryptocurrency (ICBC 2021), Publisher: IEEE, Pages: 1-9
The aim for balance between risk and reward in investment portfolios often requires studying the diversification contribution of its constituents. This objective requires to specify whether investors can extend their exposure in certain asset classes and benefit their portfolios in a statistically significant way. In this paper, we address this issue of diversification in the context of cryptoasset portfolios and examine whether their risk-adjusted performance can be enhanced through seeking exposure into the commodities class. For an equally-weighted portfolio of five cryptoassets, we first consider the addition of physical gold, as conceptualised by the CoinShares Gold and Cryptoassets Index, a diversified, monthly-rebalanced index that seeks exposure to both asset classes. We further consider modifying the index composition by replacing physical gold with a basket of five commodities. Mean-variance spanning tests reveal that the addition of physical gold in the original cryptoasset portfolio translates to a significant shift in the efficient frontier, both in terms of the global minimum variance and the tangency portfolios. Additionally, expanding the exposure in the commodity side confirms a statistically significant improvement, with the diversification benefit arising from a shift in the tangency portfolio. We further generate a number of price paths for the original index, the modified index and their components, according to a Dynamic Conditional Correlation GARCH specification, to assess the efficiency of the index weighted risk contribution scheme. Results demonstrate a superior performance of the two indices when compared against their constituents in terms of Omega ratio. The modified index appears more appropriate for investors that seek higher annual returns, while the original composition would be more appropriate for individuals with mod
Koutsouri A, Knottenbelt WJ, 2020, Stress Testing Diversified Portfolios: The Case of the CoinShares Gold and Cryptoassets Index, 2nd International Conference on Mathematical Research for Blockchain Economy, Publisher: Springer Verlag
Stress testing involves the use of simulation to assess the resilience of investment portfolios to changes in market regimes and extreme events. The quality of stress testing is a function of the realism of the market models employed, as well as the strategy used to determine the set of simulated scenarios. In this paper, we consider both of these parameters in the context of diversified portfolios, with a focus on the emerging class of cryptoasset-containing portfolios. Our analysis begins with univariate modelling of individual risk factors using ARMA and GJR--GARCH processes. Extreme Value Theory is applied to the tails of the standardised residuals distributions in order to account for extreme outcomes accurately. Next, we consider a family of copulas to represent the dependence structure of the individual risk factors. Finally, we combine the former approaches to generate a number of plausibility-constrained scenarios of interest, and simulate them to obtain a risk profile. We apply our methodology to the CoinShares Gold and Cryptoassets Index, a monthly-rebalanced index which comprises two baskets of risk-weighted assets: one containing gold and one containing cryptoassets. We demonstrate a superior risk-return profile as compared to investments in a traditional market-cap-weighted cryptoasset index.
Koutsouri A, Poli F, Alfieri E, et al., 2020, Balancing Cryptoassets and Gold: A Weighted-Risk-Contribution Index for the Alternative Asset Space, 1st International Conference on Mathematical Research for Blockchain Economy, Publisher: Springer Verlag, Pages: 217-232, ISSN: 0302-9743
Bitcoin is foremost amongst the emerging asset class knownas cryptoassets. Two noteworthy characteristics of the returns of non-stablecoin cryptoassets are their high volatility, which brings with it ahigh level of risk, and their high intraclass correlation, which limits thebenefits that can be had by diversifying across multiple cryptoassets. Yetcryptoassets exhibit no correlation with gold, a highly-liquid yet scarceasset which has proved to function as a safe haven during crises affectingtraditional financial systems. As exemplified by Shannon’s Demon, a lackof correlation between assets opens the door to principled risk controlthrough so-called volatility harvesting involving periodic rebalancing.In this paper we propose an index which combines a basket of five cryp-toassets with an investment in gold in a way that aims to improve therisk profile of the resulting portfolio while preserving its independencefrom mainstream financial asset classes such as stocks, bonds and fiatcurrencies. We generalise the theory of Equal Risk Contribution to allowfor weighting according to a desired level of contribution to volatility. Wefind a crypto–gold weighting based on Weighted Risk Contribution to behistorically more effective in terms of Sharpe Ratio than several alterna-tive asset allocation strategies including Shannon’s Demon. Within thecrypto-basket, whose constituents are selected and rebalanced monthly,we find an Equal Weighting scheme to be more effective in terms of thesame metric than a market capitalisation weighting.
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