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 their 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.
“The paper aims to study the risk-return profile of the CoinShares Gold and Cryptoassets Index (CGCI), which is the combination of a portfolio of crypto assets and gold. The authors consider a mixture of two kinds of models for both processes: Regime-Switching Intermediate-Trend, which governs the law of motion of the price at a weekly frequency, and Regime-Switching Volatility, which governs the movement of volatility at the daily frequency. The model is estimated using real data, and then using simulation (based on the estimated parameters) to evaluate the investment strategies in this asset class.”
“The paper seeks to determine the risk-reward profile of a portfolio of cryptoassets and gold. It places this in the context of the need to understand financial risk and the probability of "high-stress scenarios" including times of financial expansion and recession. The paper's specific aim, however, is the optimisation of investment portfolios, testing the resilience of the CoinShares Gold and Cryptoassets Index under different market regimes. This is achieved by simulating different market regimes using a sample of 1231 price observations from 2015-2020. The paper concludes that investors with a higher risk tolerance should look to add cryptoassets, and that those seeking protection against declining markets should invest in gold.”
“The paper addresses the problem of identifying regime switching in diversified, cryptoasset-containing portfolios. The authors consider separately and in combination a Gaussian Hidden Markov Model (HMM) and a Markov-switching GARCH model and apply it to test the resiliency of the CoinShares Gold and Cryptoassets Index. They find an overall stable risk-reward profile, a superior performance in terms of Omega ratio for investors that target wealth preservation and moderate annual returns, and underperformance regions in bear-low volatility market regimes.”
“The paper locates the research in a defined field and outlines previous relevant works, including how the models discussed have been used in previous studies.”
“The paper is well written, addresses an important area of for policy and markets, and explains and applies existing models used to extract market regimes.”
“The research question is of highly practical relevance, with frontier statistic tools.”
“The model misses an important piece: correlation… It is strange that the authors do not deal with ‘correlation’ between the crypto asset portfolio and gold prices. This is extremely important for the purpose of ‘diversification’ for financial investors. Think about recent COVID episode; I thought crypto assets and gold were moving together. Of course, you need to be careful in modeling the correlation. However, the authors could just evaluate their model by simulation by asking "what if the correlation is just 5%?"
Author Response: The time-series are simulated without specific correlation level for this particular study. The simulated sample includes pairs of crypto-gold time series with a 21-day rolling correlation that fluctuates between -0.5 to 0.5, with a few outliers as well. We believe that this adequately reflects the relationship between the two asset classes (as observed until now), where sustained significant correlations are not present, even if there are some significant short term correlations.
“I found the article to be heavy in discipline-specific jargon… It is speaking to people from a finance background.”
“It would be helpful to include a definition of resiliency and its relationship to diversification.”