In recent years, Portfolio Insurance (PI) strategies evolved thanks to a reduction in trading costs and a growth of asset classes, such as credit and commodities. That’s why we applied the Option Based Portfolio Insurance (OBPI) and the Constant Proportion Portfolio Insurance (CPPI) to an emerging asset class, namely Cryptocurrencies. The main drawback of the latter is the high volatility along with the downside risk; nonetheless, we can benefit from the performance it guarantees in bullish markets. Thus, the aim is to exhibit the PI investment strategies we designed on a cryptocurrency index; these are able to capture positive returns and, at the same time, limit the risk of drawdowns and random price jumps.
In recent years, the world of cryptocurrencies has evolved at a very fast pace. To date, cryptocurrency is one of the most active markets, where high trading volumes occur on a daily basis. This is because the investment in this new asset class was seen by many as an opportunity to obtain high returns in a short time. This has clearly led to a market characterized by excessive volatility and, to date, the main problem for investors investing in cryptocurrencies is, in fact, the risk of losing a considerable part of their investment.
However, since the crypto market is still very interesting and offers great opportunities for the entire economic system, it is necessary to find a way to invest more safely in order to be able to support the growth of this new asset but avoid having the capital dried up. In this scenario, portfolio insurance strategies can prove to be a truly fundamental tool.
Portfolio Insurance strategies, indeed, have the goal of generating a portfolio that guarantees participation in the performance in bull markets but also limit the downside risk during bearish periods. In this sense, two well-known methods in the literature are the Option Based Portfolio Insurance (OBPI) and the Constant Proportion Portfolio Insurance (CPPI).
We back-tested the different strategies and we have done a Monte Carlo simulation with a GARCH model to estimate volatility. In other words, we simulate the performances of the PI strategies, with a focus on the extension of the conventional ones, and we see whether the results are consistent with what happened in the past or totally opposite scenarios arise.
We go on by examining the empirical results and, here, a backtesting analysis will be conducted as this will make us begin to understand how, on a scenario that has already occurred, the strategies operate in comparison to a 60/40 Portfolio, understanding whether or not there might actually be an advantage in implementing these types of strategies to an asset such as that of cryptocurrencies. Then, we include a theoretical stress-test to highlight the robustness of our portfolios in extreme market conditions. When we illustrate the results, we rely on an investment of $100,000, using a cryptocurrency index, the Crypto 200 Index by Solacti (CMC200), as the risky asset. The comparison will be made both by looking at the past and by trying to understand how the strategies could behave in the future with a Geometric Brownian Motion model.
We emphasize the importance of conducting further research on PI strategies in a more innovative perspective and the added value that these extensions to PI for a Crypto-assets index have brought in a field as modern as it is uncertain as that of cryptocurrencies.