Ever since its birth nearly fifty years ago, when Jack Bogle created the first index mutual fund, the passive investment industry has enjoyed a spectacular growth, with passive investment expected to overtake active investment within the next few years.[1] The rapid development of passive investing has been to a large extend facilitated by the introduction of exchange traded funds (ETFs) as complements and substitutes to index funds. The growth of these investment vehicles has been particularly impressive over the last 20 years, with a number of ETFs worldwide growing from 276 in 2003[2] to 9,000+ globally in 2022 for a total AuM estimated at 9 trillion U.S. dollars, growing at a 11.8% CAGR since 2014 according to Trackinsight data. Chart 1: Expansion of the ETF landscape since 2014 in number of listed ETFs per year, split between passive and active strategies A stark contrast exists between the importance and significance of ETFs as the fastest growing segment of the investment management industry and the relative low level of sophistication of investment portfolio strategies involving ETFs. This disconnect underlines the critical need to frame ETF investment decisions within the modern paradigm of portfolio management. The latter is geared towards helping investors achieve an efficient spending of their limited dollar and risk budgets while respecting many constraints. It not only relates to financial risk exposures but also to a growing number of non-financial risk exposures.  Against this backdrop, this paper provides a brief discussion of how the analytical tools routinely used to measure portfolios’ exposure to financial and extra-financial risk factors can be utilized to the analysis of ETF portfolios’ risk exposures, subject to suitable adjustments. Given space constraints, we do not attempt to offer a thorough and exhaustive discussion of risk models for ETF portfolios but rather present a couple of illustrations emphasizing of how sophisticated risk analysis can help build more efficient ETF portfolios. The first illustration has a focus on financial risks, while the second illustration has a focus on extra-financial risk, and more specifically on climate-transition risks.  Measuring and Managing Financial Risk Exposures for individual ETFs – A Focus on Rewarded Risk Factors  Any active or passive portfolio can be regarded as a bundle of factor exposures. It is of critical importance for investors in ETFs to measure the exposure of their portfolios to such risk factors. There is a key distinction to be made between exposure to rewarded factors, which can be desirable since these factors are associated with a premium, and exposure to unrewarded factors, which should be minimized in the long-run.[3]   Transporting the analysis at the level of an ETF or ETF portfolio involves two main questions:  Whether the portfolio is materially exposed to rewarded factors Whether this exposure is well-diversified  With respect to the first aspect, a challenge specific to ETF portfolios is that the risk analysis should be performed not in absolute terms, but in relative terms with respect to the benchmark. Indeed, most of the risk exposure of any given ETF inevitably comes from the long-only market exposure, which typically does not vary significantly across ETFs. The more relevant dimension, where cross-sectional differences are expected to materialize, related to the residual exposure to rewarded factors once market exposure has been controlled for. With respect to the second aspect, namely the analysis of the quality of diversification, recent research has shown that simple measures such as effective number of constituents can be severely misleading (see for example Martellini and Milhau (2018)) in the sense that even a well-diversified portfolio of constituents can indeed hide a very concentrated portfolio of factor exposures. Again, to be relevant for ETF investors the analysis will have to be transported in a relative risk context. Such an analysis of the quantity and quality of rewarded relative factor exposures has precisely been proposed in a recent paper by Herzog, Jones, and Safaee (2023), where they analyze the level of risk diversification for a universe of 862 US equity ETFs from the Trackinsight database, based on weekly total returns data over the five-year period ranging from October 2017 to September 2022. Using Instrumented Principal Components Analysis (Kelly, Pruitt and Su (2019)) to build a risk model that reconciles time-series/beta-based approaches with cross-sectional/fundamental approaches (Vaucher (2023)), they decompose relative risk into individual factor-related risk contributions. They use this decomposition to obtain for each ETFs a measure of factor exposure concentration based on the effective number of relative risk contributions transformed into a suitable ratio called Concentration Risk Measure (CRM).[4] Chart 2: Example of risk contributions over a 3-year period for a Russell 1000 Value ETF using the methodology outlined by Vaucher (2023) in The Scientific Portfolio Risk Model They then use this concentration measure to analyze the impact of diversification on tracking error stability and find conclusive empirical evidence that US equity ETFs that have a diversified set of systematic active risk contributions have a more stable tracking error. These results have important practical implications for ETF investors since they suggest that investors seeking a stable tracking error for active risk budgeting purposes may benefit from selecting those ETFs that have a strong level of risk diversification. Measuring and Managing Extra-Financial Risk Exposures for ETF Portfolios – A Focus on Climate Transition Risks  While an ongoing debate exists with respect to whether increasing the exposure of a portfolio to non-financial risks as proxied by ESG scores should have a positive, negative or zero impact on the portfolio expected return (see for example Coqueret (2022)), the relevance and importance of properly measuring and managing such non-financial risks is now well-accepted, especially as they relate to climate-related risks. In what follows, we more specifically focus on measuring the impact of transition risks on ETFs. This is a challenging task for a variety of reasons:  Transition risks are difficult to measure due to their inherently multi-dimensional nature, given that the transition to a low-carbon economy generates risks at all levels including regulatory, technological, market and reputational. The relatively short time span over which these risks have had a distinctive impact on asset prices participate in the complexity to measure climate-transition risks. Transition risks are deeply entangled with sector and factor risk exposures, and it is empirically unclear how bottom-up fundamental approaches such as prospective scenario analysis and company scores typically used by ETF investors can cope with such intertwined exposures.  In order to address these difficulties, recent research has investigated the impact of transition risks on market prices by constructing dedicated factors. For example, Bouchet, Herzog and Vaucher (2023) propose a new climate-transition factor that captures both the sectoral and intra-sectoral dimension of the transition to a low-carbon economy. When using this factor to analyze the exposure to transition risks of a sample of 1,277 US equity funds between 2017 and 2021, they do not find that the addition of such a factor significantly improves the power of an asset pricing model, which is consistent with previous findings in the literature.  Interestingly however they present an approach that allows investors to disentangle the risk attributed to financial risk from those stemming from climate transition risks. Over the recent period (2017-2020), they find that the risks associated with the transition factor already represents a significant part of the active risk of some funds. They also find that exposure to certain traditional factors such as “Value” and “Investment” are associated with greater transition risk, which suggests that naïve attempts at reducing exposure to transition risks may therefore lead to undesirable biases with respect to other factors. Conclusion Value can be added by adapting to ETF portfolios some of the risk analysis methods that has been developed to measure equity portfolio exposure to both financial and extra-financial risk factors. In particular, we have reported evidence that such an analysis can allow investors to identify ETF exhibiting a higher-than-average level of stability in relative risk measured through tracking error. Turning to non-financial risks, we also report evidence that a robust measure of transition risks can be derived for ETF portfolios, which allows ETF investors to quantify the amount of risk coming from their exposure to transition sensitive instruments.   Once reliable methods have been made available to measure ex-post the exposure of ETF portfolios to financial and non-financial risks, one can then envision to use these methods to build more efficient ETF portfolios subject to financial and non-financial risk constraints. We see this step as a new frontier in ETF investing.   Notes & References [1] Bloomberg Research Intelligence, March 2021 : https://www.bloomberg.com/professional/blog/passive-likely-overtakes-active-by-2026-earlier-if-bear-market. [2] Statista Research Department, Feb 2023:https://www.statista.com/statistics/278249/global-number-of-etfs. [3] The existence of active views on short-term factor performance can rationalize having a tactical exposure to unrewarded factors that deviates from zero, but the long-term strategic exposure should in principle be zero. [4] The distribution of risk contributions is computed across a group of 17 systematic sources of risk, including 7 fundamental equity factors (market, momentum, value, size, low volatility, investment and profitability) and 10 industries. While industry factors are not rewarded risk factors, their prevalence in explaining differences in portfolio returns justifies their introduction in the analysis. Alessi, L., E. Ossola, and R. Panzica, 2021 What greenium matters in the stock market? The role of greenhouse gas emissions and environmental disclosures, Journal of Financial Stability 54, 100869. Amenc, N, M. Esakia and F. Goltz, 2021, When Greenness is mistaken for alpha, white paper, Scientific Beta, https://www.scientificbeta.com/factor/#/documentation/latest-publications/when-greenness-is-mistaken-for-alpha. Bouchet, V., Herzog, B., and B. Vaucher, 2023, Look up! A market-measure of the long-term transition risks in equity portfolios. Scientific Portfolio Working Paper. Coqueret, G., 2022, Perspectives in Sustainable Equity Investing, CRC Press. Herzog, B., Jones, J., and S. Safaee, 2023, Remember to Diversify Your Active Risk: Evidence from US Equity ETFs, forthcoming, The Journal of Beta Investment Strategies. Kelly, B.T., S. Pruitt and Y. Su, 2019. Characteristics are covariances: A unified model of risk and return. Journal of Financial Economics, 134, 3, 501-524 Martellini, L., and V. Milhau, 2018, Proverbial Baskets are Uncorrelated Risk Factors! A Factor-Based Framework for Measuring and Managing Diversification in Multi-Asset Investment Solutions, Journal of Portfolio Management, 44, 2, 8-22. Vaucher, B., 2023, The Scientific Portfolio Risk Model,  Scientific Portfolio Working Paper.•••

“If you don’t believe it or don’t get it, I don’t have the time to try to convince you, sorry.” — Satoshi Nakamoto, creator of Bitcoin Introduction Cryptocurrencies have been making the front page since 2018 and we are about to celebrate the 15th anniversary of Bitcoin protocol release this year [1]. The endless debate around their legitimacy has not prevented them from being attractive to certain investors – especially among the younger generations, who see them as a quick way to become wealthy. In a world where social networks have garnered the power to disseminate information in real time, today’s financial markets must integrate a growing emotional element. Indeed, all it takes is a news item or even a rumour to make a financial asset or an institution crash or burst. Cryptocurrencies are no exception to the rule and are ultimately victims of their decentralized operation where over- or misinformation too often governs their reputation and their price. This is further exacerbated by their 24/7 quotation and the lack of regulation. We can, then, legitimately raise the following questions: why are some investors attracted to such a risky and unpredictable asset? Can we talk about a standalone asset class which has a role to play in portfolio construction and alpha generation? How can investors get exposure to cryptocurrencies in the simplest and safest way? Based on robust and reliable data sources, the objective of this analysis is to tackle digital assets as a speculative investment and to put forward facts and stats to answer each of the above questions. An investment opportunity Cryptocurrencies offer investors access to the technological innovation of Blockchain which has revolutionized the way values are stored and exchanged on the internet without any centralized operational setup. Accessible anytime and anywhere, there is no discrimination as each actor owns the same power and rights towards cryptocurrencies. As an open book, no censorship is possible since no one can block the process. Most importantly, transparency is part of cryptocurrency DNA: intermediaries are no longer required, and transactions are made from person to person, thus reducing both transaction costs and opacity. At the core of decentralized finance, cryptocurrencies also allow billions of people to hold assets without otherwise having access to standard bank account models. As a result, even “if you don’t believe it or don’t get it”, being exposed to digital assets means being part of both a nascent technology innovation and a revolution of the financial ecosystem. In terms of adoption curve, cryptocurrencies are estimated to reach 1 billion users by 2025 (source: coinmarketcap.com). A standalone asset class and a portfolio diversifier Empirical evidence shows that cryptocurrencies have their own features and agenda. Our data sample is summarized in Annex 1 and spans the last 7 years (from December 2015 to February 2023), namely through the 2020 Financial Crisis and the 2022 bear market exacerbated by the military conflict between Russia and Ukraine. The following table summarizes major risk and return metrics computed across Fixed Income, Equities, and Cryptocurrencies to compare their respective risk/return profiles. In the case of the Maximum Drawdown, the date of the maximum loss has also been identified. Over the past seven years – taken with an annualized return of 78.17% against a slightly higher annualized volatility of 78.22% – Cryptocurrencies have offered the most attractive return per unit of risk, resulting in a return-over-risk ratio of around 1. This ratio is close to 0 for Fixed Income and only 0.52 for Equities over the same period. Equities have the highest percentage of positive returns over the sample period, followed by Cryptocurrencies and Fixed Income. The following graph illustrates the identification of the Maximum Drawdown (MDD) of Fixed Income and Equities (left scale) versus Cryptocurrencies (right scale). For each asset class, the curve labelled “peaks” connects all performance highs from which all successive losses are calculated to identify the maximum loss in percentage over the entire sample period. Based on historical data, the Maximum Drawdown borne by an investor who would have bought Cryptocurrencies at the highest and resold at the lowest over the last seven years is located at -87.56% against -25.22% for Fixed Income and -31.39% for Equities, respectively. The worst-case scenario for Cryptocurrencies, Equity, and Fixed income is respectively identified in 2018, the 2020 Financial Crisis, and in June 2022 (following the Russia-Ukraine geopolitical conflict which initiated the long - and still current - bear market phase). Interestingly, the 2018 MDD was not replaced by the 2022 Crypto Winter during which the drop in value of digital assets was steep, widespread and ongoing, namely with Terra and Luna crashing, and the FTX and BlockFi exchanges filing for bankruptcy. The following table displays correlations across major asset classes and where conditional formatting helps identifying the exposures providing the highest diversifying power. Ex Cash, the lowest correlation levels are identified between Cryptocurrencies and all the other asset classes over the past seven years - with Bitcoin leading the pattern with the lowest figures. It comes as no surprise to see much higher cross-correlations among cryptocurrencies which confirm their common behaviour. Interestingly, Bitcoin and Ethereum are the most correlated digital assets. In order to challenge the diversifying power of digital assets over time, correlations between Cryptocurrencies and the other asset classes have also been assessed over 12-month rolling windows, as shown in the chart below. Correlation levels have varied between -0.47 and 0.68 over the entire sample period, the highest levels being identified in 2022. The war in Ukraine and the geopolitical consequences, the fear of a global economic recession, uncontrolled and uncontrollable inflation drove the markets into red figures uniformly across all asset classes. For comparison purposes, the relationship between Equities and Fixed Income are also shown below (in red): here, correlation levels have almost reached 0.8 over the same period. Another way to address the diversifying power of Cryptocurrencies is to incrementally add digital assets to the traditional 60/40 portfolio while measuring the impact on performance and risk indicators. The incremental allocation to Cryptocurrencies is taken first from the weight of Equities which have a closer risk profile and then from the weight of Fixed Income [2]. The left-hand graph below shows the cumulative performances of the 60/40 portfolio over the last seven years in which Cryptocurrencies are integrated in steps of 1% up to 20%. The introduction of digital assets significantly improves the portfolio annualized return, albeit at the cost of greater annualized volatility. The fair question to raise at this point is to know whether there is an optimal allocation to Cryptocurrencies in terms of return per unit of risk taken. This time, the gradual inclusion of Cryptocurrencies ranges from 0% (i.e., a pure 60/40 portfolio) to 100% (i.e., a cryptocurrency-only portfolio) in steps of 0.5% to 1%. Starting from a pure 60/40 portfolio characterized by a return-over-risk profile of 0.55, the return per unit of risk taken improves up to 1.61 which defines a model portfolio composed of 25.5% of Cryptocurrencies, 34.5% of Equities and 40% of Fixed Income. Beyond this critical point, adding more digital assets hurts the portfolio rate of return per unit of risk taken. In other words, the diversifying power of Cryptocurrencies within a 60/40 portfolio increases up to a 25.5% allocation. These empirical results corroborate similar sensitivity analyses done by VanEck and WisdomTree, both of which added digital assets up to 3% and 5%, respectively. We are perfectly aware that allocating one-fourth of the portfolio assets to Cryptocurrencies could seem a bit farfetched/extreme, but the objective here was to measure the full upside of digital assets in a cross-asset portfolio. How to pick the best implementation solution Digital assets belong to an ecosystem which evolves independently, is poorly regulated, and complex to access, begging the legitimate question as to how investors can reliably and effectively expose their assets. The summary table in Annex 3 compares proven implementation solutions providing exposure to the spot price of digital assets [3]. They are classified according to whether they allow direct or indirect (via a wrapper) ownership of cryptocurrencies. In the latter case, a distinction is made between vehicles physically backed by cryptocurrencies such as Exchange Traded Products (ETPs) and Closed-end Funds, and those that are uncollateralized such as Tracker Certificates. ETPs offering indirect physical exposure to cryptocurrencies are clearly the option of choice, as they provide more advantages than disadvantages. Their strength lies in their ability to be accessible from the traditional financial ecosystem by reducing operational risks and drawbacks of direct ownership. The diversity of cryptocurrency supply via ETPs is closely linked to the reputational risk and liquidity (accessibility) of the underlying digital assets. In other words, for seasoned investors, the unavailability of certain cryptocurrencies through ETPs is also a safeguard. On top of that, this limitation should be reduced over time with the maturation of the underlying market. Based on Trackinsight data, the two graphs below (read left to right) illustrate capital net flow growth in digital ETPs and monthly assets under management (AuM) versus net flows, both measured in USD over the last 12 months: Both illustrations show that there is no clear pattern between performance and investor’s appetite measured through net flows and AuM: net inflows (and net outflows, respectively) do not necessarily follow positive (and respectively, negative) performances. In addition, despite 2022 market correction which did not spare Cryptocurrencies, cumulative net flows have remained constant and resilient throughout the year.  For an institutional investor subject to more regulatory constraints, physical ETPs seem to offer the most robust and easy-to-set-up option, which is why institutional capital net flows across these products have considerably increased since Q2 2021 (source: WisdomTree, 2022). It may, however, seem contradictory to see the rise and success of financial products indirectly offering exposure to digital assets, while Cryptocurrencies are meant to evolve outside the traditional centralized financial system and to be accessible through a digital wallet. In other words, wrappers such as ETPs, closed-end investment funds, or tracker certificates bring Cryptocurrencies back on stock exchanges and traditional trading platforms, and therefore keep them away from the very foundations of blockchain technology. Thorough due diligence Like any financial products, Crypto ETPs deserve a thorough and specific due diligence analysis given that digital risks as well as standard concerns must be covered. For ETPs tracking the spot price of cryptocurrencies, it is important to highlight that their respective performances should only diverge in terms of Total Expense Ratio (TER). Digital assets are quoted around the clock across independent exchanges, making ETP performances dependent on the data source and fixing chosen. This in turn makes their comparability more challenging and incomplete if one doesn’t dive more deeply into their structure and counterparty risk. Apart from the obvious and thorough analysis of the issuer, size, and track record, Trackinsight considers the following criteria crucial when selecting any physically-backed digital ETP: Underlying benchmark The index provider should be regulated and an industry leader in providing cryptocurrency reference price calculations.  Coin entitlement The number of digital assets the ETPs are entitled to should be published and accessible on a daily basis. Custodian: name and number Custodians should be regulated industry leaders and independent entities from the issuer. The use of a multiple-custodian framework can help diversify counterparty risk. Storage risks and mitigation Cold storage through fully encrypted private keys and digital assets kept offline has become the standard across Cryptocurrency ETPs. Given that zero risk does not exist, investors should verify the presence and coverage of an insurance policy against hacking, physical loss, or damage of digital currencies. Hard fork risk A fork occurs when a cryptocurrency existing code is changed, resulting in both an old and new version; this can directly impact collateral viability. The custodian’s hard fork policy should be analysed to know the risk the collateral carries in the event of forking. Crypto lending Crypto lending is a source of revenue and an appealing way to counterbalance the expense ratio. It is also a highway to opacity and uncontrollable counterparty risk as lending activities are not regulated, and borrower identity and creditworthiness are not disclosed. In addition, the accurate composition of the collateral accepted from borrowers is not public information. Staking and implied risk Staking arrangements aim to use a portion of the collateral to engage with Proof-of-Stake (PoS) [4] activities. This source of revenue can be received at the cost of higher counterparty risk. Therefore, even if the ETP is rewarded, investors must make sure the portion of staked collateralized cryptocurrencies is also held in an unsegregated cold wallet, and therefore not exposed to the internet and additional custodian counterparty risk. Another drawback of staking lies in its bonding period. To unlock rewards, users must commit to locking their stake on-chain for a predetermined period. These bonding period lengths vary considerably – anything from a few days to much longer periods of time. As a result, the limited availability during the bonding period induces a liquidity risk for the ETP in the event of massive outflows, given that access to digital assets is not possible. It is important to also consider the devaluation risk during the bonding period, as the PoS process – by virtue of how it is built – disrupts direct market access. Moreover, if the agreement validation process is not done correctly, then the node [5] usually incurs a penalty (so-called, “slashing”), resulting in the loss of some or all of the staked assets. Liquidity Bid-ask spreads along with the number of authorized participants are indicators of liquidity depth. Replication quality Annualized tracking differences should only and solely reflect the expense ratio. If the maths is not correct, then additional costs should be unveiled. Cost efficiency TER should be the key indicator here as the only component to be cut off from the spot price. Annual fees can also be an indicator of the counterparty risk taken, as some Cryptocurrency ETPs are now available for 0% TER which reveals intensive lending and/or staking activities. Last but certainly not least, issuer transparency should be the most important criteria either upon request or as public information through the official website or in the prospectus. No answer is an answer. In other words, the roadmap always leads to the same conclusion: know your Crypto ETP before investing. Key takeaways Whether praised or criticized, cryptocurrencies have delivered double-digit returns on average over the past seven years at the cost of comparable annualized volatility. Their atypical return-over-risk profile along with their decorrelation to other asset classes tend to set them as a standalone asset class. Our empirical analysis has shown that cryptocurrencies clearly have a role to play in terms of diversification power and therefore portfolio construction. A comparative analysis of all implementation solutions available to investors to gain exposure to the spot price of cryptocurrencies shows that ETPs present the most secure and economical route. As an "all-in-one" solution bringing together more strengths than weaknesses, ETPs bring cryptocurrencies back into the traditional financial ecosystem given that they are traded on regulated stock exchanges. This return to the “old world” may seem contradictory since digital assets are technically accessible by anyone, from anywhere – i.e., through a decentralized and deregulated ecosystem. The growing appetite for ETPs over the last three years is a clear sign that, even in a virtual world without borders or rules, there is an inherent need to protect the end investors and their assets, and that regulations in this regard fall short. As a result, the advent of dedicated regulation such as the “Markets in Crypto-Assets” (MiCA) in Europe in 2023 could be a game changer by shaping a legal framework around digital assets that will increase investor confidence. Last but not least, even “if you don’t believe it or don’t get it”, the public commitment of major players across different industries such as Nike, Microsoft, FC Barcelona, BlackRock in 2022 is clearly a confidence vote in favour of Cryptocurrencies (Source: Ficas). References: [1]The white paper that conceptualized blockchain technology, and also named Bitcoin, was published by Satoshi Nakamoto, the anonymous founder of Bitcoin, on 31 October 2008. [2] In order to simulate a realistic strategy, the portfolio is rebalanced quarterly to capture the market drift. [3] It is important to emphasize that the so-called “synthetic” exposures to cryptocurrencies obtained by using derivative contracts are not addressed throughout this analysis. In general, the main purpose of derivative contracts is not to capture the performance of the underlying asset, but rather to speculate on price variations at a later date. As they respond to investment strategies based on price anticipation, their performance can shift significantly from cryptocurrency spot prices. [4] “Proof-of-Stake is a cryptocurrency consensus mechanism [applied to specific tokens such as Ethereum, Cardano and Solana] for processing transactions and creating new blocks in a blockchain [based on the number of staked coins]. Proof-of-Stake (PoS) was created as an alternative to Proof-of-Work (PoW), the original consensus mechanism used to validate a blockchain and add new blocks.” (Source: investopedia.com). [5] A node refers here to a computer that connects to a cryptocurrency network. Annexes Annex 1 - Data sources The following table summarizes the indices used to represent and measure the performance of the 13 assets considered throughout our statistical analysis. Cash - Goldman Sachs Overnight Money Market Index (Link) Fixed Income - Bloomberg Multiverse Index (Link) Equities - MSCI AC World Index (Link) Real Estate - GPR 250 Oceania Index (Link) Hedge Funds - HFRX Global Hedge Fund Index (Link ) Private Equity - LPX50 Listed Private Equity Index (Link) Commodities - S&P GSCI Index (Link) Cryptocurrencies - MVIS CryptoCompare Digital Assets 100 (Link) Bitcoin - CME CF Bitcoin Reference Rate (Link)  Ethereum - CME CF Ether-Dollar Reference Rate (Link) Cardano - CF Cardano-Dollar Settlement Price (Link) Polkadot - CF Polkadot-Dollar Settlement Price (Link) Solana - CF Solana-Dollar Settlement Price (Link) Cryptocurrencies as a whole is represented by the MarketVector™ Digital Assets 100 Index (MVDA) which is a market cap-weighted index tracking the performance of the 100 largest digital assets, with Bitcoin (BTC) leading the market dominance with 45.56% as of 24 March 2023 (Source: www.coin360.com). Annex 2 - References Amo Alexandre, “Cryptomonnaies et la manière la plus sûre de s’exposer à un actif financier émotionnel”, Senior Year Thesis (Travail de Maturité), CECG Madame de Staël High School, 2022. Coinmarketcap, www.coinmarketcap.com, Can Crypto Reach 1 Billion Users By 2025?, February 2023, https://coinmarketcap.com/community/articles/63fc99befd311907dea2814a/. Cryptoast, Bitcoin et Autres Cryptomonnaies, Bien comprendre avant d’investir, Paris, Editions Larousse, 2022. Debru Pierre … (et al.), “A New Asset Class: Investing in the Digital Asset Ecosystem”, July 2022, https://www.wisdomtree.eu/en-ch. Dumas Jean-Guillaume… (et al.), Les Blockchains en 50 questions – comprendre le fonctionnement et les enjeux de cette technologie, Paris, Dunod, 2022.  Ficas, www.ficas.com, Market Review & Top Crypto Trends 2023, 2023, https://ficas.com/research-and-insight/market-review-2022-outlook-2023/. Investopedia, www.investopedia.com, Crypto Lending, 2022,  https://www.investopedia.com/crypto-lending-5443191. VanEck, www.vaneck.com, https://www.vaneck.com/ch/en/bitcoin-etn/. Annex 3 - Implementation solutions­ The ETP acronym The “ETP” acronym holds here for Exchange Traded Fund (ETF), Exchange Traded Note (ETN), and Exchange Traded Cryptocurrencies (ETC). As a reminder, and in terms of legal structure, ETFs are investment funds, while ETNs and ETCs are debt securities (bonds).•••