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Cryptocurrencies, as an 'asset class' (if we may use that phraseology), or speculative wagers, are *the riskiest* traded assets in the world. Nothing else like stocks, ETFs, commodities, etc., comes close. Therefore, having a thorough understanding of the financial risk one is being exposed to when dealing with such speculative assets is imperative for capital protection.

This work presents an analysis regarding the risks of investing in some of the most recognized Cryptocurrencies: Bitcoin, Ethereum, Dogecoin, Binance Coin, Ripple, Cardano, Shiba Inu, & Solana. How much these currencies can fall in a market crash is analyzed utilizing established methodologies. Which, amongst the above-mentioned Cryptos, provides the best risk-adjusted returns, is also examined, i.e., which Crypto provides the highest mean returns per unit of risk.

Those interested in trading Cryptocurrencies, or exposed to them through addition to their portfolio, must understand the factors expounded in this work to thoroughly understand the risks (upside & downside) Cryptos expose investors/traders to.

If you cannot pay attention or thoroughly understand these factors, then you, more likely than not, are setting yourself up for significant losses.

Table 1.

### Critical points

The price of Bitcoin, as per the data & 95% VaR, can fall -26.3%% in a week (W) & -52.6% in a month (M); the same for Ethereum stands at -41.53% (W), 83.06% (M), & for Cardano, the same stand at -53.24% (W) & over 100% (M). Nonetheless, there remains a 5% probability of losses exceeding these values.

This means that for every $1000 invested in Bitcoin, Ethereum, & Cardano (in that order), traders/investors should experience losses equal to, or lower than, $263, $526, $532 in a week; there remains a 5% chance of losses exceeding these figures.

For all other Cryptocurrencies analyzed in this report, see column 7 ([6] in the rows of the table above).

See also:

Binance Coin (BNB), Ripple (XPR), & Cardano (ADA), in that order, as per the data, are the riskiest Cryptos. As per VaR analysis, their prices can fluctuate significantly, and they can lose more than 90% of their value in under a month. That, of course, also means that investors that invest in these coins can lose all their initial investment in under a month in an adverse period. Thus, they can in no way be classified as currencies, which are supposed to, fundamentally, be a store of value; they can only be classified as speculative wagers as per their risk profiles.

On the flip side, the above-mentioned three have had high mean weekly returns that surpass any other asset class. However, the fundamental question that remains is whether the whole Crypto-phenomenon is a bubble?

**Has big/institutional money stabilized BTC & reduced volatility?**

BTC has somewhat stabilized (in terms of volatility & returns) from 2018 onwards. Its mean monthly volatility from 2011 to 2017 period stood at 48%, declining to 22% in 2018 to 2021 period. Mean monthly returns from 2011 to 2017 stood at 23%, reducing to an average of 5% per month from 2018-2021.

BTC's stabilization hasn't improved its proposition as an investment; its average Shapre ratio for the 2011-2017 period stood at about 48%, however, it declined to 23.4% in the 2018-2021 period; put simply, while BTC's volatility has reduced somewhat, it's risk-adjusted returns (percentage returns per unit of risk) declined by about 51% in 2018-2021 period, compared to 2011-2017 period.

### Cryptocurrencies that offer the highest risk-adjusted returns

As per the Sharpe ratio, in the time-series assessed, Solana (SOL) has provided the highest risk-adjusted returns (42% return per 1 unit of risk, or put another way, 42 cents per every dollar risked by investing), followed by Shiba Inu (SHIB) (26%), & Ethereum (ETH) (22%).

This also means that excluding highly volatile coins, Ethereum (ETH) has provided the highest risk-adjusted returns; the lowest such returns are offered by Ripple (XPR), which has offered the mean weekly returns as ETH, however, its price volatility (risk) is 89% higher than ETH; hence, as per historical performance, it is the least attractive coin to hold as an investment.

When returns are assessed based on two risk parameters as done under RCD-VAR ratio [(10) row in Table 1], while SOL still emerges as offering the best returns commensurate to risk, the second place is secured by Bitcoin (0.57), and third-place by ETH (0.52). This fundamentally means that while returns offered by emerging coins such as SHIB and SOL do appear attractive, when adjusted for two risk parameters, stable coins such as ETH and BTC emerge as superior options.

Further statistical testing confirms that the risk profile of ETH & BTC is different with statistical significance (T1), ETH is risker; similarly, SOL's risk profile is higher than ETH with statistical significance (T2), and SHIB is risker than SOL with statical significance (T3). Results of statical tests are attached at the end.

It is also important to understand that while BTC & ETH have been traded for a considerable time by now, SOL and SHIB are still new players on the block; whether they'll maintain an exceptional performance long-term cannot be said.

Many Cryptos that used to be in the top five have declined to below top 40 (notable examples: Monero (XMR), Zcash (ZEC) [below top 60 for ZEC], Dash [below top 70 for Dash]). Others that were said to be the 'next big thing' & were on a solid upward trajectory, such as Electroneum (ETN), Bytecoin (BCN), etc., have fallen by hundreds of places in the rankings to become what they actually were without the hype—worthless for all practical purposes.

Thus, the brief history of Crypto teaches us to stay away from volatile coins—no matter how much they rise in the short run—and if one has to add Crypto to the portfolio, stick with stable coins: notably BTC & ETH.

"Never let greed govern your decisions . . . that will never serve you well. A logical investor always performs better than an emotional one "

Stocks are still widely considered as being overvalued, and bond yields are considered synthetically lowered by central banks—to a point where yields (arguably) don't cover the long-term depreciation of capital caused by inflation. In the current condition, many are concerned that stocks may not perform as they have in recent years & bonds due to high inflation & synthetically lowered yields (especially sovereign bonds) depreciate capital, rather than appreciate it. This condition is the primary driver of demand for Crypto in the current period.

Nevertheless, conditions and market dynamics can change in a short period, changing the landscape altogether.

**How to use values provided in this work:**

(1) Cathy, attracted by the recent price appreciation in many Cryptos may want to invest in, say, BNB. She may have an assumption that the price of BNB will continue to appreciate handsomely; However, she might also have a concern that the price may sharply fall, as it has in the past, and that can cause her severe losses. To understand how much the price of BNB can fall in a crash, she may utilize The VaR analysis of BNB provided in this report to manage her exposure, leverage, stop loss, etc.

This value stands at -67% weekly and over 100% per month, as per a 95% confidence level (Table 1). This figure should convey to her that weekly losses, in an adverse period, 95% of the time will be -67% or lower; or, $670 for every $1000 invested. Alternatively, Losses 5% of the time would be at least 67% in a week; the VaR figure also affirms that she can lose all her investment in under a month, and thus, she should manage her downside risk very carefully.

(2) George, concerned about a sudden rise in inflation, may want to invest his wealth in scarce assets, and may be interested in Cryptocurrencies. He may examine the ratios provided in this work, such as the Sharpe ratio, RCD-VAR ratio (Table 1), to acquire the coins that offer the best risk-adjusted returns. Such an approach would, of course, be far superior to any anecdotes or recommendations provided by 'gurus' as ratios calculated in this work are data-derived.

It is important to note, however, that price dynamics and fluctuations in particular periods may differ from the mean values calculated in this work. Long-term, the values should, nonetheless, be more compatible with mean values.

See also:

Those interested in assets with actual intrinsic value, see: How Much Can Metals, Energy, & Grains Fall In a Market Crash? | Which Is the Best Commodity to Hold?

### Statistical tests (results)

__(T1) F test for variances, using F distribution (dfnum=418,dfdenom=330) (two-tailed) (validation)__

** 1. H0 hypothesis**
Since p-value < α, H0 is rejected.
The sample standard deviation (S) of

**BTC's**population is considered to be

**not equal to**the sample standard deviation (S) of

**ETH's**population. In other words, the difference between the sample standard deviation (S) of the

**two**populations is big enough to be statistically significant.

** 2. P-value**
The p-value equals

**2.22e-16**, ( p(x≤F) = 1.11e-16 ). It means that the chance of type I error (rejecting a correct H0) is small: 2.22e-16 (2.2e-14%). The smaller the p-value the more it supports H1.

**The test statistic F equals**

__3. The statistics__**0.3544**, which is not in the 95% region of acceptance: [0.8161 : 1.2284]. S1/S2=0.6, is not in the 95% region of acceptance: [0.9034 : 1.1083]. The 95% confidence interval of σ12/σ22 is: [0.2885 , 0.4342].

__(T2) F test for variances, using F distribution (dfnum=67,dfdenom=330) (two-tailed) (validation)__

** 1. H0 hypothesis**
Since p-value < α, H0 is rejected.
The sample standard deviation (S) of

**SOL's**population is considered to be

**not equal to**the sample standard deviation (S) of

**ETH's**population. In other words, the difference between the sample standard deviation (S) of the

**two**populations is big enough to be statistically significant.

** 2. P-value**
The p-value equals

**0.002948**, ( p(x≤F) = 0.9985 ). It means that the chance of type I error (rejecting a correct H0) is small: 0.002948 (0.29%). The smaller the p-value the more it supports H1.

**The test statistic F equals**

__3. The statistics__**1.6924**, which is not in the 95% region of acceptance: [0.6721 : 1.4199]. S1/S2=1.3, is not in the 95% region of acceptance: [0.8198 : 1.1916]. The 95% confidence interval of σ12/σ22 is: [1.1919 , 2.5179].

__(T3) F test for variances, using F distribution (dfnum=67,dfdenom=28) (two-tailed) (validation)__

** 1. H0 hypothesis**
Since p-value < α, H0 is rejected.
The sample standard deviation (S) of

**SOL's**population is considered to be

**not equal to**the sample standard deviation (S) of

**SHIB's**population. In other words, the difference between the sample standard deviation (S) of the

**two**populations is big enough to be statistically significant.

** 2. P-value**
The p-value equals

**0.001489**, ( p(x≤F) = 0.0007446 ). It means that the chance of type I error (rejecting a correct H0) is small: 0.001489 (0.15%). The smaller the p-value the more it supports H1.

**The test statistic F equals**

__3. The statistics__**0.3849**, which is not in the 95% region of acceptance: [0.553 : 1.9649]. S1/S2=0.62, is not in the 95% region of acceptance: [0.7436 : 1.4018]. The 95% confidence interval of σ12/σ22 is: [0.1959 , 0.6961].

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