To date, there have been several attempts to somehow determine the fundamental value of bitcoins in academic literature. For example, Yermack (2013) expressed an opinion that bitcoin has no intrinsic value at all. Jenssen (2014) argues that value of bitcoin is related to computer labor power required to produce it. A little later, Garcia, et al. (2014) stated that the estimated cost of mining bitcoin should at least serve as a lower boundary for its’ fundamental value.

Woo et all. (2013) put forward another hypothesis that the fundamental cost of bitcoin is based on its’ money-like properties. A similar theory was expressed by Polasik et al. (2014).

The last two theories — determination of fundamental value on the basis of computing power required to produce bitcoins, and the demand for a given crypto currency as a mediums of exchange, are of the greatest interest at the moment. The reason for this is the possibility of constructing mathematical and economic models for formulating and estimating hypotheses of bitcoin cost.

Regarding computing power, the most interesting work, in our opinion, is the work of Adam Hayes, published in 2015 under the title: Cryptocurrency Value Formation: An Empirical Analysis. Leading to a Cost of Production Model for Valuing Bitcoin. In this article the author formulates econometric hypothesis, and then creates a regression model to validate the hypothesis. The author tested the model on 66 most popular crypto-currencies and obtained interesting results.

Adjusted coefficient of determination was 0.83, which is quite high and reflects the model’s credibility. Omitting statically insignificant regressor (there were 2 of them in a model), the model looks li ke this:

ln (price) = -9.68 + 0.67 * ln (GC / s) — 0.98 * (Coins / m) + 7.43 * (algorithm type), where

GC / s — computational power in gigahash per second;

Coins / m — the number of new coins per minute (division of block reward by time between blocks);

Algorithm type — a dummy variable (0 if SHA-256 is used, 1 if scrypt is used).

It is interesting that all the signs of coefficients under regressors are confirmed by a priori judgments about their effect on the price. For example, a priori judgment on the influence of computational power can be formulated as “the more powers are involved in calculating 1 coin, the more it costs”, i.e. relationship is positive. The sign of “coins / m” regressor reflects the effect of diminishing marginal utility. Type of algorithm reflects the effect of a more efficient algorithm (scrypt, which corrects some of the shortcomings of SHA-256).

Thus, Hayes built a fairly reliable model for estimating the value of the crypto currency. The question of its predictive ability is still open, but the model itself is sound and logical.