Modelling the digital / Crypto currencies with the fiat Money of the country: can Crypto overtake fiat money? / Halil Paino and Syed Iskandar Zulkarnain Sayd Idris

Since the awakening of Bitcoin, the terms “Block chain”, “Digital Currency”, “Crypto currencies” have become the buzz words. Lot of research is being carried out studying various aspects of digital currencies and crypto currencies. It is almost a decade now since, Bitcoin is issued for the first time on January 2009. Since then there are numerous (more than 1000) Alt currencies which came into market. Crypto currencies were highly hyped and offered the rate of return of 700% in some cases to the investors. On the flip side many investors lost heavily when the prices of crypto currencies trembled terribly. Ammous S. (2018) has compared top 5 crypto currencies – Bitcoin, Lite coin, Ether, Ripple and Steem – with the fiat money. He has compared them on the basis of their monetary policy. Further, each currency is evaluated on the basis of supply growth, credibility and stability. He concludes that bitcoin has limited supply growth but highly volatile demand which makes the prices of the bitcoin extremely. Digital currencies and crypto currencies were thought to be very promising when introduced. It has boomed feverously in the hope of the inherent promise of being capable of replacing the traditional currency. It fumbled when the demand has dropped when the market saw its limitations as global acceptance. We will applied the Analytic Hierarchy Process (AHP) method for us to derive ratio scales from paired comparisons and rate the expected performances for two alternatives. AHP was originally developed by Prof. Thomas L. Saaty and has been widely adopted to transform the expert’s tacit knowledge into quantitative results. This is one of multi criteria decision making method which derive ratio scales from paired comparisons. The input can be obtained from quantitative data such as wage, material cost or from qualitative data such as preference and subjective opinion. Considering that human is not always consistent, this method allows small amount of inconsistency in decision making process.