The Empirical Research on the Impact of Digital Currency on Monetary Liquidity and Currency Multiplier

DOI

10.2991/aebmr.k.191217.146How to use a DOI?

Keywords

digital currency, recursive neural network, unscented Kalman filter algorithm, the velocity of currency circulation, money multiplier

Abstract

First, this paper establishes an AI model to simulate the issuance of currency. Based on VAR model, recursive neural network and state space model, the model simulates the issuance of digital currency under certain conditions, and uses the lossless Kalman filter algorithm for deep learning and weight correction. Second, on the basis of money supply theory, this paper analyzes the impact of digital currency on currency circulation speed and money multiplier through empirical research, such as the unit root test of time series, co-integration test, error correction model of different variables in the short term. There is a definite possibility that the network effect of digital currency accelerates the velocity of money circulation in different periods. The more trading entities, the greater the growth. In addition, the digital currency also has an impact on the currency multiplier, which increases the leverage ratio and risk accumulation of the financial system and the difficulty of financial supervision. Finally, this paper puts forward that the digital currency trading system can be regulated by designing a controllable anonymity algorithm for digital currency.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Fenghua SHAO
PY  - 2019
DA  - 2019/12/20
TI  - The Empirical Research on the Impact of Digital Currency on Monetary Liquidity and Currency Multiplier
BT  - Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019)
PB  - Atlantis Press
SP  - 839
EP  - 847
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.191217.146
DO  - 10.2991/aebmr.k.191217.146
ID  - SHAO2019
ER  -