Digital Money Never Sleeps? Modern qualitative-based indices in digital assets

Abstract

What can we know about digital assets, as digital money also never sleeps? How can we measure the variations in these digital assets from a new perspective? What are the effects of digital assets on financial markets? The non-stop changes in finance caused by the financial technology revolution have deeply changed assets. Moreover, as investment tools, assets have been expanded from jewels, precious metals, real estate, stocks, bonds and others to cryptocurrencies, central bank digital currencies (CBDCs) and non-fungible tokens (NFTs). The story of Digital Money Never Sleeps is inextricably linked to how assets evolved in the FinTech area and how digital assets transformed the financial markets. This thesis creatively shows how to tap an online database to develop and evaluate new measures of interest to the three most popular digital assets: cryptocurrencies, CBDCs and NFTs. This thesis then offers useful insights into the financial implications of the three digital assets by focusing on three critical research questions motivated by a systematic literature review.

The first research question and its research activities introduce three new qualitative-based indices around cryptocurrency spaces by using 726.9 million and 778.2 million news articles separately: on cryptocurrency policy uncertainty (UCRY Policy), on cryptocurrency price uncertainty (UCRY Price) and on cryptocurrency environmental attention (ICEA). Then, around these three newly issued cryptocurrency indices, this thesis further applies the vector autoregression (VAR)-based model to quantify the shocks from UCRY Policy, UCRY Price and ICEA to financial markets. Moreover, this thesis tests cryptocurrency uncertainties' impacts and predictive power on precious metal markets.

The second research question and its investigations provide two new qualitative-based indices around the growing area of Central Bank Digital Currency (CBDC) by using 660 million news stories: the CBDC Uncertainty Index (CBDCUI) and CBDC Attention Index (CBDCAI). The application of structural vector autoregression (SVAR) and dynamic conditional correlation model (DCC)-GJR-generalised autoregressive conditional heteroskedasticity (GARCH) models to the second research question helps this thesis uncover how CBDC indices interact with several financial indicators, which could provide novel empirical evidence on the effects of CBDC news on financial markets.

The third research question and its studies present unique insights into the NFT market by creating the non-fungible tokens attention index (NFTsAI) based on 590 million news reports. This study uses the TVP-VAR volatility spillover connectedness model to explore the risk transmission across NFTs' attention and financial markets. Moreover, motivated by the violent fluctuations in the NFT markets captured by the NFTsAI, this thesis further detects the price bubble in these emerging digital assets.

Ultimately, these qualitative-based indices can provide new proxies for measuring and evaluating cryptocurrencies, CBDCs and NFTs. Moreover, this thesis can offer a new methodology to develop new measures to deepen our understanding of finance and economics. The three investigations in this thesis display solid and robust results to answer the research questions identified in the literature review. Additional research directions are also determined for future research throughout the thesis.