Index
2023; Linguagem: Inglês
10.1108/s1569-376720220000022016
ISSN1875-5437
Tópico(s)Blockchain Technology Applications and Security
ResumoCitation (2023), "Index", Kim, S.-J. (Ed.) Fintech, Pandemic, and the Financial System: Challenges and Opportunities (International Finance Review, Vol. 22), Emerald Publishing Limited, Bingley, pp. 363-376. https://doi.org/10.1108/S1569-376720220000022016 Publisher: Emerald Publishing Limited Copyright © 2023 Suk-Joong Kim INDEX Account-based digital payments, 228 via bank accounts, 228 Account-based payments, 228 ACI Financial Markets Association (ACI FMA), 174 Adjacent markets, 190 Alphabet, 238 Alternative credit, 27, 32 American Bankers Association (ABA), 175 Analytical Business Enterprise Research and Development database (ANBERD), 19 AnnA Villa luxury property, 157 Anti-money laundering/anti-terrorist funding (AML/AT F), 147 Apple, 238 Art, 153 Artificial intelligence (AI), 14, 346 Asian Development Bank (ADB), 38 Data Library, 40, 53 Regional Indicator, 40 Asian financial centers, 343, 347 Asset tokenization, 155 Asset under management (AUM), 8, 244, 246 Asset-backed securities (ABS), 147 Asset-backed tokenization, 153 Asset-backed tokens (ABTs), 6, 146, 150–154 background, 148–150 benefits of tokenization, 154–155 capital requirements, 171–172 case studies, 156–161 challenges, 155–156 consultation outcomes, 173–176 general principles, 168–171 regulatory issues, 168–176 risks of permissionless DLTS and smart contracts, 161–168 Asset-pricing relationships comparison of cryptocurrency and equity market factors, 100–103 cryptocurrency pricing by equity and crypto factors, 104–108 cryptocurrency pricing by global and regional factors, 108–109 data, 98–100 Association of Proprietary Traders (APT), 174 Auto loans, 154 Automated teller machines (ATMs), 17 Bahamas, CBDC, 192 Bank for International Settlements, 351 CBDC coalition, 195 Bank of America (BofA), 16, 128, 351 Bank of England, 7, 202–204 Bank of Russia, 194 Bank of Thailand (BOT), 146 Bank Policy Institute, 173 Banking business models, 18 crisis, 342 deposits, 354 fintech, 17 industry, 16 legislation, 79 license, 14 revolution, 339 Banking crisis (2001), 79 Banks, 14, 224 account-based digital payments via bank accounts, 228 business model, 14 criticism, 175 transfers, 228 Basel Capital Accords, 175 Basel Capital Adequacy Framework, 161, 173 Basel Committee on Banking Supervision (BCBS), 7, 17, 147, 168 approach, 172 regulatory proposals, 147 Basel III international regulations, 166 Basel III prudential framework, 172 BBVA, 173–174 BeautyChain (BEC), 163 Beijing, 341 Benchmark currency, 235 Benchmark finance regressions, 283–286 Bigtech, 14 Bigtech credit, 23, 27, 32 pc, 27 Binance, 148, 164, 209 BIS, 168–169, 171, 173, 187 Bitcoin, 96, 98, 100, 114–115, 146–148, 174, 186, 223, 355 blockchain, 155 companies, 164 DLT, 161 protocol, 149 Bitcoin Association Switzerland (BAS), 176 Bitcoin gold (BTG), 163 Bitzlato, 164 Black-owned businesses, 78 Blockchains, 7, 14, 148, 154, 227 blockchain-based euro, 225 governance, 207 monetary innovation around, 225–226 projects, 202 technology, 223 BNP-Paribas, 173 BNY-Mellon, 173 Bond-I, 158, 173 Bonds, 151, 153 Borrower, 81 Borrower-level variables, 69 Borrower–lender distance, 65 Borrow–lender distance, 72 Brazilian Central Bank, 231 Brexit, 348–353 negotiation uncertainty, 349 referendum, 348 Bridge Protocol platform, 159 Business enterprise, 120 environment, 342–344 method patents, 15, 18 models, 14, 226 California Resources Cooperation (CRC), 122 Canadian Bankers Association (CBA), 174 Capital City Bank Group (CCBG), 128 Capital controls on capital flows, 301 empirical framework, 301–305 events, 299, 304, 314 on inflows implemented in Nigeria, 311–313 on inflows released in Kenya, 309–311 interventions, 314 on outflows imposed and controls on inflows removed in Egypt, 307–309 on outflows removed in Russia, 305–307 results, 305, 313–314 sensitivity analysis, 314–321 Capital flight, 298, 307 Capital flows management, 298 Capital inflows, 304 Capital outflows, 304, 310 Carry risk premiums, 96 Cash, 189, 228 transactions, 227 Central Bank Digital Currencies (CBDCs), 7, 158, 186, 203, 224, 231, 355 Bahamas, 192 BoE proposing, 203–204 case studies, 192 cryptocurrency, 208–211 Eurozone, 193 financial inclusion, 188 and KYC, 231 monetary and economic sovereignty, 191–192 monetary policy, 187–188 motivation for, 186–187 need for, 206 Nigeria, 193 payment services, 188–191 People’s Republic of China, 193–194 role of retail CBDC in UK payments landscape and global financial system, 204–206 Russian Federation, 194 Sweden, 194–195 UK case for, 211–213 UK Implement CBDC, 206–208 United States of America, 195 Uruguay, 195–196 Central Bank of Bahamas, 192–193 Central Bank of Uruguay, 195 Central banks, 224 China, 233 Euro Area, 234–235 money, 186 responses to digital payments, 232 United States, 233–234 Ceteris paribus, 187, 283 Challenger banks, 211 China, 233 China’s “political philosophy”, 238 China’s Digital Currency Electronic Payment, 355 Citigroup, 351 “Cliff-edge” effects, 173 Coinbase, 148, 170, 176, 209 Coinmarketcap. com, 98, 224 Commercial bankers, 344 Commercial banks, 4, 161 Commodities Futures trading Commission (CFTC), 150 Commonwealth Bank of Australia (CBA), 146 Companies, 237 Competition, 203–204 for bank business models, 14 competition-law based access, 191 financial technology and, 71–72 Competitive dynamics, 229 Competitive pressure, 27 Concentration of mining capacity, 164 Concentration of mining power, 163 Conditional value-at risk models (CVaR models), 114, 127, 133 data description, 122 diversification constraints, 119–122 mathematical models for portfolio optimization, 116–119 portfolio including cryptocurrency, 131–133 portfolio optimization with, 118 portfolio without cryptocurrency, 122–131 Consensus mechanism, 149 process, 150 Consumer loan performance data, 81–85 gender gap in, 81 methodology, 85–87 results, 87–90 Control Bond, 157 Conventional capital flows management objectives, 299 Conventional financial stability objectives, 299 Conventional risky assets, 114 Convex quadratic programming problem, 117 Coronavirus Aid, Relief, and Economic Security Act (CARES Act), 64 Corporate social responsibility (CSR), 348 Corporations, 238 Cosmos, 148 Countries considered in study, 36 Country list, 329 Covariance matrix without Bitcoin, 125 COVID-19 crisis, 64–65 dummies, 263, 266–267 outbreak, 37 pandemic crisis, 4–5, 8, 15, 38, 188, 245, 247 recession, 259 Creative solutions, 349 Credit, 278, 283, 285 allocation process, 78 channel view concept, 276 market imperfection, 276 money systems, 346 Credit cards, 228 loans, 154 Cross sectional asset-pricing tests, 96 Cross-border deposits, 310 Cross-border lending, 298, 302, 305 to Kenya, 309 Cross-sectional variation, 279 “Crowd-out” effect, 247 Crowdfunding platforms, 15 Crypto assets, 150, 168–169, 208, 212, 221, 223, 238, 300 taxonomy, 154 Crypto exchanges, 212 Crypto factors, cryptocurrency pricing by, 104–108 Crypto-trading, 123 Cryptocurrencies, 4, 6–7, 96, 99, 146, 148, 153, 186, 202, 208–211, 213, 300 age, 96 bi-monthly portfolio rebalancing by CVaR model, 136 bi-monthly portfolio rebalancing by Kataoka’s model, 135 bi-monthly portfolio rebalancing by Markowitz model, 134 comparison of cryptocurrency and equity market factors, 100, 102 comparison of equity and cryptocurrency size and momentum factors, 103 competencies, 208–210 exchanges, 148, 300 factors, 97, 108 implied volatility factors, 103 numerical results from technical analysis using daily closing price data, 137 portfolio including, 131 portfolio without, 122–131 pricing by equity and crypto factors, 104–108 pricing by global and regional factors, 108–109 rebalancing and diversification with crypto, 133–136 size and momentum factors, 100 trading based on technical analysis with crypto, 133 Cryptocurrency market (CMKT), 99, 106 Cryptocurrency momentum (CWML), 99, 102, 108 Cryptocurrency size (CSMB), 99, 106 Cryptocurrency uncertainty factor (UCRY), 100 Cyber-attacks, 170 Data, 39–42, 190, 249–250 capital controls, 301–303 consumer loan performance, 81–85 cryptocurrencies meet equities, 98–100 description, 122 descriptive Statistics, 84 dissemination, 158 fintech in PPP, 66–69 firm-level variables, 66–67 lender-level variables, 68 measures of distance, 68 methodology, 68–69 and sample, 279 summary statistics, 41, 99 variable descriptions, 82 DebtMarket, 289 Decentralized autonomous organisation (DAO), 166 hacking, 167, 171, 175 smart contracts, 167 Decentralized finance (DeFi), 224, 231–232 Decentralized monetary systems, 223 Decentralized networks, 232 Decision-making process, 90 Delisted cryptocurrencies, 98 Deliveryversus-payment processes (DvP), 225 Deposit amount, 81 Deutsche Bundesbank, 237 Diem, 186 Difference-in-difference (DID), 299, 316–318 Digital art, 153 Digital assets, 151 Digital cash, 227, 231 Digital currencies, 213, 236 Digital dollarisation, 191–192 Digital economy, 206 Digital euro from geopolitical perspective, 224, 227 account-based digital payments via bank accounts, 228 cash transactions, 227 CBDC and KYC, 231 central bank digital currencies, 231 central banks’ responses to digital payments, 232–235 digital money solutions, 226–227 euro as digital regional currency, 236–239 geopolitical significance of money, 235–236 monetary innovation around blockchain technology, 225–226 novel digital money solutions, 228–230 stablecoins and KYC, 230 trigger solutions, 231–232 Digital finance, 46–51 Digital financial inclusion data, variables, and methodology, 39–42 digital finance, pandemic uncertainty index, and GDP losses, 46–51 empirical results, 42–46 regressions, 43–45 robustness check, 51–53 SEM decomposition of path effects, 48–50, 52 SEM framework, 47 SEM ratio analyses, 51 Digital Market Act, 191 Digital money, 226 solutions, 226–227 Digital payment (Digi_pay), 42 central banks’ responses to, 232–235 Digital platforms, 148 Digital pound, 210 Digital regional currency, euro as, 236–239 Digital technologies, 14 Digitalization, 224 Digitalized payment methods, 202 Distance, measures of, 68 Distributed Denial of Service, 170 Distributed ledger technology (DLT), 146, 148, 154–155, 221 Scriptless Bond Project, 158 Distributed ledgers (DL), 147–148 Diversification among small-, mid-, and large-cap stocks, 120 Diversification constraints, 119 blend with growth and value stocks, 120–122 diversification among small-, mid-, and large-cap stocks, 120 investing in different industries, 119–120 portfolio evaluation by Markowitz Model, 127 portfolio rebalancing, together with, 128–130 portfolios without, 124–128 Diversification of stock portfolio, 119 Diversification with crypto, rebalancing and, 133 Divorce rate, 83, 86 Domestic governments, 205 Domestic payments, 187 Donor pools, 331 selection of, 303–304 Double-spending, 161 Dublin, 347, 349 Dummy variables, 81 DvP transactions, 226 e-Krona, 195 E-money, 188 Economic changes and percussion on leading financial centers, 348 Brexit, 348–353 fintech as new driver of financial innovations, 354–357 Hong Kong under China’s tightening grip, 353–354 Economic development, 274 measure for, 275 Economic growth, 356 financial development in, 338 Economic lockdowns, 37 Economic mechanisms, 15 Economic networks, 339 Economic sovereignty, 191–192 Economist Intelligence Unit (EIU), 46 Economists, 274 Economy, 8–9 financial sector, 274 Efficiency and competition in payment markets, 190 Egypt, 333 controls on outflows imposed and controls on inflows removed in, 307–309 “El corralito” (capital controls in Argentina), 300 Elderly dependence average number, 83, 86 Electronic Chinese yuan (e-CNY), 193 Electronic custody, 152 Emerging economies, 81 Emerging market context, 79 Empirical analysis, 275, 283 (see also Sensitivity analysis) additional analyses, 289 benchmark finance regressions, 283–285 FSS and labor market performance, 287 main results, 285–289 Empirical design, 277 data and sample, 279 descriptive statistics, 279–283 modelling approach, 277–279 variable definitions and sources, 280 Empirical framework, 301 data, 301–303 empirical model, 304–305 selection of interventions and donor pools, 303–304 Empirical model, 304–305 eNaira, 193 End user benefit vs. risk matrix based on BoE retail CBDC design principles, 219–220 Equisafe, 157 Equities, 151, 153 cross-sectional asset-pricing regressions with equity and cryptocurrency risk factors, 105, 107 cryptocurrency pricing by equity factors, 104–108 downside risk, 108 Ether, 223, 238 Ethereum, 147, 150–151, 155, 230 blockchain, 146 multi-signature wallet parity, 165 smart contracts, 166 Euro area, 234–235 as digital regional currency, 236–239 system, 205 Euro Banking Association (EBA), 175 Euro Medium Term Notes (EMTN), 158 Euro-system central banks, 209 European Central Bank (ECB), 193, 203, 224 European financial centers, 349 European Financial Markets, reduced access to, 348–353 European Union (EU), 191, 347 authorities, 349 CBDC models, 205 investors, 351 model, 205 Eurozone, CBDC, 193 Ex ante heterogeneity, 83 Excess Finance, 278 ExcessStockMarket, 286 Exchange traded funds (ETFs), 8, 244 crisis, 259–270 data, sample selection, and variable construction, 249–250 ecosystem, 245 empirical results, 250 ETF-specific categories, 246 event studies on delisted and Zombie ETF, 251–259 first-mover and winner-takes-all effects, 250–251 GFC and COVID-19 crisis, 260–262, 268 hypotheses, 248–249 institutional holdings and trading of ETFs during COVID-19 crisis, 269 issuer, 248 liquidity, 246, 263 subsample analysis, 264–265 Experimental Bond, 157 External capital flows, 300 Fama–French factors, 104 Fama–MacBeth methodology, 104 Federal Reserve, 195 Federation of Latin American Banks, 174 Female labor force participation, 83 Female–Fintech interaction, 90 FIA European Principal Traders Association (FIA EPTA), 174 Fiat currency, 151 Fiat-backed stablecoins, 229 Finance, 283, 352 Financial centers, 308 challenges to dominance, 348–357 determinants of global financial center, 340–348 economic power, 341–342 financing infrastructure development and accessibility, 346–347 function, 340 governance and business environment, 342–344 growth of financial services, 344–345 high-skilled labor force, 345–346 host country’s reputation and stability, 347–348 Financial Conduct Authority (FCA), 157, 202 Financial corporates, 158 Financial crisis, 259, 344 Financial development, 282, 338 Financial inclusion, 187–188, 208 Financial innovations, 23, 212 Financial Market Infrastructures (FMIs), 202 Financial markets, 339 Financial patents, 19 Financial Policy Committee (FPC), 212 Financial sector size (FSS), 274 background and related literature, 275–277 empirical analysis, 283–289 empirical design, 277–283 Financial services, growth of, 344–345 Financial stability, 187, 298 motive, 300 Financial Stability Board (FBS), 17, 150 Financial system, 6–9, 338–339 Financial technology (FinTech), 4–5, 14, 17, 27, 68, 71–72, 78, 80, 90, 212, 300, 346, 352, 356 and bigtech competition, 14 borrowers, 83 companies, 146, 151, 158, 160 and competition, 71–72 credit, 23, 27, 32 credit pc, 32 data and methodology, 20–23 dependent and explanatory variables, 21 drivers of average value of patents possessed by banking sector, 30–31 drivers of number of patents held by banking sector, 28–29 drivers of value of patents possessed by banking sector, 25–26 enabled financial inclusion, 39 fintech-driven financial inclusion, 38 firms, 14 initiatives, 186 lenders, 66 literature review and hypotheses formulation, 17–20 and loan distance, 69–71 method, 23–24 as new driver of financial innovations, 354–357 number, 22, 32 patents, 16 results and interpretation, 24–32 Financial–economic development relationship, 338 Financing for fintechs, 22, 27, 32 infrastructure accessibility, 346–347 infrastructure development, 346–347 Firm data, 66 Firm-level variables, 66–67 summary statistics, 67 FirmAge, 67 First-mover effects, 247–248, 250–253 Fiscal policy, 203 Five-factor Fama–French model, 106 51% attacks, 155, 162 risk of, 156 Foreign central banks, 205 Forks, 170 Four-factor Carhart model, 106 Funding process, 70, 114 Fuzzy pattern algorithm, 20 Gemini, 170 Gender gap in loan outcomes, 79 Gender-based differences, 85 Generalized method of moments (GMM), 275, 279 Germany, 308 GHash. io, 164 Giant technology, 14 Global currencies, 232 Global Digital Asset & Cryptocurrency Association (GDCA), 174 Global Digital Finance, 174 Global equity market factor, 108 Global factors, cryptocurrency pricing by, 108–109 Global financial center, determinants of, 340–348 Global Financial Centres Index, 353 Global Financial Crisis (GFC), 8, 247, 275, 348 Global Financial Development Database, 302 Global financial systems, 342 Global Green Finance Index (GGFI), 352 Goldman Sachs, 351 Google, 16 Governance, 342–344 issues, 156 tokens, 176 Gross domestic product (GDP), 5, 38–39, 46, 274 in developing countries, 38 losses, 46–51 per capita, 22 Growth stocks, 121 criteria for, 121 Hacking, 155 risk of, 156 Hash, 149 function, 150 Hashing power, 161 Health pandemic, 37 Herfindahl–Hirschman index (HHI), 68 High-skilled labor force, 345–346 “Higher-value” US dollar stablecoins, 229 HM Treasury, 202 Hong Kong, 341, 343–344, 347 under China’s tightening grip, 353–354 financial center position, 346 financial system, 345 Hong Kong-dominated East Asian financial markets, 353 Hong Kong Monetary Authority, 354 Host country’s reputation and stability, 347–348 Household size, 86 Human capital, 345 Huobi, 164 Huobi, 209 Illegal transactions, 164 Implied volatility factors, 103 Incentive mechanism, 149 Income, 81 Inflation, 46 Information and communication technologies (ICTs), 14, 208, 340 Information technology, 147, 347 Infrastructure accessibility, 346 Initial coin offerings (ICO), 151 Initial public offerings (IPO), 347 Innovation Diffusion Theory, 17 Innovations, 223 in banking, 203 Innovative technologies, 355 Innovators, 15 Intellectual Property (IP), 20 Intermediaries, 224 International deposit insurance community, 186 International financial centers (IFC), 9, 308–309, 341 International financial firms, 344 International financial regulation of cryptoassets background, 148–150 benefits of tokenization, 154–155 case studies, 156–161 challenges, 155–156 regulatory issues of ABT, 168–176 risks of permissionless DLTS and smart contracts, 161–168 International financial services industry, 341 International Monetary Fund (IMF), 344 International Financial Statistics, 302 International Patent Classification code, 20 International payments, 224 International transaction parties, 237 Internet banking, 17 Interoperability, 156 Interoperable financial innovations, 212 Interventions, selection of, 303–304 Investment banking, 340 business model, 344 Investment banks, 161 IP Business Information (IPBI), 20 Italian Banking Association (ABI), 174–176 Japan, 308 financial crises, 344 Joint logit regression analysis, 248 JP Morgan, 351 Jurisdictions, 200 Kataoka models, 114, 117, 127, 133 data description, 122 diversification constraints, 119–122 mathematical models for portfolio optimization, 116–119 portfolio including cryptocurrency, 131–133 portfolio without cryptocurrency, 122–131 Kenneth French data library, 99 Kenya, 334 controls on inflows released in, 309–311 Kenyan Capital Markets Authority, 309 Know your-client/customer (KYC), 147, 170, 230 CBDC and, 231 exchanges, 170 Kraken, 148 Labor force, 356 participation of women, 86 Labor market performance background and related literature, 275–277 empirical analysis, 283–289 empirical design, 277–283 Large-cap stocks, diversification among, 120 Large-scale cyber-attack, 170 Ledger, 148 Legal risks, 170 Legal tender, 300 Lender-level variables, 68–69 Lex Cryptographia, 209 Liquidity, 247, 259 risks, 245 shocks, 245 Loan application process, 79 Loan distance, financial technology and, 69–71 Loan evaluation process, 79 Loan officers, 79 LoanRange, 67 LocalBitcoins, 164 “Locked up” in smart contract, 226 Logistic regression, 254 Logit regression, 85, 250, 267 London, 341 impacts on London financial center, 348–353 Luxdeco’s issuance, 158 M-Pesa, 206 Machine learning, 14 Markets capitalization of stablecoins, 229 in crypto assets regulation, 230 infrastructure business, 351 Markowitz models, 114, 133 data description, 122 diversification constraints, 119–122 mathematical models for portfolio optimization, 116–119 portfolio including cryptocurrency, 131–133 portfolio without cryptocurrency, 122–131 Mathematical models, 115 Kataoka’s model, 117 mean variance model by Markowitz, 116–117 for portfolio optimization, 116 portfolio optimization with CVaR, 118 Mean variance model by Markowitz, 116–117 Meta, 238 Microsoft, 16 Microstructure of arbitrage, 245 Mid-cap stocks, diversification among, 120 Millicent, 210, 214 network, 210 Mining blocks, 149 concentration, 163–165 pools, 163–165 rewards, 164 Mobile banking, 206 Mobile financial services, 206 Mobile payments, 15, 228 Monetary innovation around blockchain technology, 225–226 Monetary insurance, 354 Monetary policy, 187–188, 203 tools, 192 Monetary sovereignty, 187, 191–192 Money, 226 geopolitical significance of, 235–236 Money Laundering Regulations (MLRs), 212 Mortgages, 154 Moving averages (MA), 130 MA-cross, 131 MPS tokens, 159 Nanopayments, 225 National financial centers, 340 National Security Law (NSL), 353 Negotiation process, 349 Net Asset Value (NAV), 246 Network effects, 190 “New Cold War”, 353 New York, 339–341 Nigeria, 335 CBDC, 193 controls on inflows implemented in, 311–313 Non-financial corporates, 158 Non-KYC exchanges, 164 Non-linear settings, 85 Non-linearities, 275, 286 Non-zero ether, 166 Nonfinancial companies, 16 Nonfungible tokens (NFTs), 148, 150, 213 Nontradeable assets, 154 Novel digital money solutions, 228 stablecoins, 229–230 Numerical riddles, 114 Off-chain/on-chain ABTs, 147, 153, 156, 171 “Off-chain” assets, 147 On-chain currency, 230 “One-size-fits-all” approach, 173 Operational efficiencies, 64 Operational risks, 155–156, 169 Ordinary least square regression (OLS regression), 23, 40 Organisation for Economic Co-Operation and Development (OECD), 150 countries, 282 economies, 282, 286 tax statistics, 279 Over-borrowing, 300 Over-the-counter (OTC), 351 Overstock. com Inc., 159 Pandemic, 4–5 Pandemic uncertainty index (PUI), 46–51 Paris, 341 “Past loser” cryptocurrencies, 97 Patent valuation process, 20 Paycheck Protection Program (PPP), 5, 64, 78 background, 65–66 data, 66–69 financial technology and branch distance, 71 financial technology and competition, 71–72 financial technology and loan distance, 69–71 loan distance and bank competition, 73 results, 69 Payment Systems Regulator (PSR), 212 Payments, 188, 228 evolution of, 7 platforms, 7 process, 226 services, 188–191 systems, 234, 354 PayPal, 228, 234 Pecuniary externalities, 300 Peer-to-peer (P2P), 15 lenders, 15 network, 148 Peernova (Bitcoin companies), 164 People’s Bank of China (PBoC), 193, 233 People’s Republic of China, CBDC, 193–194 Periodic evaluation, 115 Permissioned blockchains, 149, 161 Permissionless DLTS mining pools and mining concentration, 163–165 risks of, 161 risks of smart contracts, 165–168 Physical assets, 151 Placebo simulations, 315–316 Placebo test, 314–316 Point-of-sale (POS), 204 Policy makers, 186 Policy uncertainty, 106 Polkadot, 148 Poloniex, 148 Portfolio without cryptocurrency, 122 including cryptocurrency, 131–133 without diversification constraints, 124–128 rebalancing, with diversification constraints, 128–130 trading based on technical analysis, 130–131 Portfolio optimization with CVaR, 118 mathematical models for, 116–119 problems, 118 Posthumous non-zero ether, 166 Pragmatic adaptation, 349 Price discovery process, 245 Price uncertainty, 106 Private credit, 298 Private equity (PrivateEq), 67 Private equity/venture capital (PE/VC), 155 Private money, 7, 186 Private sector, 83 payment interface providers, 207 Probabilistic linear model (PLM), 79 Probit, 85 Programmable payments, 225 Proof-of-stake (PoS), 149 Proof-of-work (PoW), 149 Proxies for digital financial inclusion, 60 Prudential Regulatory Authority, 202 Public blockchain systems, 230 Public money, 188 Public sector, 83 institutions, 205 Quantitative easing (QE), 274 Real assets, 151 Real-estate, 151, 153 Regional factors, cryptocurrency pricing by, 108–109 Regression, 104 RegTech, 147 Regulation, 212 Reputation of excellent business environment, 356 Reputational damage, 349 Reputational risks, 170 Research and development (R&D), 16 activities, 16 expenses, 27 Resilience of payment markets, 189–190 Retail CBDC, 187, 204, 206 interoperability mechanism, 206 role in UK payments landscape, 204–206 role in wider global financial system, 204–206 Return on equity (ROE), 121 Revolut (Fintech company), 210–211 Risk factors comparison of Cryptocurrency and Equity Market Factors, 100–103 cryptocurrency pricing by equity and crypto factors, 104–108 cryptocurrency pricing by global and regional factors, 108–109 data, 98–100 Russia, 332 controls on outflows removed in, 305–307 Russian payments system, 194 Scalability, 155 Selling (SELL), 267 platforms, 190 Selling and buying (SELL-BUY), 267 Sensitivity analysis, 314–321 (see also Empirical analysis) difference-in-difference, 316–317 identifying assumptions, 317–319 placebo test, 314–316 synthetic difference in difference, 319–321 Singapore, 341, 343–344, 347 financial market, 343 Skepticism, 338 Small Business Administration (SBA), 64 Small businesses, 65–66 Small-and medium-sized enterprises (SMEs), 155 Small-cap stocks, diversification among, 120 Smart contracts, 7, 147–148, 150, 152, 154, 161, 202, 213, 225 languages, 153 risks of, 161, 165–168 Social Cognitive Theory, 17 Social mechanisms, 167 Social networks, 190 Solidity, 153 Sovereign wealth funds, 205 Special Administrative Region (SAR), 353 Special Purpose Vehicle (SPV), 153 SpoondliesTech, 164 Stablecoins, 150, 169, 171, 186, 228, 229–230 StockMarket, 78, 283, 285–286 Stocks, 151 Streaming money, 225 Structural equation modeling (SEM), 39 Student loans, 154 Supervisory review process, 172 Sustainability, 352 Sustainable finance, 353 development, 357 Sveriges Riksbank, 195 Sweden, CBDC, 194–195 Switzerland, 308 Switzerland’s Capital Markets and Technology Association (CMTA), 159 Sybil attack, 161 Syndicated loans, 345 Synthetic control method (SC method), 9, 298 Synthetic difference in difference approach (SDID approach), 299, 319–321 Target Instant Payment Settlement (TIPS), 193 Technical analysis, 115 method, 131 trading based on technical analysis with crypto, 133 Technological innovations, 346 Technological vulnerabilities, 170 Technology, 14, 202 technology-based nonbank competition, 16 Telecommunication, 339 Tether, 186 Third-party intermediaries, 207 Three-factor model, 96 Time-series regressions, 96 Timestamp, 149 Tokenization, 150, 152 benefits of, 154–155 in equity markets, 158–159 Tokenized assets, 154 Tokenized cryptocurrencies, 151 Tokenizing corporate bonds, 157 Tokenizing real assets, 156 Tokenlon, 209 Tokens, 154 (see also Asset-backed tokens (ABTs)) Tracking error, 246 Trading based on technical analysis with crypto, 133 bi-monthly portfolio rebalancing by CVaR model, 140 bi-monthly portfolio rebalancing by Kataoka’s model, 139 bi-monthly portfolio rebalancing by Markowitz model, 138 logarithm of trading volume, 248 numerical results from technical analysis using daily closing price data, 141 volume, 246 Traditional assets, 151, 171 Traditional bank deposits, 7 Traditional bank loans, 79 Traditional contracts, 168 Traditional financial assets, 151 Traditional financial intermediaries, 161 Traditional IFC determinants, 356 “Transfer technologies” of currencies, 224 Trigger solutions, 231–232 trigger solution-based payments, 228 Turkish Lira (TL), 81 Turkish Statistical Institute (TUIK), 79, 83 Two-step Fama–MacBeth procedure, 104 Two-tier remuneration approach, 205 UK banking system, 203 UK case for CBDC, 211–213 UK CBDC models, 205 UK financial system, 202 UK payments landscape, retail CBDC role in, 204–206 UK Supreme Court, 175 UK Uber drivers, 175 Uncertainty factor, 97 Unicorn ETF, 251 Uniswap, 209 United States (US), 14, 233–234 CBDC, 195 Unregulated tokens, 211 Uruguay, CBDC, 195–196 Validation protocol, 153 Value stocks, 121 Variables, 39–42 construction, 249–250 definition, 60–61, 76 and sources, 330 Variance accounted for (VAF), 51 Virtual cards, 15 Volatility factor for cryptocurrencies (VCRIX), 99, 103 Volatility factor for equities (VIX), 99, 103 Wage determination process, 276 Wells Fargo, 351 “Winners-take-all” effect, 247–248, 250–253 World Bank, 39, 158, 226 GFD Database, 39, 274, 279, 289 World Bank’s World Development Indicators (WDI), 279, 302 World Governance Indicators database (WGI database), 279 Yahoo Finance, 122 Zip code level HHI index, 72 Zombie ETF, event studies, 251–259 Book Chapters Prelims Part I: An Overview Chapter 1: An Overview of Fintech, Pandemic and the Financial System: Challenges and Opportunities Part II: Fintech and Pandemic Chapter 2: Banks’ Patenting as an Answer to Emerging Fintech and Bigtech Competition: A Cross-Country Empirical Study Chapter 3: Digital Financial Inclusion: Its Role in Mitigating GDP Losses During the Pandemic Chapter 4: The Role of FinTech in the Paycheck Protection Program Chapter 5: Gender Gap in Consumer Loan Performance: Evidence from Fintech Lending in an Emerging Economy Part III: Cryptocurrency and the Financial System Chapter 6: Cryptocurrencies Meet Equities: Risk Factors and Asset-pricing Relationships Chapter 7: Got Crypto? Evidence from Markowitz, Kataoka, and Conditional Value-at-Risk Models Chapter 8: International Financial Regulation of Cryptoassets and Asset-Backed Tokens Part IV: Central Bank Digital Currency Chapter 9: Central Bank Digital Currencies: The Motivation Chapter 10: A Review of the Proposed Bank of England’s “Retail” Central Bank Digital Currency (CBDC) as a Cryptocurrency Competitor Chapter 11: The Digital Euro From a Geopolitical Perspective: Will Europe Lag Behind? Part V: Economy and the Financial System Chapter 12: The Journey of an Exchange Traded Fund: Becoming a Unicorn or Zombie Chapter 13: Does Finance Benefit Society? Financial Sector Size and Labor Market Performance Chapter 14: On the Effectiveness of Capital Controls: A Synthetic Control Method Approach Chapter 15: Determinants in the Development of Financial Centers: Evolution Around the World Index
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