Blockchain and Decentralized Finance | Financial Innovation| Machine Learning | Payments
Blockchain and Decentralized Finance
Abstract: Blockchain-based decentralized exchanges (DEXes) are a pre-requisite for the nascent decentralized finance ecosystem. The most successful form of DEX in terms of trading volume are swap exchanges, smart contracts that pool liquidity and that price transactions with a deterministic function. Almost all swap exchanges use a pricing rule that is conceptually problematic as it gives rise to intrinsically profitable front-running opportunities as well as cross-DEX arbitrage. In practice, these shortcomings cause significant network congestion, a negative externality that raises costs for all users and that threatens the long-term viability of the blockchain ecosystem. Calibrated to a low liquidity but frequently used trading pair on UniSwap, the largest DEX for much of 2020/21, about 14% of transactions see an implicit excess cost of at least 50bps, which is orders of magnitude larger than the common trading costs for this pair on centralized exchanges. I further present an alternative pricing rule, based on insights from the market microstructure literature, that does not suffer from these flaws.
Executive Summary:
Today’s global economic digitization of society, driven by technology trends, continues to advance at exponential speeds. Billions of Internet of Things devices have already made their way into our daily lives, our homes and cars, but also into health care, manufacturing, supply-chains and other infrastructure. This development is in sharp contrast to the financial sector which still operates on legacy infrastructure(s). The net-effect is that current systems of payment lack the flexibility to adapt to the digitization of the economy. They remain slow, clunky, and expensive; often one receives a digital service, or even physical goods, faster than the merchant receives the payment. Further, the emergence of Decentralized Finance, through blockchain technology, has already demonstrated a capacity to disrupt the financial sector, impact national sovereignty, and affect established monetary transmission channels. Hence, it is no surprise that nations and tech-firms are now building new digital infrastructures for finance, banking, and payments that circumvent those legacy practices. Governments around the globe equivalently find themselves in an awkward position. On the one hand, monetary policies rely on the established functions of the financial sector. For many decades, banks have conveniently served as deputies in enacting those policies, along with efforts to squash money laundering, tax evasion, and the financing of terrorism. On the other hand, over the past decade, governments have publicly recognized the need to enable digital innovation to keep their economies competitive. Further, they acknowledge the responsibility to enable their citizens to protect their privacy from unabridged data harvesting, and the need for financial inclusion in core economic national activities, irrespective of means and location. Finally, economies such as Canada’s risk that their home currency is displaced, or their national security gets severely compromised, if consumers and businesses alike flee to a more convenient, let alone foreign, digital payments alternative. Against this backdrop, in recent years many central banks have raced to explore, research and test the issuance of digitally native money, or Central Bank-issued Digital Currencies (CBDCs), in an effort to rediscover the very essence and use of “fiat cash”. The Bank of Canada (BoC) has emerged as a thought leader on CBDCs at an international level having spent almost a decade and significant resources on this endeavour. The Bank is now preparing to put itself in a position where it can issue a digital loonie should certain conditions mandate it. As the BoC has been contemplating the design of a CBDC for some time, given the scale of the particular enterprise, it wanted to sample ideas at arm’s length. To do this, in early 2020, the Bank ran a competition among universities to research and propose a CBDC design. Being a finalist in this competition, this manuscript presents a design proposal for a Central Bank Digital Loonie (CBDL) based on careful academic research of the possible technological, legal, and economic components of such an unprecedented and historic expedition.
Additional Resources:
https://slides.com/ap248/cdbl-report/
https://www.youtube.com/playlist?list=PLTmzBTSqnXduG8kmgSYUx0eblM50BQcFw
Executive Summary:
Facebook’s cryptocurrency project Libra -- and the associated impacts on banks, central banks, the global payments and trading system, and others -- hinges on the widespread and global adoption of the digital currency, especially amongst young people in emerging markets.
To assess the likelihood of Libra adoption, we deploy a survey of 5000+ respondents in the U.S., India, and Nigeria, asking whether respondents are prepared to use money that has been issued by a technology company, and whether, if they own a business, they are willing to accept such money.
Development of reference rates fro crypto assets
Financial related microsurveys
Abstract: A Call to Arms for PayTech: The Future of Payment in Canada
The following is based upon three pillars. First, it incorporates themes and insights from a two-hour roundtable discussion held at the Rotman School of Management on February 22nd, 2019 composed of leading investors, businesses, regulators, and academics. Chatham House rules were in effect. Second, several interviews were undertaken to further develop these themes and insights. Third, additional research was undertaken to complement the contents herein.
Andreas Park and Katya Malinova, Tokenomics: When Tokens Beat Equity (Paper, Medium Article, Lecture)
Abstract: In an initial coin offering, investors fund a venture in exchange for tokens that grant rights to future economic output. To many financial industry insiders, tokens have no intrinsic merit and exist only as a way to evade regulations. We demonstrate that generic revenue-based token contracts are indeed economically inferior to equity and lead to over- or under-production. However, an optimally designed token contract, which is a combination of an output presale and an incremental revenue sharing agreement, yields the same payoffs as equity. Moreover, with entrepreneurial moral hazard, tokens can finance a strictly larger set of ventures than equity.
Financial Innovation
Executive Summary:
Throughout the COVID-19 pandemic we saw a surge of interest in stock market trading by retail customers, along with the emergence of “meme” stocks, TikTok investment advice, and the gamification of trading by startup brokerages. There are, however, no detailed, reliable data on who the new investors are and on what motivates them.
We use a unique, random, anonymous engagement approach among the US Web-using public in the first half of 2021 to reach over 1,600 first-time investors and almost 2,500 established investors. This approach ensures broad reach amongst those not typically or sufficiently included in traditional surveys, including young people. For more on the method, see RIWI (TSXV: RIWI).
The resulting data confirm that new investors tend to be younger and lower income, and that they tend to make riskier investments, have a shorter-term investment horizon, and get their investment information from non-standard sources including social media such as Reddit. The data also challenge conventional wisdom and raise concerns about these next-generation investors. While the focus of the public discussion tends to be on younger investors, these data show that new investors in the over-35 years old age group are particularly susceptible to engage in riskier behavior and obtain information from non-standard sources.
Abstract: From 2010--2015, a group of convicted traders accessed earnings information hours before their public release by hacking several major newswire services. We use their "insider" trading as a natural experiment to investigate how efficiently markets incorporate private information in prices. 15% of a firm’s earnings surprise was incorporated into its stock price prior to its public release when the hackers had access to non-public information. Volume and spread-based measures of informed trading detect this activity, but order flow-based measures do not. We find evidence that uninformed, professional traders traded in the same direction, amplifying the impact of informed trading.
Machine Learning
Abstract: Machine learning tools have been developed to generate synthetic data sets that are indistinguishable from available historical data. In this paper, we investigate whether the tools can be used for stress testing. In particular we test whether synthetic data can be used to provide reliable risk measures when the confidence levels are high. Our results are encouraging and suggest that synthetic data produced from the most recent 250 days of historical data are potentially useful for determining regulatory market risk capital requirements.
Abstract: A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). A disadvantage of MCA is that some information in the volatility surface is lost during the calibration process and the prices of exotic options will not in general be consistent with those of plain vanilla options. We consider an alternative approach where the structure of the user’s preferred model is preserved but points on the volatility are features input to a neural network. We refer to this as the volatility feature approach (VFA) model. We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast.
Abstract: A volatility surface is an important tool for pricing and hedging derivatives. The surface shows the volatility that is implied by the market price of an option on an asset as a function of the option’s strike price and maturity. Often, market data is incomplete and it is necessary to estimate missing points on partially observed surfaces. In this paper, we show how variational autoencoders can be used for this task. The first step is to derive latent variables that can be used to construct synthetic volatility surfaces that are indistinguishable from those observed historically. The second step is to determine the synthetic surface generated by the latent variables that fits available data as closely as possible. The synthetic surfaces produced in the first step can also be used in stress testing, in market simulators for developing quantitative investment strategies, and for the valuation of exotic options. We illustrate our procedure
Abstract: This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the basic reinforcement learning approach in a number of ways. First, it uses two different Q-functions so that both the expected value of the cost and the expected value of the square of the cost are tracked for different state/action combinations. This approach increases the range of objective functions that can be used. Second, it uses a learning algorithm that allows for continuous state and action space. Third, it compares the accounting P&L approach (where the hedged position is valued at each step) and the cash flow approach (where cash inflows and outflows are used). We find that a hybrid approach involving the use of an accounting P&L approach that incorporates a relatively simple valuation model works well. The valuation model does not have to correspond to the process assumed for the underlying asset price.
Abstract: We employ neural networks to understand volatility surface movements. We first use daily data on options on the S&P 500 index to derive a relationship between the expected change in implied volatility and three variables: the return on the index, the moneyness of the option, and the remaining life of the option. This model provides an improvement of 10.72% compared with a simpler analytic model. We then enhance the model with an additional feature: the level of the VIX index prior to the return being observed. This produces a further improvement of 62.12% and shows that the expected response of the volatility surface to movements in the index is quite different in high and low volatility environments.
Payments
Executive Summary:
This report is a summary of current events in the Payments Industry based on publicly available information sources. With its focus on key issues and on country/regional comparisons, it provides a useful launching pad for students and working professionals interested in this dynamic industry. Given the rapid pace of change, even those already quite familiar with aspects of the industry may benefit from a broader perspective and from an update on international developments. A cut-off date for new information has to be selected for compiling such a report and the end of May 2021 was chosen.
Each section of the report begins with an overall international perspective and comparison, followed by country/regional updates about a particular topic. Countries with highly advanced financial infrastructures such as the UK, the US and Canada are compared with less financially developed regions such as India, Africa and South America. Also included are Sweden, China and Japan, in an attempt to consider interesting examples while maintaining a broad perspective. Selected topics make clear the rapid pace of change in the Payments industry as well as the presence of significant country/regional disparities. Depending on the interests of the reader, the report could be reviewed by topic or by country/region.
Abstract: A Call to Arms for PayTech: The Future of Payment in Canada
The following is based upon three pillars. First, it incorporates themes and insights from a two-hour roundtable discussion held at the Rotman School of Management on February 22nd, 2019 composed of leading investors, businesses, regulators, and academics. Chatham House rules were in effect. Second, several interviews were undertaken to further develop these themes and insights. Third, additional research was undertaken to complement the contents herein.