Speech Title: "Adoption and Risk Management of Decentralized Fintech Platforms"

DateThursday, May 4th

Abstract:  Blockchain is a distributed ledger technology built around the principles of transparency and decentralization. Initially conceived as the backbone technology to enable cryptocurrency payments, blockchain has expanded to support a large class of business applications, ranging from financial to supply chain services. In this talk, we argue that the current design of smart contract enabling blockchains may severely limit the adoption of decentralized financial (DeFi) innovations. We begin by discussing adoption of automated Market Makers (AMMs), the most prominent class of blockchain-based decentralized exchanges (DEXs). We show that limited adoption arises because of the priority fee order-processing mechanism and the liquidity pooling nature of DEXs. We then discuss allocative inefficiencies of existing blockchains, and the operational risks arising from the transparent observability of transactions, which exposes users to the risk of being frontrun by malicious actors. We show that existing solutions based on private off-chain communication channels designed to mitigate miner extractable value cannot fully align the economic incentives of users and validators, and thus do not yield allocative efficiency. We conclude with a brief highlight of adoption and risk management challenges faced by DeFi supporting blockchains.

Speech Title: "Statistical Consequences of Fat Tails"

Date: Friday, May 5th

Abstract:  A talk around topics from Statistical Consequences of Fat Tails: Real World Epistemology, Preasymptotics, and Applications (2020). The monograph investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible. 

Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress". Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under of the "laws of the medium numbers" --which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. 
A few examples: 

• The sample mean is rarely in line with the population mean, with effect on "naive empiricism", but can be sometimes be estimated via parametric methods.
• The "empirical distribution" is rarely empirical.
• Parameter uncertainty has compounding effects on statistical metrics. 
• Dimension reduction (principal components) fails. 
• Inequality estimators (GINI or quantile contributions) are not additive and produce wrong results. 
• Many "biases" found in psychology become entirely rational under more sophisticated probability distributions 
• Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. 

Speech Title: "Regulating Crypto"

Date:  Saturday, May 6th

Abstract:  The products and process used in crypto present numerous operational risks that are entirely new to financial markets. Last year the attack on Terra’s algorithmic stablecoin, the collapse of FTX and the subsequent contagion throughout the ecosystem could have marked a turning point.  Instead, the price of bitcoin rose by 43% in January 2023. Price and volume manipulation is present in every type of crypto market. Most trading of crypto assets happens on platforms that have no external regulation whatsoever -- Ponzi schemes and rug pulls are rife, yet projects that build on the honest principles of decentralized finance are commonly defeated by pump-and-dump, and most retail traders have no idea that their collateral account could be wiped out entirely by the exchange itself, without any warning. Following an overview of crypto products, processes and risks I focus on the extraordinary, dangerous and completely unregulated growth of Binance and tether.