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RESEARCH PAPERS

_{1- Volatility on the Crypto-currency Market: A Copula-GARCH Approach.}

_Abstract:

_{This study analyzes the relationship between crypto-currencies, proxied by a Bitcoin (BTC) and Ether (ETH) index, and key macroeconomic variables from April 2013 to May 2024. We focus on US term spread, Volatility Index (VIX), and 5-year breakeven inflation as predictors. Our findings reveal no significant dependence between returns and the term spread, suggesting investors do not consider policy paths or economic cycles when trading crypto-currencies. In contrast, extreme low VIX values are linked to high Crypto-currency Market (CM) volatility, with upper tail dependence estimated at 3.7\% and 7.6\% using Gumbel-Hougaard and Joe copulas, respectively. Our copula modeling exercise also shows a weak correlation of crypto-currency returns with breakeven inflation. Robustness checks, including a sub-sample analysis and variable transformation, confirm these results. We find that while crypto-currencies exhibit weak links to certain financial fundamentals, they respond differently to economic indicators compared to traditional assets, showing increased returns during restrictive monetary policies. The study highlights a need for further research integrating extreme events with dynamic time series analysis to better understand these relationships.}

_{2- Cross-Asset Lead-Lag Dynamics in the Tails.}

_Abstract:

_{TThis study asks whether the risk linkages across U.S. equity sectors behave differently during market crashes and rallies than during normal trading. Using quantile Granger causality tests on 15 assets spanning all major sectors and key benchmarks, we find that cross-sector spillovers are overwhelmingly a tail phenomenon. Predictive linkages are strong during extreme market movements but virtually absent under normal conditions, producing a robust U-shaped pattern across the return distribution. The nature of these spillovers is regime-dependent. Crash-driven predictive linkages dominate during monetary-tightening periods, while symmetric tail spillovers emerge during crisis-recovery episodes. These results suggest that conventional correlation-based risk metrics, which rely on full-sample averages, may understate cross-sector vulnerability during extreme events.}

_{3- A Continuous-time Theory of Currency Substitution.}

_Abstract: _{We analyze the coexistence of cash (fiat money) and privately-issued currencies (crypto-currencies) in a dynamic model where all factors of production are paid in fiat money. This introduces a cash-in-advance constraint that affects both consumption and investment, leading to non-neutrality of money. Crypto-currencies add distortions through labor reallocation and transaction fees. Using flexible utility specifications, we explore the impact of substitutability between money and crypto-purchased goods. Our main result is that an increase in the money supply raises inflation and shifts labor allocation, affecting growth dynamics. While broader economic variables remain stable, real wages are highly sensitive to changes in consumer preferences and crypto-fees, underscoring the impact of private digital currencies on the economy’s long-term trajectory.}

_{4- Currency Competition and Monetary Non-neutrality (with Simone Valente).}

_Abstract: _{We build a theoretical model where both fiat money and crypto-currencies are used as media of exchange for differentiated goods. Crypto-currencies offer pecuniary benefits, such as avoiding consumption taxes, and non-pecuniary benefits like transaction privacy, while non-users face utility losses that grow with available goods. We identify an endogenous threshold good where consumers are indifferent between government-backed money and privately-issued currency, leading to three equilibrium scenarios: all goods purchased with fiat, all with crypto, or a mix of both. Our model predicts that, while fiat money is neutral, crypto-currencies are non-neutral due to mining costs, which affect labor allocation. *}

_{5- A Direct Test of the Fundamental Assumption of Option Pricing Models using Big Data Analytics
(with Peter G. Moffatt and Anthony Rezitis)}

_Abstract: _{The most fundamental assumption underlying option pricing models is that the market price of an option is equal to the discounted expected value of the final payoff. In this paper, we test this assumption directly with data on market prices of options combined with data on realised final payoffs. The data set contains around 25 million European options written on the S&P500 index, between January 2022 and December 2023, with expiry dates ranging from January 2022 to July 2025. Only “near-the-money” options are included in the sample. The framework for testing the hypothesis of interest is a heteroscedastic regression model with discounted actual payoff at expiry as the dependent variable, and market price of the option as the independent variable. The joint hypothesis under test is essentially that the intercept is zero and the slope is one. This hypothesis is tested both parametrically and non-parametrically. As well as being tested for the entire sample, it is tested separately for call options and put options. It is also tested separately for bull-market phases and bear-market phases. The fundamental assumption is always strongly rejected, usually with the intercept being different from zero but the slope not being different from one. We conclude that a useful way of judging the performance of an option pricing model is to compare computed option valuations to discounted final payoffs, rather than to market prices.}

_{6- Using Digital Records and Machine Learning to Detect Tax Avoidance in Customs Declarations (with N. Angelopoulos and R.C. Vincent.}

_Abstract: _{Trade taxation remains a central source of public revenue in low-capacity states, yet customs administrations often exhibit weak and uneven enforcement. In this paper, we investigate an under-explored margin of tax evasion: strategic selection among customs offices within a single country. We ask whether importers exploit spatial heterogeneity in enforcement by routing otherwise identical imports through offices where the expected cost of evasion is lower. We assembled comprehensive administrative data on more than 2.16 million import declarations in Haiti from 2017 to 2024. Because Haiti applies a uniform national tariff schedule, systematic differences in reporting behaviour across offices reflect differences in implementation rather than legal rules. Our empirical strategy exploits within-firm and within-product variation to estimate office-specific elasticities of declared import values with respect to ad–valorem tax rates. We find a large and economically meaningful response of declared values to taxes, with an elasticity close to minus one. Allowing the tax response to vary by office reveals substantial enforcement heterogeneity: secondary ports exhibit tax elasticities that are 2 to 3 times larger than those at the main port. These patterns persist under common-support restrictions and within-firm specifications that compare the same importer clearing the same product at the same time through different offices. We address concerns about endogenous office choice and measurement error with a re-centred exposure instrumental-variables strategy based on broker routing networks interacting with product–time tax intensity. The IV estimates closely mirror the baseline results. The findings show that decentralised customs administration generates exploitable enforcement heterogeneity, with important implications for revenue mobilisation and customs reform in fragile states.}

Kensley Blaise