Update Research.md

_pages/Research.md

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 <span style="color:blue"> **SELECTED WORK IN PROGRESS** </span> 
 
- <sub>[**1- Volatility on the Crypto-currency Market: A Copula-GARCH Approach (with Peter G. Moffatt and Andrea Calef**)](https://kensleyblaise.github.io/assets/files/crypto_copula.pdf){:target="_blank"}{:style="color: blue;"}</sub>
+ <sub>[**1- Volatility on the Crypto-currency Market: A Copula-GARCH Approach (with Peter G. Moffatt and Andrea Calef)**](https://kensleyblaise.github.io/assets/files/crypto_copula.pdf){:target="_blank"}{:style="color: blue;"}</sub>
 
  <sub> **Abstract:** </sub>
  <sub> *This research investigates whether crypto-currency returns correlate with expected uncertainty related to the global economy or other risky markets. In a first attempt, we use the copula framework to estimate the dependence magnitude between returns on the crypto-currency market, the interest rate spread, the breakeven inflation and the volatility index from the S&P500 options (VIX). Our results show that uncertainty information about future policy and the state of the economy contained in the interest rate spread bears no importance in crypto-currency price fluctuations. However, we find a pattern, although relatively small, for high estimated crypto-currency returns volatility to overlap with low VIX values. On another level, we find evidence that dynamic time series models can improve our understanding of price fluctuations on the crypto-currency market. We estimate a 5.6 percentage points increase of today's log-returns on the crypto-currency market for each one percentage point increase of yesterday's breakeven inflation. The effect is instantaneous and about 12 percentage points in recent time periods (2020-2022).* </sub>   
 
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+ <sub>[**2- A Theoretical Model of Private Currency Dynamics (with Simone Valente)**](https://kensleyblaise.github.io/assets/files/Theory_Paper.pdf){:target="_blank"}{:style="color: blue;"}</sub>
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+ <sub> **Abstract:** </sub>
+ <sub> *We investigate the role of “private currencies”, also known as “cryptocurrencies”, in the context of a theoretical model.  We first construct a static model in which a crypto-currency is to facilitate real transactions. In this environment, households naturally access fiat currencies through their roles as workers and owners of physical capital. The decision to exchange a portion of their fiat currency holdings for cryptos is contingent upon whether crypto-transactions offer advantages, enabling them to bypass costs associated with conventional money-based transactions in the goods market. Preliminary findings suggest that the volume of goods exclusively purchased using traditional money increases with higher crypto fee rates and lower consumption taxes on money payments. Second, we extend the model to study monetary neutrality in a context where both currencies provide liquidity services.  Our preliminary results suggest that the neo-classical prediction of non-neutrality holds in such a set-up. The latter result contrasts with the empirical literature providing increasing evidence of co-movement between the emerging crypto market and stabilization policies across the globe.* </sub>   
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+  <sub>[**3- Information Disclosures and Price Movements: A High-Frequency Identification Approach (with A. Calef and G. vecchio)**]</sub>
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+ <sub> **Abstract:** </sub>
+ <sub> *We constructed a cross-sectional dataset comprising filings from 10,000 corporations in the United States, sourced from the Securities Exchange Commission historical archives, to examine how firms' information disclosures affect equity prices. Traditionally, the efficient market hypothesis posits that essential information is inherently reflected in asset prices. Our dataset includes each firm's risk assessment and descriptions of investments, including future strategic decisions. We align this textual data with high-frequency equity prices to assess market absorption of information. Specifically, we investigate price movements in a 30-minute window surrounding report releases. Our preliminary findings indicate that the market effectively predicts financial information related to new projects and strategic investments. However, managerial decisions often surprise the market, leading to a 2.14\% price decline when corporations announce layoffs or release deceptive information. Additionally, we observe that information becomes more influential in predicting price movements for companies with lower financial valuation. We interpret this finding as suggesting that higher-valued companies may exhibit greater managerial resilience to changes, thereby reducing market perceptions of risk associated with critical information.* </sub>