Research articles for the 2020-03-07
Dependence Structure of Market States
SSRN
We study the dependence structure of market states by calculating empirical pairwise copulas of daily stock returns. We consider both original returns, which exhibit time-varying trends and volatilities, as well as locally normalized returns, where the nonstationarity has been removed. The empirical pairwise copula for each state is compared with a bivariate K-copula. This copula arises from a recently introduced random matrix model, in which nonstationary correlations between returns are modeled by an ensemble of random matrices. The comparison reveals overall good agreement between empirical and analytical copulas, especially for locally normalized returns. Still, there are some deviations in the tails. Furthermore, we find an asymmetry in the dependence structure of market states. The empirical pairwise copulas exhibit a stronger lower tail dependence, particularly in times of crisis.
SSRN
We study the dependence structure of market states by calculating empirical pairwise copulas of daily stock returns. We consider both original returns, which exhibit time-varying trends and volatilities, as well as locally normalized returns, where the nonstationarity has been removed. The empirical pairwise copula for each state is compared with a bivariate K-copula. This copula arises from a recently introduced random matrix model, in which nonstationary correlations between returns are modeled by an ensemble of random matrices. The comparison reveals overall good agreement between empirical and analytical copulas, especially for locally normalized returns. Still, there are some deviations in the tails. Furthermore, we find an asymmetry in the dependence structure of market states. The empirical pairwise copulas exhibit a stronger lower tail dependence, particularly in times of crisis.
Early Causes of Financial Disquiet and the Gender Gap in Financial Literacy: Evidence from College Students in the Southeastern United States
SSRN
Financial literacy, a cornerstone of family economic well-being, is surprisingly low in the United States. The literature has established that financial literacy is lower among women than among men. As sound financial decision-making among both male and female household heads is of paramount importance to well-being, we look to identify the underlying causes that may initiate and perpetuate this differential. We found that a gender-based gap in understanding develops by early college age, before individuals have had the opportunity to develop financial skills through experience or specialization in household roles. The literature indicates that women tend to underestimate their abilities relative to men, particularly in areas of math and financial decision-making. Math ability, financial confidence, and financial and math education have been found to enhance financial literacy, and so we focus on math self-efficacy as an early indicator of financial assurance and literacy. Using an ordered probit model and data from a sample of 529 college students across three institutions in the southeastern United States, we extend the current literature to find that for men, objective math ability drives financial literacy. For women, on the other hand self-efficacy â€" and not objective ability â€" is predictive of financial literacy. Understanding the underlying causes of the gender-based financial literacy gap can inform the creation of better education, family, and cultural intervention methods by which to close this gap in financial literacy, decisions, and outcomes.
SSRN
Financial literacy, a cornerstone of family economic well-being, is surprisingly low in the United States. The literature has established that financial literacy is lower among women than among men. As sound financial decision-making among both male and female household heads is of paramount importance to well-being, we look to identify the underlying causes that may initiate and perpetuate this differential. We found that a gender-based gap in understanding develops by early college age, before individuals have had the opportunity to develop financial skills through experience or specialization in household roles. The literature indicates that women tend to underestimate their abilities relative to men, particularly in areas of math and financial decision-making. Math ability, financial confidence, and financial and math education have been found to enhance financial literacy, and so we focus on math self-efficacy as an early indicator of financial assurance and literacy. Using an ordered probit model and data from a sample of 529 college students across three institutions in the southeastern United States, we extend the current literature to find that for men, objective math ability drives financial literacy. For women, on the other hand self-efficacy â€" and not objective ability â€" is predictive of financial literacy. Understanding the underlying causes of the gender-based financial literacy gap can inform the creation of better education, family, and cultural intervention methods by which to close this gap in financial literacy, decisions, and outcomes.
Exploring the Interplay Between Early Warning Systems’ Usefulness and Basel III Regulation
SSRN
We analyse the ability of credit gap measures to predict banking crises by estimating the usefulness measure conditionally on policymaker's preferences. The results show that the signals based on the credit gap indicators are most useful when the policymaker’s preferences regarding Type I and Type II errors are approximately equal. However, according to the current consensus, the preferences to avoid missing a crisis are higher than issuing a false signal. This means that the usefulness of the credit-gap-based early warning systems is likely to increase once the static Basel III regulative measures are implemented (assuming that their implementation results in lower financial crises’ costs).
SSRN
We analyse the ability of credit gap measures to predict banking crises by estimating the usefulness measure conditionally on policymaker's preferences. The results show that the signals based on the credit gap indicators are most useful when the policymaker’s preferences regarding Type I and Type II errors are approximately equal. However, according to the current consensus, the preferences to avoid missing a crisis are higher than issuing a false signal. This means that the usefulness of the credit-gap-based early warning systems is likely to increase once the static Basel III regulative measures are implemented (assuming that their implementation results in lower financial crises’ costs).
The State of Stablecoins (2018)
SSRN
The report presents new insights and data on stablecoins, an innovative and rapidly evolving sector of the cryptocurrency ecosystem. Stablecoins, as the name suggests, are cryptocurrencies that are designed to minimize price volatility. The findings in this research study are based on the analysis of a new data set for 57 individual stablecoins. The empirical data utilized in the analysis was gathered from both public and non-public sources. The total number of active projects makes stablecoins one of the largest cryptoasset categories, and as shown in the report stablecoins are also a leading category across a number of key metrics (e.g., venture funding). The level of interest and resources devoted to stablecoins is striking and indicates that stablecoins are viewed as a very important part of the digital assets ecosystem. Indeed, stablecoins are often thought of as a foundational layer or infrastructure, one that could significantly expand the cryptoasset userbase from our current estimate of approximately 20-30 million individuals.
SSRN
The report presents new insights and data on stablecoins, an innovative and rapidly evolving sector of the cryptocurrency ecosystem. Stablecoins, as the name suggests, are cryptocurrencies that are designed to minimize price volatility. The findings in this research study are based on the analysis of a new data set for 57 individual stablecoins. The empirical data utilized in the analysis was gathered from both public and non-public sources. The total number of active projects makes stablecoins one of the largest cryptoasset categories, and as shown in the report stablecoins are also a leading category across a number of key metrics (e.g., venture funding). The level of interest and resources devoted to stablecoins is striking and indicates that stablecoins are viewed as a very important part of the digital assets ecosystem. Indeed, stablecoins are often thought of as a foundational layer or infrastructure, one that could significantly expand the cryptoasset userbase from our current estimate of approximately 20-30 million individuals.
