Research articles for the 2021-08-03
arXiv
Democracy is widely believed to contribute to economic growth and public health. However, we find that this conventional wisdom is no longer true and even reversed; democracy has persistent negative impacts on GDP growth since the beginning of this century. This finding emerges from five different instrumental variable strategies. Our analysis suggests that democracies cause slower growth through less investment, less trade, and slower value-added growth in manufacturing and services. For 2020, democracy is also found to cause more deaths from Covid-19.
SSRN
It was once conventional wisdom that lenders routinely influenced corporate managers’ decision making. Covenants constrained borrower risk taking and compelled specific affirmative obligations to protect lenders. Recent policy discussion, however, laments loan markets’ turn to various forms of high-risk lending. So-called leveraged loans â€" relatively risky, below-investment-grade loans â€" more than doubled in outstanding dollar terms, growing from about $550 billion in 2010 to $1.2 trillion by 2019. These risky loans have taken up a larger and larger share of the loan markets over time. More leveraged loans are also “covenant-lite,†issued without traditional financial maintenance covenants. And regulators worry about “add-backs†â€" borrowers’ growing practice of making upward adjustments to projected earnings that tend to weaken leverage constraints.Moreover, bank regulatory changes have incentivized “originate-to-distribute†loan syndications that enable non-bank lenders to hold and trade leveraged loans too risky for banks to keep. Syndicated lending now involves greater and greater participation by nonbank or “institutional†lenders like hedge funds, CLOs (collateralized loan obligations), and mutual funds. Commentators worry about the new species of risky loans, with their dearth of traditional covenants and the fewer instances of lender intervention, which may portend instability in debt markets. At the same time, weakened covenant protections may lead to weakened corporate governance.In this Article, I respond to these fears, arguing that they may be overblown. The increasing share of leveraged and covenant-lite loans may not necessarily evidence undisciplined debt issuance. Many seemingly troublesome loans are issued as subparts of deals that include loans with traditional covenants and cross-default provisions, which effectively constrain borrower behavior. Though add-backs may increase firm leverage, they may also improve the informativeness of earnings-based financial covenants. In addition, while the incidence of loan covenant violations has dropped dramatically across U.S. public firms, recent research suggests that covenants have become more efficient. In effect, covenants are doing more with less. Financial covenants have generally become less restrictive and more discriminating in differentiating distress from non-distress situations.
arXiv
This paper presents the advantages of alternative data from Super-Apps to enhance user' s income estimation models. It compares the performance of these alternative data sources with the performance of industry-accepted bureau income estimators that takes into account only financial system information; successfully showing that the alternative data manage to capture information that bureau income estimators do not. By implementing the TreeSHAP method for Stochastic Gradient Boosting Interpretation, this paper highlights which of the customer' s behavioral and transactional patterns within a Super-App have a stronger predictive power when estimating user' s income. Ultimately, this paper shows the incentive for financial institutions to seek to incorporate alternative data into constructing their risk profiles.
arXiv
In the context of traditional life insurance, the future discretionary benefits ($FDB$), which are a central item for Solvency~II reporting, are generally calculated by computationally expensive Monte Carlo algorithms. We derive analytic formulas for lower and upper bounds for the $FDB$. This yields an estimation interval for the $FDB$, and the average of lower and upper bound is a simple estimator. These formulae are designed for real world applications, and we compare the results to publicly available reporting data.
RePEC
How do financial frictions affect firm dynamics, allocation of resources across firms, and aggregate productivity and output? Is the nature of productivity shocks that firms face primary for the effects of financial frictions? I first use a comprehensive dataset of Spanish firms from 1999 to 2014 to estimate non-parametrically the firm productivity dynamics. I find that the productivity process is non-linear, as persistence and shock variability depend on past productivity, and productivity shocks are non-Gaussian. These dynamics differ from the ones implied by a standard AR(1) process, commonly used in the firm dynamics literature. I then build a model of firm dynamics with financial frictions in which productivity shocks are non-linear and non-Gaussian. The model is consistent with a host of evidence on firm dynamics, financial frictions, and firms’ financial behaviour. In the model economy, financial frictions affect the firm life cycle. Without financial frictions, the size of an entrant firm will be three times larger. Furthermore, profit accumulation, which allows firms to overcome financial frictions, is slow, and it only speeds up when firms are mature. As a consequence, the average exiting firm is smaller than it would be without financial frictions. The aggregate consequences of financial frictions are significant. They result in misallocation of capital and reduce aggregate productivity by 16%. This figure is only 8% if productivity dynamics evolve according to a standard AR(1) process.
SSRN
In this short paper, we outline geometric and arithmetic compound formulae for interest rate swaps. We also present ISDA protocol when compounding with a floating spread.
arXiv
Most trading in cryptocurrency options is on inverse products, so called because the contract size is denominated in US dollars and they are margined and settled in crypto, typically bitcoin or ether. Their popularity stems from allowing professional traders in bitcoin or ether options to avoid transferring fiat currency to and from the exchanges. We derive new analytic pricing and hedging formulae for inverse options under the assumption that the underlying follows a geometric Brownian motion. The boundary conditions and hedge ratios exhibit relatively complex but very important new features which warrant further analysis and explanation. We also illustrate some inconsistencies, exhibited in time series of Deribit bitcoin option implied volatilities, which indicate that traders may be applying direct option hedging and valuation methods erroneously. This could be because they are unaware of the correct, inverse option characteristics which are derived in this paper.
arXiv
A common issue when analyzing real-world complex systems is that the interactions between the elements often change over time: this makes it difficult to find optimal models that describe this evolution and that can be estimated from data, particularly when the driving mechanisms are not known. Here we offer a new perspective on the development of models for time-varying interactions introducing a generalization of the well-known Kinetic Ising Model (KIM), a minimalistic pairwise constant interactions model which has found applications in multiple scientific disciplines. Keeping arbitrary choices of dynamics to a minimum and seeking information theoretical optimality, the Score-Driven methodology lets us significantly increase the knowledge that can be extracted from data using the simple KIM. In particular, we first identify a parameter whose value at a given time can be directly associated with the local predictability of the dynamics. Then we introduce a method to dynamically learn the value of such parameter from the data, without the need of specifying parametrically its dynamics. Finally, we extend our framework to disentangle different sources (e.g. endogenous vs exogenous) of predictability in real time.
We apply our methodology to several complex systems including financial markets, temporal (social) networks, and neuronal populations. Our results show that the Score-Driven KIM produces insightful descriptions of the systems, allowing to predict forecasting accuracy in real time as well as to separate different components of the dynamics. This provides a significant methodological improvement for data analysis in a wide range of disciplines.
RePEC
For decades, the monetary economics literature has considered multiple deposit expansion via the money multiplier logic as empirically corroborated. However, the developments witnessed in advanced economies since the Global Financial Crisis challenged this settled view, and central banks as well as the Bank for International Settlements were among the first to openly reconsider it. In this paper, we revisit the issue empirically, but in a way aligned with a 'narrative' context of the evolving institutional frameworks for banking activities and monetary policy that profoundly and ultimately shape it out. Using a vector autoregression model estimated on Russian monthly data over two subsamples, 2005-2012 and 2012-2019, we find robust evidence that, while multiple deposit expansion may have existed in underdeveloped financial systems in the past, where the volume of lending was limited by the supply of bank reserves, nowadays lending is constrained mainly by the demand for credit. The key explanations we propose are: the rapid rise of money markets in the 20th- 21st centuries, the unlimited access to central bank liquidity provision facilities, and the evolution of bank management from the 'golden rule' of banking, where liquidity gaps aim at zero, to Asset and Liability Management, where banks flexibly manage liquidity gaps. Our results robustly show that the influence on real money balances of money supply factors, such as bank reserve requirements and the real monetary base, has become statistically insignificant over the recent decade in Russia, while that of money demand factors, such as the nominal interest rate, has remained significant and negative, which is consistent with the economic intuition we have suggested.
SSRN
Millions of homeowners are having difficulty affording their monthly mortgage payments because of the COVID-19 pandemic. The most vulnerable group of borrowers is concentrated within the Federal Housing Administration (FHA) program, with over 900,000 borrowers who are more than 90 days delinquent. Research provides clear evidence that immediate and substantial payment reduction is the most effective mortgage default prevention tool available. This paper suggests several changes to the FHA COVID-19 Home Retention Options that FHA should consider to provide more borrowers with deeper payment reductions, many at no cost to the Mutual Mortgage Insurance Fund, that would provide borrowers in need of financial assistance with greater eligibility as well as more substantial payment reductions that help them retain their home and avoid foreclosure. We estimate the incremental payment relief and resulting reduction in foreclosures that would be created by each of our proposed changes, and demonstrate how our suggestions would increase the availability of mortgage modifications for borrowers with lower incomes and higher debt-to-income ratios, particularly Black and Hispanic borrowers.
SSRN
This paper describes a novel methodology of measuring risky and conservative mortgage credit using household survey data for 18 European Union countries and the United Kingdom. In addition, we construct time series for both types of credit and embed them into a global vector autoregressive (GVAR) model, so as to study how shocks to both variables affect domestic output and propagate across countries through cross-border banking exposures. The results show that a decrease in risky credit can have long-lasting positive effects on GDP, both in the originating country and its most exposed peers, while a fall in conservative credit is detrimental. In some geographies, negative shocks to both types of credit reduce output, a feature linked to the lower relevance of homeownership which implies that mortgage credit plays a less prominent role in the domestic economy.
SSRN
Smart Contracts (SCs) are usually defined as contracts that are instantiated in computer-executable code that automatically executes all or parts of an agreement with the assistance of block-chain’s distributed trust technology. This is principally a technical description and results in an overly narrow focus. The goal of this paper is to provide an overview of the rapidly evolving multidisciplinary literature on Smart Contracts to provide a synthesis perspective on the economic implications of smart contracts. This necessitates casting a wider-net that ties SCs to the literature on the economics of AI and the earlier Industrial Organization literature to support speculation about the role of SCs in the evolution of AI and the organization of economic activity. Accomplishing this goal builds on a repurposing of the Internet hourglass model that puts SCs at the narrow waist between the real (non-digital) and virtual (digital) realms, serving as the connecting glue or portal by which AIs may play a larger role in controlling the organization of economic activity.
arXiv
Although it has been well accepted that the asymptotic covariance matrix of maximum likelihood estimates (MLE) for complete data is given by the inverse Fisher information, this paper shows that when the MLE for incomplete data is derived using the EM algorithm, the asymptotic covariance matrix is however no longer specified by the inverse Fisher information. In general, the new information is smaller than the latter in the sense of Loewner partial ordering. A sandwich estimator of covariance matrix is developed based on the observed information of incomplete data and a consistent estimator of complete-data information matrix. The observed information simplifies calculation of conditional expectation of outer product of the complete-data score function appeared in the Louis (1982) general matrix formula. The proposed sandwich estimator takes a different form than the Huber sandwich estimator under model misspecification framework (Freedman, 2006 and Little and Rubin, 2020). Moreover, it does not involve the inverse observed Fisher information of incomplete data which therefore notably gives an appealing feature for application. Recursive algorithms for the MLE and the sandwich estimator of covariance matrix are presented. Application to parameter estimation of regime switching conditional Markov jump process is considered to verify the results. The simulation study confirms that the MLEs are accurate and consistent having asymptotic normality. The sandwich estimator produces standard errors of the MLE which are closer to their analytic values than those provided by the inverse observed Fisher information.
SSRN
We examine the diversification benefits of cryptocurrency asset categories. To mitigate the effects of estimation risk, we employ the Bayes-Stein model with no short-selling and variance-based constraints. We estimate the inputs using lasso regression and elastic net regression, employing the shrunk Wishart stochastic volatility model and Gaussian random projection. We consider nine cryptocurrency asset categories, and find that all but two provide significant out-of-sample diversification benefits. The lower is investor risk aversion, the more beneficial are cryptocurrencies as portfolio diversifiers. During uncertain economic environments, such as the post-COVID-19 period, cryptocurrencies provide the same diversification benefits as in more stable environments. Our results are robust to different portfolio benchmarks, regression technique, transaction cost, portfolio constraints, higher moments and Black-Litterman models.
arXiv
We analyse high-frequency realised volatility dynamics and spillovers in the bitcoin market, focusing on two pairs: bitcoin against the US dollar (the main fiat-crypto pair) and trading bitcoin against tether (the main crypto-crypto pair). We find that the tether-margined perpetual contract on Binance is clearly the main source of volatility, continuously transmitting strong flows to all other instruments and receiving only a little volatility. Moreover, we find that (i) during US trading hours, traders pay more attention and are more reactive to prevailing market conditions when updating their expectations and (ii) the crypto market exhibits a higher interconnectedness when traditional Western stock markets are open. Our results highlight that regulators should not only consider spot exchanges offering bitcoin-fiat trading but also the tether-margined derivatives products available on most unregulated exchanges, most importantly Binance.
arXiv
We study the impact of climate volatility on economic growth exploiting data on 133 countries between 1960 and 2005. We show that the conditional (ex-ante) volatility of annual temperatures increased steadily over time, rendering climate conditions less predictable across countries, with important implications for growth. Controlling for concomitant changes in temperatures, a +1oC increase in temperature volatility causes on average a 0.9 percent decline in GDP growth and a 1.3 percent increase in the volatility of GDP. Unlike changes in average temperatures, changes in temperature volatility affect both rich and poor countries.
