Research articles for the 2020-02-04
arXiv
Understanding disaggregate channels in the transmission of monetary policy to the real and financial sectors is of crucial importance for effectively implementing policy measures. We extend the empirical econometric literature on the role of production networks in the propagation of shocks along two dimensions. First, we set forth a Bayesian spatial panel state-space model that assumes time variation in the spatial dependence parameter, and apply the framework to a study of measuring network effects of US monetary policy on the industry level. Second, we account for cross-sectional heterogeneity and cluster impacts of monetary policy shocks to production industries via a sparse finite Gaussian mixture model. The results suggest substantial heterogeneities in the responses of industries to surprise monetary policy shocks. Moreover, we find that the role of network effects varies strongly over time. In particular, US recessions tend to coincide with periods where between 40 to 60 percent of the overall effects can be attributed to network effects; expansionary economic episodes show muted network effects with magnitudes of roughly 20 to 30 percent.
SSRN
We use a GARCH dummy model to study the influence of calendar effects on daily conditional returns and volatility of Bitcoin during the period 2013â€"2019. The Halloween, day-of-the-week (DOW), and month-of-the-year (MOY) effects are analyzed. Our results reveal no evidence of a Halloween calendar anomaly. A classical DOW effect is not present in Bitcoin returns, however, we find significantly lower risk over the weekend whilst in the beginning of the week Bitcoin's volatility is more intense. Moreover, supporting evidence of a reverse January effect is detected. Our results also show that investors’ risk drops substantially in September.
arXiv
We develop a target zone model with realistic features such as finite exit time, non-stationary dynamics and heavy tails. Our rigorous characterization of risk corresponds to the dynamic counterpart of a mean-preserving spread. We explicitly solve for both stationary and transient exchange rate paths, and show how they are influenced by the distance to both the time horizon and the target zone bands. This enables us to show how central bank intervention is endogenous to both the distance of the fundamental to the band and the underlying risk. We discuss how the credibility of the target zone is shaped by the set horizon and the degree of underlying risk, and we determine a minimum time at which the required parity can be reached. We prove that the interplay of the diffusive component and the destabilizing risk component can yield an endogenous regime shift characterized by a threshold level of risk above which the target zone ceases to exist. All the previous results cannot obtain by means of the standard Gaussian and affine models. We recover by numerical simulations the different exchange rate densities established by the target zone literature.
arXiv
Proof of Stake (PoS) is a burgeoning Sybil resistance mechanism that aims to have a digital asset ("token") serve as security collateral in crypto networks. However, PoS has so far eluded a comprehensive threat model that encompasses both Byzantine attacks from distributed systems and financial attacks that arise from the dual usage of the token as a means of payment and a Sybil resistance mechanism. In particular, the existence of derivatives markets makes malicious coordination among validators easier to execute than in Proof of Work systems. We demonstrate that it is also possible for on-chain lending smart contracts to cannibalize network security in PoS systems. When the yield provided by these contracts is more attractive than the inflation rate provided from staking, stakers will tend to remove their staked tokens and lend them out, thus reducing network security. In this paper, we provide a simple stochastic model that describes how rational validators with varying risk preferences react to changes in staking and lending returns. For a particular configuration of this model, we provide a formal proof of a phase transition between equilibria in which tokens are predominantly staked and those in which they are predominantly lent. We further validate this emergent adversarial behavior (e.g. reduced staked token supply) with agent-based simulations that sample transitions under more realistic conditions. Our results illustrate that rational, non-adversarial actors can dramatically reduce PoS network security if block rewards are not calibrated appropriately above the expected yields of on-chain lending.
SSRN
This paper investigates whether ECB corporate sector purchases impact the funding structure of non-financial corporates. Regression models are estimated using a unique microdata panel, combining data on all Eurosystem corporate sector purchases and individual balance sheets of 672 non-financial corporations headquartered in the euro area with access to capital markets. The findings indicate that ECB purchases of corporate bonds reduce the dependence on bank financing of corporates whose debt is purchased. The effects vary according to corporates’ interest paid, financial expenses and price-to-book ratio. In addition, this paper shows that the relationship between central bank purchases and corporates’ dependence on bank financing is non-linear. The downward effect on bank dependence is largest for those corporates of which most debt is purchased under the CSPP, relative to their total stock of debt.
SSRN
We study whether digital technology streamlines the regulatory process and reduces the costs of complying with regulation. To identify the effect of digital technology on regulatory compliance costs, we leverage a quasi-experimental policy change which mandates the use of an internet-based flow management tool that enables insurers and regulators to exchange policy form and rate filing information. We find that digitization lowers the costs of complying with regulation. The average insurer per line of business and year in the highest quartile regarding the proportion of business under the mandate saves 5.4 percent of general expenses. Our results also suggest a fixed cost of adopting the technology, with larger cost-saving accruing to firms that adopt the new technology more widely.
SSRN
Hedging techniques have been widely adopted in market-consistent or fair valuation approach required by recent solvency regulations, to take into account the market prices of the hedgeable parts of insurance liabilities. In this study, we investigate the fair dynamic valuation of insurance liabilities, which are model-consistent (mark-to-model), market-consistent (mark-to-market), and time-consistent, as proposed by Barigou et al. (2019) in a multi-period setting. We introduce the loss averse convex hedging technique, which `punishes' loss outcomes more than gain outcomes. We prove that fair dynamic valuations are equivalent to the class of loss averse convex hedge-based valuation. Moreover, we propose and provide a complete characterization of loss averse mean-variance hedging and show how to implement loss averse mean-variance hedge-based dynamic valuations using numerical examples.
SSRN
Using fourteen years of data on Dutch consumers’ trust in financial institutions, we find that financially literate consumers are more likely to trust banks, insurance companies and pension funds, and the competence and integrity of the managers of these institutions. This holds both for broad-scope and narrow-scope trust. Although trust in respondents’ own financial institutions is significantly higher than general trust in financial institutions, both forms of trust are positively related. Financially knowledgeable people are more likely to trust the prudential supervisor. Finally, our results indicate that trust in the supervisor is positively related to trust in the financial sector.
SSRN
Initial Coin Offerings (ICOs) are currently one of the most fashionable topics in the area of financial markets. For token issuers, they are a success story. In 2018 alone, more than $ 14 billion dollars have been raised in ICOs. From the investors’ perspective, however, things do not look so shiny. Lots of them claim to have lost significant amounts of money invested in tokens issued by more or less reputable companies.In this market report, we analyze the ICOs of 2018. We illustrate the ICO market of that year and scrutinize how prices of the issued tokens have developed until mid 2019. Thereby, we take the perspective of an investor that bought tokens initially from the issuer. More than 90% of 2018's ICOs were based on the Ethereum Blockchain. We found that investors, which invested in ICOs during 2018, had only roughly an 8% chance that their tokens traded above their ICO issue price after six months. These numbers have not changed by July 2019. Nine in ten tokens trade below their ICO price. More than 70% of tokens have lost substantially all their value. This could indicate that the market gets rid of the those ICOs which are initiated by companies without a proper product or without a serious intention to develop a sustainable business. This could help the ICO market as a whole to become mature. We will continue this research in an ICO Market Report 2019/2020.
SSRN
This paper discusses market manipulation schemes on option expiration dates. We show that under ordinary circumstances, writers, but not holders, would have an incentive to manipulate the expiration of standard options, but both are incentivized to manipulate cash-settled options. Using our baseline results, we examine profits and incentives for a number of common option strategies. We then discuss a number of novel changes in modern option markets, such as automatic (as opposed to optional) exercise, different methods of calculating settlement prices, and passive exit strategies, and how these innovations affect the incentives to manipulate. Finally, we discuss manipulation for positional as opposed to profit-taking motives and examine several recent regulatory cases involving option manipulation.
SSRN
Incorporating arbitrage-free term-structure dynamics into a semi-structural macro-model, we jointly estimate the real equilibrium interest rate (r), trend inflation, and term premia for the United States and the euro area, using a Bayesian approach. The natural real rate and trend inflation are cornerstones determining equilibrium yields across maturities and macroeconomic trends. Taking into account the secular decline in equilibrium rates, term premia exhibit cyclical behavior over the business cycle, rather than the commonly reported trend. Our estimates suggest a fall in r from a pre-crisis level of about 3% to around zero, but estimates are subject to sizeable uncertainty. Including survey expectations can lift r estimates for recent quarters by a margin.
arXiv
We consider a large collection of dynamically interacting components defined on a weighted directed graph determining the impact of default of one component to another one. We prove a law of large numbers for the empirical measure capturing the evolution of the different components in the pool and from this we extract important information for quantities such as the loss rate in the overall pool as well as the mean impact on a given component from system wide defaults. A singular value decomposition of the adjacency matrix of the graph allows to coarse-grain the system by focusing on the highest eigenvalues which also correspond to the components with the highest contagion impact on the pool. Numerical simulations demonstrate the theoretical findings.
SSRN
We find that investment responds more sensitively to a firm’s Tobin’s q when its share price is more discrete. Low-price U.S. stocks exhibit higher investment-q sensitivity, but this pattern disappears in countries whose tick sizes increase with share prices. Using Tick Size Pilot Program as a controlled experiment, we find that an increase in the tick size increases price informativeness and investment-q sensitivity, particularly when firms face tighter tick-size constraints. Investment-q sensitivity increases more when managers’ incentives to learn are stronger, that is, when they have less precise information and when they have more resources to respond to price signals.
SSRN
Motivated by the world-wide surge of FinTech lending, we analyze the implications of lenders’ information technology adoption for financial stability. We estimate bank-level intensity of IT adoption before the global financial crisis using a novel dataset that provides information on hardware used in US commercial bank branches after mapping them to their parent bank. We find that higher intensity of IT-adoption led to significantly lower non-performing loans when the crisis hit: banks with a one standard deviation higher IT-adoption experienced 10% lower non-performing loans. High-IT-adoption banks were not less exposed to the crisis through their geographical footprint, business model, funding sources, or other observable characteristics. Loan-level analysis indicates that high-IT-adoption banks originated mortgages with better performance and did not offload low-quality loans. We apply a simple text-analysis algorithm to the biographies of top executives and find that banks led by more â€Å"tech-oriented� managers adopted IT more intensively and experienced lower non-performing loans during the crisis. Our results suggest that technology adoption in lending can enhance financial stability through the production of more resilient loans.
SSRN
Fintech lending to small businesses is growing rapidly in the United States, but the industry remains largely unregulated. In this study, I examine borrower outcomes when a borrower states that they are using credit from a fintech for small business purposes; I subsequently examine the differences in loan terms for regulated versus unregulated small business credit. I find that small business loans are charged a higher rate of interest than consumer loans from the same fintech lender, and also find that small business fintech loans have worse terms than small business loans from regulated banking entities. I propose a regulatory framework to offer small business borrowers protection from predatory fintech lenders and to clarify the proper regulators of this growing industry.
SSRN
This article argues that there is a fundamental mismatch between the nature of finance and current approaches to financial regulation. Today’s financial system is a dynamic and complex ecosystem. For these and other reasons, policy makers and market actors regularly have only a fraction of the information that may be pertinent to decisions they are making. The processes governing financial regulation, however, implicitly assume a high degree of knowability, stability, and predictability. Through two case studies and other examples, this article examines how this mismatch undermines financial stability and other policy aims. This examination further reveals that the procedural rules meant to promote accountability and legitimacy often fail to further either end. They result instead in excessive expenditures before new rules are adopted, counterproductive efforts to perfect ever more detailed rules, and too little re-evaluation of existing rules in light of new information or changed circumstances. The mismatch between the nature of finance and how finance is regulated helps to explain why financial regulation has failed in the past and why it will likely fail again. It also suggests the need for a new approach to financial regulation, one that acknowledges the limits of what can be known given the realities of today’s complex and constantly evolving financial ecosystem.
