Research articles for the 2021-06-23
SSRN
Anti-selection occurs when information asymmetry exists between an insurer and an applicant. When an applicant knows that they are at high risk of loss, but the insurer does not, the applicant may try to exploit this knowledge differential to secure insurance at a lower premium that does not match risk. Predictive genetic testing could lead to anti-selection if individuals, but not insurers, learn of genetic risk. Yet, to address fear of discrimination, several countries have, or are considering, limitations on insurers’ use of predictive genetic test results.In this paper, we discuss anti-selection theory and modeling and illustrate how regulation regarding insurer use of predictive genetic test results could impact anti-selection in insurance markets. The extent of this impact turns on how much individuals alter their insurance purchasing behavior following predictive genetic testing. At first blush it may seem likely that those who learn that they are at high-risk of a genetic condition would attempt to gain greater coverage. However, we highlight several domains of on-the-ground realities that challenge this baseline assumption. These real-world considerations should be incorporated into modeling of anti-selection to truly assess the potential impacts of regulation limiting insurer use of predictive genetic testing.
arXiv
We consider networks of banks with assets and liabilities. Some banks may be insolvent, and a central bank can decide which insolvent banks, if any, to bail out. We view bailouts as an optimization problem where the central bank has given resources at its disposal and an objective it wants to maximize. We show that under various assumptions and for various natural objectives this optimization problem is NP-hard, and in some cases even hard to approximate. Furthermore, we also show that given a fixed central bank bailout objective, banks in the network can make new debt contracts to increase their own market value in the event of a bailout (at the expense of the central bank).
arXiv
The computation of Greeks is a fundamental task for risk managing of financial instruments. The standard approach to their numerical evaluation is via finite differences. Most exotic derivatives are priced via Monte Carlo simulation: in these cases, it is hard to find a fast and accurate approximation of Greeks, mainly because of the need of a tradeoff between bias and variance. Recent improvements in Greeks computation, such as Adjoint Algorithmic Differentiation, are unfortunately uneffective on second order Greeks (such as Gamma), which are plagued by the most significant instabilities, so that a viable alternative to standard finite differences is still lacking. We apply Chebyshev interpolation techniques to the computation of spot Greeks, showing how to improve the stability of finite difference Greeks of arbitrary order, in a simple and general way. The increased performance of the proposed technique is analyzed for a number of real payoffs commonly traded by financial institutions.
SSRN
This paper studies how United States (US) policy actions impacted mortgage-backed securities (MBS) investors and mortgage borrowers during the subprime mortgage crisis of 2007 to 2010. The effects of the following policy actions on MBS spreads and mortgage lending spreads are studied: (i) US Government conservatorship of Fannie Mae and Freddie Mac; (ii) US Federal Reserve quantitative easing (QE) programs. We provide the following contributions: (i) The novel multi-equation score-driven t-QVARMA (quasi-vector autoregressive moving average) model of the multivariate t-distribution is used for the robust measurement of policy effects. (ii) In addition to the effects of QE, the effects of government conservatorship are also measured. (iii) The data period of the relevant literature is extended to March 2020, which provides a larger sample size for the pre-crisis and post-crisis periods. The results indicate that both policy actions significantly reduced MBS and mortgage lending spreads.
SSRN
We explore the effect of the presence of female directors in boards of directors on the economic impact of bank mergers and acquisitions (M&As). Using a unique, hand-collected dataset on 1,130 M&As announced by U.S. banks between 2003 and 2018, we find a significant negative relationship between female board membership and shareholder wealth after the banking crisis. Our results are robust to alternative model specifications that control for different proxies for gender diversity, heteroskedasticity, endogeneity and firm-specific variables. Our findings suggest that board gender diversity should be promoted with caution, and policy makers should acknowledge its limitations as a corporate governance mechanism.
arXiv
Child height is a significant predictor of human capital and economic status throughout adulthood. Moreover, non-unitary household models of family behavior posit that an increase in women's bargaining power can influence child health. We study the effects of an inheritance policy change, the Hindu Succession Act Amendment (HSAA), which conferred enhanced inheritance rights to unmarried women, on child height. We find robust evidence that the HSAA improved the height and weight of children. In addition, we find evidence consistent with a channel that the policy improved the women's intrahousehold bargaining power within the household, leading to improved parental investments for children. These study findings are also compatible with the notion that children do better when their mothers control a more significant fraction of the family resources. Therefore, policies that empower women can have additional positive spillovers for children's human capital.
arXiv
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price-volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this issue from a viewpoint of fractal analysis, which can cover the nonlinear interactions and the self-similarity properties widely acknowledged in the field of econophysics. The asymmetric cross-correlations between price and volatility for Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC) during the period from June 1, 2016 to December 28, 2020 are investigated using the MF-ADCCA method and quantified via the asymmetric DCCA coefficient. The approaches take into account the nonlinearity and asymmetric multifractal scaling properties, providing new insights in investigating the relationships in a dynamical way. We find that cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH. In contrast, for XRP and LTC, inverted reactions are present where cross-correlations are stronger in uptrend markets.
SSRN
The Financial Accounts of the Spanish Economy show that in 2020 the developments in balance sheets and financial transactions were strongly influenced by the health, social and economic crisis and by the extraordinary economic (monetary, fiscal and financial) policy measures adopted to mitigate its effects. Against this background, unlike the previous three years, households reduced their debt in the form of bank loans, essentially owing to the sharp slowdown in consumer credit. However, given the more pronounced contraction in household income, their aggregate debt/gross disposable income (GDI) ratio rose by 2.1 percentage points (pp) to 94.8%, breaking the downward trend of the previous years. The debt-to-GDP ratio of non-financial corporations also increased (by 12.3 pp to 85%), which is explained not only by the decline in GDP but also by the considerable growth in new financing, primarily in the form of bank loans. A further relevant aspect in 2020 was the substantial rise in holdings of liquid assets by firms and households since firms built up precautionary liquidity buffers and households sharply increased their saving.
arXiv
Famously mathematical finance was started by Bachelier in his 1900 PhD thesis where - among many other achievements - he also provides a formal derivation of the Kolmogorov forward equation. This forms also the basis for Dupire's (again formal) solution to the problem of finding an arbitrage free model calibrated to the volatility surface. The later result has rigorous counterparts in the theorems of Kellerer and Lowther. In this survey article we revisit these hallmarks of stochastic finance, highlighting the role played by some optimal transport results in this context.
SSRN
We investigate the relationship between private firms’ disclosures and the demand for the equity of their publicly traded peers. Using data on the global movement of public equity, we find that a one standard deviation increase in private firm disclosure transparency â€" proxied by the number of disclosed private firms’ financial statement line items â€" reduces global investors’ demand for public equity by 13% to 16% or by $206 million to $253 million in dollar terms. These findings are consistent with private firm disclosures generating negative pecuniary externalities â€" global investors reallocate their capital away from public firms to more transparent private firms â€" and less consistent with these disclosures creating positive information externalities that would benefit public firms. Consistent with this interpretation, we find that the reduction in demand for public equity is offset by a comparable increase in capital allocation to more transparent private firms. Using staggered openings of the Bureau van Dijk database offices in each investee country as a plausibly exogenous shock to private firm disclosures, we conclude that the negative relationship between private firm disclosures and public equity demand is likely causal.
SSRN
In this paper, we ask whether benefit corporations have a role to play in the emerging EU sustainable governance framework. In sec. 2, we briefly introduce the benefit corporation with regard to US law and to the laws of some EU member States, such as France and Italy, which have adopted this company form. In sec. 3, we focus on the benefit corporation’s purpose and function from a comparative law perspective, asking whether benefit corporations perform a useful function internationally. We argue that corporate purpose tends to be a flexible concept across countries and that benefit corporations are not the only way to reconcile profit and social values in business corporations. In sec. 4, we compare the critical features of the law relating to benefit corporations with the essential elements of the emerging sustainable governance framework. We show that the latter partially overlaps with the laws on benefit corporations and to some extent is a substitute for them, therefore reducing the potential interest in this corporate form. In sec. 5, we conclude that mainly firms which the new EU sustainable governance framework does not apply to, such as non-listed SMEs, will adopt the benefit corporation model when available in their jurisdiction, while other companies may still adopt it mostly for communicating their commitment to sustainability.
SSRN
This article examines the relation between labor matching and long-term equity returns. A long short portfolio of high minus low labor matching firms generates four-factor abnormal returns of 6.67% annually. The returns are 5.77% over industry- and 5.28% over characteristics-matched benchmarks. Our findings are robust after controlling for portfolio weights, factor models, number of portfolios, and exclusion of outliers. The higher matching firms also show greater positive surprises and positive returns following earnings announcements. A quasi-natural experiment in which state-wide labor matching was impeded following the adoption of inevitable disclosure doctrine shows firms headquartered in those states lost significant shareholder value.
SSRN
In order to maintain the function of a decentralized financial system like Bitcoin, transaction fees are offered to engage miners in the transaction confirmation process. This paper investigates the effect of miner competition on the equilibrium transaction fees. We develop a game-theoretic model with costly entry into mining activities. We find that miners may strategically assemble fewer transactions into a block to reduce total fees, and as a result, to deter entry. Equilibrium transaction fees also depend on block rewards as a rise in total fees is accompanied by a drop in rewards. Our empirical analysis supports the model's predictions. We provide evidence on the existence of excess capacity in a block, taking into account the random confirmation process. The empirical findings demonstrate that heightened competition tends to increase the block size and total fees. Furthermore, the halving of rewards correlates to a fee hike.
arXiv
There is no exact closed form formula for pricing of European options with discrete cash dividends under the model where the underlying asset price follows a piecewise lognormal process with jumps at dividend ex-dates. This paper presents alternative expansions based on the technique of Etore and Gobet, leading to more robust first, second and third-order expansions across the range of strikes and the range of dividend dates.
arXiv
We investigate the benefits of feature selection, nonlinear modelling and online learning when forecasting in financial time series. We consider the sequential and continual learning sub-genres of online learning. The experiments we conduct show that there is a benefit to online transfer learning, in the form of radial basis function networks, beyond the sequential updating of recursive least-squares models. We show that the radial basis function networks, which make use of clustering algorithms to construct a kernel Gram matrix, are more beneficial than treating each training vector as separate basis functions, as occurs with kernel Ridge regression. We demonstrate quantitative procedures to determine the very structure of the radial basis function networks. Finally, we conduct experiments on the log returns of financial time series and show that the online learning models, particularly the radial basis function networks, are able to outperform a random walk baseline, whereas the offline learning models struggle to do so.
SSRN
This study quantifies firm-specific operating exposure to cumulative unexpected weather variations and examines how it affects earnings predictability and analysts’ forecasts. Two competing hypotheses are tested. The reduction in earnings seasonality hypothesis posits that operating weather exposure reduces earnings seasonality, thereby increasing forecast dispersion and reducing forecast accuracy. The increase in short-term earnings persistence hypothesis posits that operating weather exposure makes short-term earnings more persistent, leading to lower forecast dispersion and higher accuracy. The results provide strong evidence that firms with higher operating weather exposure display lower earnings seasonality but higher short-term earnings persistence. The net effect is that analysts’ forecasts become significantly noisier with more dispersion and lower accuracy. These results are stronger for industries with higher seasonality and for regions experiencing extreme weather conditions. Further analysis shows that firms’ profit margin and asset turnover exposures to abnormal precipitation and temperature variations contribute to the overall weather effects.
SSRN
We propose implied spreads (IS) and normalized implied spreads (NIS) as simple measures to characterize option prices. IS is the credit spread of an option’s implied bond, the portfolio long a risk-free bond and short a put option. NIS normalizes IS by the risk-neutral default probability and reflects tail risk. IS and NIS are countercyclical and predict implied bond returns, while neither, like implied volatility, predicts put returns. These opposite predictability results are consistent with a stochastic volatility, stochastic jump intensity model, as put premia increase in volatility but decrease in jump intensity, while implied bond premia increase in both.
arXiv
It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data.
arXiv
This paper presents the Runge-Kutta-Legendre finite difference scheme, allowing for an additional shift in its polynomial representation. A short presentation of the stability region, comparatively to the Runge-Kutta-Chebyshev scheme follows. We then explore the problem of pricing American options with the Runge-Kutta-Legendre scheme under the one factor Black-Scholes and the two factor Heston stochastic volatility models, as well as the pricing of butterfly spread and digital options under the uncertain volatility model, where a Hamilton-Jacobi-Bellman partial differential equation needs to be solved. We explore the order of convergence in these problems, as well as the option greeks stability, compared to the literature and popular schemes such as Crank-Nicolson, with Rannacher time-stepping.
arXiv
Inspired by recent progress in quantum algorithms for ordinary and partial differential equations, we study quantum algorithms for stochastic differential equations (SDEs). Firstly we provide a quantum algorithm that gives a quadratic speed-up for multilevel Monte Carlo methods in a general setting. As applications, we apply it to compute expectation values determined by classical solutions of SDEs, with improved dependence on precision. We demonstrate the use of this algorithm in a variety of applications arising in mathematical finance, such as the Black-Scholes and Local Volatility models, and Greeks. We also provide a quantum algorithm based on sublinear binomial sampling for the binomial option pricing model with the same improvement.
SSRN
I provide a selective review of recent developments in financial econometrics related to measuring, modeling, forecasting and pricing “good†and “bad†volatilities based on realized variation type measures constructed from high-frequency intraday data. An especially appealing feature of the different measures concerns the ease with which they may be calculated empirically, merely involving cross-products of signed, or thresholded, high-frequency returns. I begin by considering univariate semivariation measures, followed by multivariate semicovariation and semibeta measures, before briefly discussing even richer partial (co)variation measures. I focus my discussion on practical uses of the measures emphasizing what I consider to be the most noteworthy empirical findings to date pertaining to volatility forecasting and asset pricing
SSRN
Recently, Egypt has prioritized financial inclusion in its monetary reform agenda. The objective of this paper is to examine the determinants of financial inclusion in Egypt using the World Bank¡¯s Global Findex 2017 database to conduct a logistic regression. Empirical results prove that there is no significant relationship between gender and the level of financial inclusion in Egypt, whereas, richer, more educated and older individuals are more strongly included in the financial system. The results reveal that the main barrier to financial inclusion is actually a lack of money; which hinders opening a formal account, savings account or credit account. Through possible policy measures, the paper recommends the need for a progressive approach to robust financial literacy and awareness in order for a positive economic growth role of financial inclusion to emerge in the Egyptian economy.
arXiv
In order to slow the spread of the CoViD-19 pandemic, governments around the world have enacted a wide set of policies limiting the transmission of the disease. Initially, these focused on non-pharmaceutical interventions; more recently, vaccinations and large-scale rapid testing have started to play a major role. The objective of this study is to explain the quantitative effects of these policies on determining the course of the pandemic, allowing for factors like seasonality or virus strains with different transmission profiles. To do so, the study develops an agent-based simulation model, which is estimated using data for the second and the third wave of the CoViD-19 pandemic in Germany. The paper finds that during a period where vaccination rates rose from 5% to 40%, large-scale rapid testing had the largest effect on reducing infection numbers. Frequent large-scale rapid testing should remain part of strategies to contain CoViD-19; it can substitute for many non-pharmaceutical interventions that come at a much larger cost to individuals, society, and the economy.
SSRN
Online Peer-to-Peer lending (P2P lending or online lending), as an important component of financial technologies (fintechFinTech), was once very popular in China, but by the end of 2020, it had been completely wiped out from the Chinese financial landscape. This is not really due to business failure, but is essentially a product of regulatory dysfunction. Since 2015, China has tried to regulate the P2P lending market by establishing an innovative central-local co-regulatory regime under which local regulators are incentivized to contribute to the regulation of P2P lending in cooperation with the central government. Nevertheless, it seems that the central-local cooperative regulation has failed to achieve its purpose considering the dramatic fall of the P2P lending market in practice. There are serious regulatory issues at both the central and local levels. The central government fails to make proactive regulatory responses to the rise of P2P lending, yet takes heavy-handed measures to control risks after the outbreak of a market crisis. Local regulators, on the other hand, fail to strike a balance between promoting economic growth and protecting investors. The incomplete devolution of regulatory power to local governments also leads to difficulties assigning responsibility between regulators at different levels. There are implications for the central-local co-regulation in China, including the clear allocation of the central and local regulatory power, the enhancement of the local regulator’s independence and the establishment of central-local coordination mechanisms.
SSRN
We study initial and subsequent effects of credit ratings on acquisition activity. Our findings suggest that access to bond markets, i.e. overcoming bank lending constraints, in order to conduct large and high quality acquisitions is an important motive for becoming rated. This initial connection drives many prior results. However, following the initial credit rating effect, acquisition activity actually dampens in response to credit rating induced monitoring. Monitoring by credit rating agencies is linked to diversifying acquisitions, acquisitions of low risk targets and a reduced likelihood of overpayment.
SSRN
In this paper, I show that the existence of non-bank financial institutions (NBFIs) has implications for the optimal regulation of the traditional banking sector. I develop a New Keynesian DSGE model for the euro area featuring a heterogeneous financial sector allowing for potential credit leakage towards unregulated NBFIs. Introducing NBFIs raises the importance of credit stabilization relative to other policy objectives in the welfare-based loss function of the regulator. The resulting optimal policy rule indicates that regulators adjust dynamic capital requirements more strongly in response to macroeconomic shocks due to credit leakage. Furthermore, introducing non-bank finance not only alters the cyclicality of optimal regulation, but also has implications for the optimal steady-state level of capital requirements and loan-to-value ratios. Sector-specific characteristics such as bank market power and risk affect welfare gains from traditional and NBFI credit.
SSRN
We examine how childcare inequalities in the home affect the work productivity of women, using unique data on the family structures of hedge fund managers, and the shock from school closures during the COVID-19 lockdowns. We show that funds with female managers miss out on a 7% excess return on average compared to male-only funds in the shock-month of school closures, providing a direct measure of the cost of unpaid care work. This cost increases with the proportion of mothers in the fund, especially mothers with young children. The performance of funds managed by fathers or women without children is not affected by school closures. With increasing calls for more women representation in all layers of the economy and the efforts exerted towards that goal, there is reason for concern that these efforts might not factor in, as the pandemic has uncovered how women bear both the burden of unpaid care work, and its subsequent cost to their paid work.
