# Research articles for the 2020-06-08

A Level Playing Field? Empirical Evidence That Ethnic Minority Analysts Face Unequal Access to Corporate Managers
Flam, Rachel W.,Green, Jeremiah,Lee, Joshua A.,Sharp, Nathan Y.
SSRN
Given the lack of diversity among senior executives of U.S. public companies, we investigate whether ethnic minority analysts face unique barriers to management access. We find managers are less likely to select minority analysts to participate in the Q&A session of public earnings conference calls, and minority analysts selected to participate receive lower levels of prioritization and engagement than non-minority analysts. Minority analystsâ€™ access to management does not improve over time or with companies recognized for workplace diversity. The consequences of unequal treatment extend beyond conference calls, as investors are less likely to vote for minorities as Institutional Investor All-Stars.

A Note on the Impossibility of Correctly Calibrating the Current Exposure Method for Large OTC Derivatives Portfolios
Murphy, David
SSRN
The capital charges for counterparty credit risk form an important part of the Basel Capital Accords. The Basel Committee permits firms to use a variety of methods to calculate regulatory capital on this risk class, including a simple approach â€" the constant exposure method or CEM â€" and a more sophisticated models-based approach known as EPE (for â€˜expected positive exposureâ€™).Counterparty credit risk capital models estimate the potential future exposure (â€˜PFEâ€™) of a portfolio of derivatives with a counterparty based on whatever margining scheme applies. The CEM approximates this PFE using a constant percentage of notional, with the portfolio capital charge being the sum of the percentages which apply to each instrument. The CEM therefore recognizes no diversification benefit. In contrast, EPE approaches model the entire future of the net portfolio and thus provide much more accurate estimates for portfolios with more than a handful of instruments. The inaccuracy of the CEM is hardly surprising as it was intended only for smaller portfolios and less sophisticated firms.More recently the Basel Committee has proposed that the CEM be used as a method for determining the adequacy of financial resources available to an OTC derivatives central counterparty (â€˜CCPâ€™). Since cleared portfolios are very large and very well-hedged, it might be imagined that the CEM is not well suited to this task. This paper confirms that suspicion. In particular we show that the use of the CEM to estimate the riskiness of CCP default fund contributions leads to a significant overstatement of risk. Further, we show that the CEM cannot be simply recalibrated to provide a more risk sensitive approach. Thus an approach which provides more accurate estimates for typical CCPs is to be preferred.

Adversarial Robustness of Deep Convolutional Candlestick Learner
Jun-Hao Chen,Samuel Yen-Chi Chen,Yun-Cheng Tsai,Chih-Shiang Shur
arXiv

Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called \emph{adversarial attacks}. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of constructing perturbed examples and use these examples to boost the robustness of the model. Our algorithm increases the stability of DL models for candlestick classification with respect to perturbations in the input data.

An Impulse-Regime Switching Game Model of Vertical Competition
René Aïd,Luciano Campi,Liangchen Li,Mike Ludkovski
arXiv

We study a new kind of non-zero-sum stochastic differential game with mixed impulse/switching controls, motivated by strategic competition in commodity markets. A representative upstream firm produces a commodity that is used by a representative downstream firm to produce a final consumption good. Both firms can influence the price of the commodity. By shutting down or increasing generation capacities, the upstream firm influences the price with impulses. By switching (or not) to a substitute, the downstream firm influences the drift of the commodity price process. We study the resulting impulse--regime switching game between the two firms, focusing on explicit threshold-type equilibria. Remarkably, this class of games naturally gives rise to multiple Nash equilibria, which we obtain via a verification based approach. We exhibit three types of equilibria depending on the ultimate number of switches by the downstream firm (zero, one or an infinite number of switches). We illustrate the diversification effect provided by vertical integration in the specific case of the crude oil market. Our analysis shows that the diversification gains strongly depend on the pass-through from the crude price to the gasoline price.

Auditors and the Principal-Principal Agency Conflict in Family-Controlled Firms
Ben Ali, Chiraz,Boubaker, Sabri,Magnan, Michel
SSRN
This paper examines whether multiple large shareholders (MLS) affect audit fees in firms where the largest controlling shareholder (LCS) is a family. Results show that there is a negative relationship between audit fees and the presence, number, and voting power of MLS. This is consistent with the view that auditors consider MLS as playing a monitoring role over the LCS, mitigating the potential for expropriation by the LCS. Therefore, our evidence suggests that auditors reduce their audit risk assessment and audit effort and ultimately audit fees in family-controlled firms with MLS.

COVID-19 Pandemic and Global Financial Market Interlinkages: A Dynamic Temporal Network Analysis
Chakrabarti, Prasenjit,Jawed, Mohammad Shameem,Sarkhel, Manish
SSRN
This study uses network theory to investigate the change in the dynamics of the financial markets of G20 countries, in the aftermath of COVID-19. The sheer scale, scope, and nature of the disruptions brought by the pandemic makes it an unprecedented global event. We find a major change in the structure of market linkages, departing from their pre-crisis behavior, both advanced and emerging markets form a tightly coupled close community after the disease outbreak. Chinese market shows a divergence by distancing itself from the rest of the cohort. This has significant implications on the geographical portfolio diversification strategies and benefits

COVID-19 Pandemic and Stock Market Response: A Culture Effect
Fernandez-Perez, Adrian,Gilbert, Aaron B.,Indriawan, Ivan,Nguyen, Nhut (Nick) Hoang
SSRN
National culture has been shown to impact the way investors, firm managers, and markets in their entirety respond to different situations and events. The psychology literature, however, notes that in terms of crisis, old behaviors and norms can get replaced by new norms as groups adjust to the new situation. To date, no one has looked at the impact of culture on market responses to disasters. This paper is the first to address the effect of national culture on stock market responses to a global health disaster. We find larger declines and greater volatilities for stock markets in countries with higher uncertainty avoidance, lower individualism, and greater experience with disease-causing pathogens during the first three weeks after the confirmation of the first COVID-19 case within a country. Our results are robust after controlling for a number of variables, including investor fear, cumulative infected cases, the stringency of government response policies, the 2003 SARS experience, the level of democracy, political corruption, and trade openness.

COVID-19: Guaranteed Loans and Zombie Firms
Zoller-Rydzek, Benedikt,Keller, Florian
SSRN
Based on the ZHAW Managers Survey (7-13 April 2020) we evaluate firm reactions towards the COVID-19 crisis. We find that the Swiss economic lockdown measures successfully froze the economy, i.e., firms show very little pro-active reactions towards the crisis, but drastically decrease their business activities. The firms in the survey report that the decline in foreign demand is the single most important reasons for their deteriorating business situation. The only significant pro-active reactions to mitigate the crisis are increased digitalization efforts. These efforts are expected to have a long-lasting impact on firms' performance due to a selection effect, i.e., firms with more positive experience of digitialization will maintain their higher levels of digitalization even after the crisis. In general we find that firms that faced a more difficult business situation before the crisis are affected more severely during the crisis. Moreover, we investigate the impact of the Swiss federal loan program (Bundeshilfe) on the business activities of Swiss firms. Specifically, we focus on the take up of firms and its interaction with the perceived business situation before and during the COVID-19 crisis. To this end, we develop a stylized theoretical model of financially constrained heterogeneous firms. We find that policy makers face a trade-off between immediate higher unemployment rates and long-term higher public spending. The former arises from a combination of a too strong economic impact of the COVID-19 lockdown and too low levels of loans provided by the government to financially distressed firms. Nevertheless, providing (too) high levels of loans to firms might create zombie firms that are going to default on their debt in the future leading to an increase in public spending.

Changes in Ownership Structure; Conversions of Mutual Savings and Loans to Stock Charter
Masulis, Ronald W.
SSRN
This study analyzes both the causes and effects of mutual S&L conversions to corporate charter. Changes in technology and government policies have substantially increased S&L competition, riskbearing, and potential scale and scope economies. Evidence indicates that these changes have decreased the relative operating advantages of mutual S&Ls, encouraging conversions to stock charter. The S&Lâ€™s financial and operating characteristics, which affect the success of the conversion effort, are also explored.

Coordinated Transaction Scheduling in Multi-Area Electricity Markets: Equilibrium and Learning
Mariola Ndrio,Subhonmesh Bose,Ye Guo,Lang Tong
arXiv

Tie-line scheduling in multi-area power systems in the US largely proceeds through a market-based mechanism called Coordinated Transaction Scheduling (CTS). We analyze this market mechanism through a game-theoretic lens. Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights. Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.

Corporate Social Responsibility and Foreign Institutional Investor Heterogeneity
Roy, Partha P.,Rao, Sandeep,Marshall, Andrew P.,Thapa, Chandra
SSRN
We investigate the nexus between corporate social responsibility (CSR) and ownership of foreign institutional investors (FII). Using a quasi-natural experiment setup of mandated CSR regulation in India, the aggregated examination shows that firms complying with mandated CSR activities (CSR firms) attract more FII ownership (FIO) compared to firms which do not comply. However, relative to all other legal origins, FII from civil law origin countries seem to invest more in CSR firms. Evidence also suggests that independent and long term FII tend to be more drawn towards CSR firms, relative to all other types of FII. Results further indicate that host firms spending more on educational projects as part of their CSR engagement seem to attract higher FIO. Finally, firms which attract greater FIO, as a result of complying with mandated CSR activities, appear to attain higher market valuations.

Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices
Calypso Herrera,Florian Krach,Anastasis Kratsios,Pierre Ruyssen,Josef Teichmann
arXiv

The robust PCA of high-dimensional matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, the algorithm must re-run each time a new matrix should be decomposed. Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated. Therefore, we introduce Denise, a deep learning-based algorithm for robust PCA of symmetric positive semidefinite matrices, which learns precisely such a function. Theoretical guarantees that Denise's architecture can approximate the decomposition function, to arbitrary precision and with arbitrarily high probability, are obtained. The training scheme is also shown to convergence to a stationary point of the robust PCA's loss-function. We train Denise on a randomly generated dataset, and evaluate the performance of the DNN on synthetic and real-world covariance matrices. Denise achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time.

Design Choices in Central Clearing: Issues Facing Small Advanced Economies
Murphy, David,Budding, Edwin
SSRN
For some contracts traded between some institutions, central clearing is becoming mandatory. Regulatory incentives are also being altered to encourage the use of CCPs where reasonably possible, and to ensure that where central clearing is not appropriate capital is held against the risks that arise. In this paper, we review some of the issues involved in deciding which transactions should be centrally cleared, where CCPs should be located, and how they should be designed, managed, and regulated. As derivatives reform progresses, the soundness of the central counterparties becomes more important to the soundness of the financial system, so these questions are important.

Distance to Headquarter and Real Estate Equity Performance
Milcheva, Stanimira,Yildirim, Yildiray,Zhu, Bing
SSRN
We study the effect of geographic portfolio diversification of real estate firms on their investment performance before and after the global financial crisis (GFC). In addition to previously used dispersion metrics, we also account for the distance of the properties to the corporate headquarters. We document a notable shift in the non-market performance of real estate companies after the crisis. Pre-GFC, we do not find a difference in non-market performance across equities based on geographic diversification. Post-GFC, equities with high geographic dispersion significantly outperform the market, while firms with concentrated property holdings do not deliver a significant alpha. Increased real estate equity market sophistication and strong institutional presence can explain why this effect is only observed for dispersed small firms, those invested outside gateway metro areas, or companies with low institutional ownership.

Do Analysts Mind the GAAP? Evidence From the Tax Cuts and Jobs Act of 2017
Chen, Novia (Xi),Koester, Allison
SSRN
This study examines the quality of analystsâ€™ GAAP-based earnings forecasts. Ideally, addressing this question requires events that have an ex ante estimable earnings impact, and affect GAAP earnings but not street earnings. The deferred tax adjustment as a result of a 2017 tax law change meets these criteria. Focusing on the fourth quarter of 2017 (2017Q4), we find that analystsâ€™ GAAP earnings forecasts and revisions fail to incorporate the vast majority of the deferred tax adjustment. We explore two potential explanations for this finding â€" task-specific complexity and lack of GAAP earnings forecasting effort. We find evidence consistent with the latter. Our final analyses consider two implications of our findings. First, despite analysts underreacting to the deferred tax adjustment, investors promptly impound the adjustment into stock prices at the legislative enactment date, indicating that analystsâ€™ GAAP earnings forecasts are not a good proxy for investor expectations of GAAP earnings during our sample period. Second, analysts who best incorporate the adjustment into their 2017Q4 GAAP earnings forecasts issue more accurate GAAP earnings forecasts for subsequent quarters, indicating that our inferences extend beyond a single quarter and account. Collectively, these findings have implications for research that relies on analystsâ€™ GAAP earnings forecasts to be of reasonable quality.

Do Private Household Transfers to the Elderly Respond to Public Pension Benefits? Evidence from Rural China
arXiv

Ageing populations in developing countries have spurred the introduction of public pension programs to preserve the standard of living for the elderly. The often-overlooked mechanism of intergenerational transfers, however, can dampen these intended policy effects as adult children who make income contributions to their parents could adjust their behavior to changes in their parents' income. Exploiting a unique policy intervention in China, we examine using a difference-in-difference-in-differences (DDD) approach how a new pension program impacts inter vivos transfers. We show that pension benefits lower the propensity of receiving transfers from adult children in the context of a large middle-income country and we also estimate a small crowd-out effect. Taken together, these estimates fit the pattern of previous research in high-income countries, although our estimates of the crowd-out effect are significantly smaller than previous studies in both high-income and middle-income countries.

Double Deep Q-Learning for Optimal Execution
Brian Ning,Franco Ho Ting Lin,Sebastian Jaimungal
arXiv

Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the optimal actions of a trader. The model is a fully connected Neural Network trained using Experience Replay and Double DQN with input features given by the current state of the limit order book, other trading signals, and available execution actions, while the output is the Q-value function estimating the future rewards under an arbitrary action. We apply our model to nine different stocks and find that it outperforms the standard benchmark approach on most stocks using the measures of (i) mean and median out-performance, (ii) probability of out-performance, and (iii) gain-loss ratios.

Dual Space Arguments Using Polynomial Roots in the Complex Plane: A Novel Approach to Deriving Key Statistical Results
Crack, Timothy Falcon,Osborne, Michael,Crack, Malcolm,Osborne, Mark
SSRN
We present a canonical orthogonal decomposition of sample variance and its applications. Surprisingly, our decomposition arises naturally from a novel dual space argument using polynomial roots in the complex plane. Linking these two seemingly disparate literatures yields a new pathway to the derivation of key statistical results under standard assumptions. These results include the chi-squared distribution of the scaled sample variance, the loss of one degree of freedom (relative to sample size) in the sample variance, the distribution of Snedecorâ€™s F-test of differences in dispersion, the independence of the sample mean and sample variance, and the distribution of the one-sample Student-t test of the mean. We suggest several promising directions for future research using our dual space method.

Duality for optimal consumption under no unbounded profit with bounded risk
Michael Monoyios
arXiv

We give a definitive treatment of duality for optimal consumption over the infinite horizon, in a semimartingale incomplete market satisfying no unbounded profit with bounded risk (NUPBR). Rather than base the dual domain on (local) martingale deflators, we use a class of supermartingale deflators such that deflated wealth plus cumulative deflated consumption is a supermartingale for all admissible consumption plans. This yields a strong duality, because the enlarged dual domain of processes dominated by deflators is naturally closed, without invoking its closure. In this way we automatically reach the bipolar of the set of deflators. We complete this picture by proving that the set of processes dominated by local martingale deflators is dense in our dual domain, confirming that we have identified the natural dual space. In addition to the optimal consumption and deflator, we characterise the optimal wealth process. At the optimum, deflated wealth is a supermartingale and a potential, while deflated wealth plus cumulative deflated consumption is a uniformly integrable martingale. This is the natural generalisation of the corresponding feature in the terminal wealth problem, where deflated wealth at the optimum is a uniformly integrable martingale. We use no constructions involving equivalent local martingale measures. This is natural, given that such measures typically do not exist over the infinite horizon and that we are working under NUPBR, which does not require their existence. The structure of the duality proof reveals an interesting feature compared with the terminal wealth problem. There, the dual domain is $L^{1}$-bounded, but here the primal domain has this property, and hence many steps in the duality proof show a marked reversal of roles for the primal and dual domains, compared with the proofs of Kramkov and Schachermayer.

Dynamic Clearing and Contagion in Financial Networks
Tathagata Banerjee,Alex Bernstein,Zachary Feinstein
arXiv

In this paper we will consider a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities. Emphasis will be placed on the construction, existence, and uniqueness of the continuous-time framework and its formulation as a differential equation driven by the operating cash flows. Finally, the financial implications of time dynamics will be considered. The focus will be on how the dynamic clearing solutions differ from those of the static Eisenberg-Noe model.

Dynamic Horizon Specific Network Risk
Jozef Barunik,Michael Ellington
arXiv

This paper examines the pricing of dynamic horizon specific network risk in the cross-section of stock returns. We suggest how to track such dynamic network connections on a daily basis using time-varying parameter vector auto-regressions. Empirically, we characterize the short-term and long-term risks from a large-scale dynamic network on all S&P500 constituents' return volatilities. Consistent with theory, we show that stocks with high sensitivities to dynamic network risk earn lower returns. A two-standard deviation increase in long-term (short-term) network risk loadings associate with a 14.73% (12.96%) drop in annualized expected returns.

Earnings Beta
Ellahie, Atif
SSRN
The literature on â€˜cash flowâ€™ or â€˜earningsâ€™ beta is theoretically well-motivated in its use of fundamentals, instead of returns, to measure systematic risk. However, empirical measures of earnings beta based on either log-linearizing the return equation or log-linearizing the clean-surplus accounting identity are often difficult to construct. I construct simple earnings betas based on various measures of realized and expected earnings, and find that an earnings beta based on price-scaled expectations shocks performs consistently well in explaining the cross-section of returns over 1981â€"2017. I also examine the relation between different measures of beta and several firm characteristics that are either theoretically connected to systematic risk or are empirically associated with returns, and find evidence in support of the construct validity of an earnings beta based on price-scaled expectations shocks. Overall, the findings suggest that this easy-to-construct earnings beta can be suitable for future researchers requiring a measure of systematic risk.

Economics of carbon-dioxide abatement under an exogenous constraint on cumulative emissions
arXiv

The fossil-fuel induced contribution to further warming over the 21st century will be determined largely by integrated CO2 emissions over time rather than the precise timing of the emissions, with a relation of near-proportionality between global warming and cumulative CO2 emissions. This paper examines optimal abatement pathways under an exogenous constraint on cumulative emissions. Least cost abatement pathways have carbon tax rising at the risk-free interest rate, but if endogenous learning or climate damage costs are included in the analysis, the carbon tax grows more slowly. The inclusion of damage costs in the optimization leads to a higher initial carbon tax, whereas the effect of learning depends on whether it appears as an additive or multiplicative contribution to the marginal cost curve. Multiplicative models are common in the literature and lead to delayed abatement and a smaller initial tax. The required initial carbon tax increases with the cumulative abatement goal and is higher for lower interest rates. Delaying the start of abatement is costly owing to the increasing marginal abatement cost. Lower interest rates lead to higher relative costs of delaying abatement because these induce higher abatement rates early on. The fraction of business-as-usual emissions (BAU) avoided in optimal pathways increases for low interest rates and rapid growth of the abatement cost curve, which allows a lower threshold global warming goal to become attainable without overshoot in temperature. Each year of delay in starting abatement raises this threshold by an increasing amount, because the abatement rate increases exponentially with time.

Endowment Performance and the Demise of the Multi-Asset-Class Model
Ennis, Richard
SSRN
Endowment funds large and small underperform passive investment. Moreover, an analysis of the performance of 41 of the largest individual endowments over the 11 years ended June 30, 2019, reveals that none outperformed with statistical significance, while one in four underperformed with statistical significance. The multi-asset-class approach to institutional investing has failed to deliver diversification benefits and has had an adverse effect on endowment performance. Given prevailing diversification patterns and costs of 1 to 2% of assets, it is likely that the great majority of endowment funds will underperform in the years ahead.

Equal Risk Pricing of Derivatives with Deep Hedging
Alexandre Carbonneau,Frédéric Godin
arXiv

This article presents a deep reinforcement learning approach to price and hedge financial derivatives. This approach extends the work of Guo and Zhu (2017) who recently introduced the equal risk pricing framework, where the price of a contingent claim is determined by equating the optimally hedged residual risk exposure associated respectively with the long and short positions in the derivative. Modifications to the latter scheme are considered to circumvent theoretical pitfalls associated with the original approach. Derivative prices obtained through this modified approach are shown to be arbitrage-free. The current paper also presents a general and tractable implementation for the equal risk pricing framework inspired by the deep hedging algorithm of Buehler et al. (2019). An $\epsilon$-completeness measure allowing for the quantification of the residual hedging risk associated with a derivative is also proposed. The latter measure generalizes the one presented in Bertsimas et al. (2001) based on the quadratic penalty. Monte Carlo simulations are performed under a large variety of market dynamics to demonstrate the practicability of our approach, to perform benchmarking with respect to traditional methods and to conduct sensitivity analyses.

Estimating Full Lipschitz Constants of Deep Neural Networks
Calypso Herrera,Florian Krach,Josef Teichmann
arXiv

We estimate the Lipschitz constants of the gradient of a deep neural network and the network itself with respect to the full set of parameters. We first develop estimates for a deep feed-forward densely connected network and then, in a more general framework, for all neural networks that can be represented as solutions of controlled ordinary differential equations, where time appears as continuous depth. These estimates can be used to set the step size of stochastic gradient descent methods, which is illustrated for one example method.

Explicit option valuation in the exponential NIG model
Jean-Philippe Aguilar
arXiv

We provide closed-form pricing formulas for a wide variety of path-independent options, in the exponential L\'evy model driven by the Normal inverse Gaussian process. The results are obtained in both the symmetric and asymmetric model, and take the form of simple and quickly convergent series, under some condition involving the log-forward moneyness and the maturity of instruments. Proofs are based on a factorized representation in the Mellin space for the price of an arbitrary path-independent payoff, and on tools from complex analysis. The validity of the results is assessed thanks to several comparisons with standard numerical methods (Fourier-related inversion, Monte-Carlo simulations) for realistic sets of parameters. Precise bounds for the convergence speed and the truncation error are also provided.

Fast calibration of two-factor models for energy option pricing
Emanuele Fabbiani,Andrea Marziali,Giuseppe De Nicolao
arXiv

Energy companies need efficient procedures to perform market calibration of stochastic models for commodities. If the Black framework is chosen for option pricing, the bottleneck of market calibration is the computation of the variance of the asset. For energy commodities, it is common to adopt multi-factor linear models, whose variance obeys a matrix Lyapunov differential equation. In this paper, both analytical and numerical methods to derive the variance through a Lyapunov equation are discussed and compared in terms of computational efficiency. The Lyapunov approach illustrated herein is more straightforward than ad-hoc derivations found in the quantitative finance literature and can be readily extended by a practitioner to higher-dimensional models. A practical case study is presented, where the variance of a two-factor mean-reverting model is embedded into the Black formulae and the model parameters are then calibrated against listed options. In particular, the analytical and numerical method are compared, showing that the former makes the calibration 14 times faster. A Python implementation of the proposed procedures is available as open-source software on GitHub.

Foreign Exchange and the Capital Market Dynamics: New Evidence from Non-linear Autoregressive Distributed Lag Model
Omoregie, Osaretin Kayode
SSRN
The purpose of this study was to investigate and analyze the relationship between foreign exchange and capital market dynamics in Nigeria from January 1999 to February 2018. The study deployed the Non-Linear-ARDL model to study the dynamics of exchange rate and the capital market in Nigeria. The research outcome revealed that a rise (fall) in all-share-index is related to real exchange rate depreciation (appreciation), while real exchange rate depreciation (appreciation) is associated with an increase (decrease) in all-share-index. Besides, the research outcome also showed that there is a presence of time-specific long-run, bi-directional, and unidirectional causality with stronger interrelation after the Global Financial Crisis. The study recommends that to properly hedge and diversify portfolio against potential risk in these two markets, market players need to understand the dynamics between them.

From Free Markets to Fed Markets: How Unconventional Monetary Policy Distorts Equity Markets
PutniÅ†Å¡, TÄlis J.
SSRN
In response to the COVID-19 pandemic, the US Federal Reserve almost doubled its balance sheet by adding $3 trillion of assets in the space of three months constituting the most aggressive unconventional monetary policy on record. We show that these actions had a substantial effect on stock markets, accounting for one-third of the rebound in markets since March 2020 (increasing returns by 11-13%) and contributing to the apparent disconnect between stock prices and the real economy. Using dynamic time-series models, we characterize the strong bi-directional symbiotic relation between the Fedâ€™s balance sheet and stock markets. Generating Realistic Stock Market Order Streams Junyi Li,Xitong Wang,Yaoyang Lin,Arunesh Sinha,Micheal P. Wellman arXiv We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data. Hedging-Induced Correlation in Illiquid Markets BrÃ¸gger, SÃ¸ren Bundgaard SSRN I develop a model with two assets in which the hedging activity of derivatives dealers, interacting with market illiquidity, distorts the covariance structure of the market. I apply the model to hedging of counter party risk, and find strong support for the model's key predictions. Using evidence from Japan, I show that hedging of counter party risk associated with currency swap portfolios drives a strong, non-fundamental correlation between credit and currency markets. The effects are economically significant. For example, I estimate that counter party risk hedging associated with SoftBank's FX swap portfolio accounts for 25% of the weekly volatility of SoftBank CDS returns. Integrated ridesharing services with chance-constrained dynamic pricing and demand learning Tai-Yu Ma,Sylvain Klein arXiv The design of integrated mobility-on-demand services requires jointly considering the interactions between traveler choice behavior and operators' operation policies to design a financially sustainable pricing scheme. However, most existing studies focus on the supply side perspective, disregarding the impact of customer choice behavior in the presence of co-existing transport networks. We propose a modeling framework for dynamic integrated mobility-on-demand service operation policy evaluation with two service options: door-to-door rideshare and rideshare with transit transfer. A new constrained dynamic pricing model is proposed to maximize operator profit, taking into account the correlated structure of different modes of transport. User willingness to pay is considered as a stochastic constraint, resulting in a more realistic ticket price setting while maximizing operator profit. Unlike most studies, which assume that travel demand is known, we propose a demand learning process to calibrate customer demand over time based on customers' historical purchase data. We evaluate the proposed methodology through simulations under different scenarios on a test network by considering the interactions of supply and demand in a multimodal market. Different scenarios in terms of customer arrival intensity, vehicle capacity, and the variance of user willingness to pay are tested. Results suggest that the proposed chance-constrained assortment price optimization model allows increasing operator profit while keeping the proposed ticket prices acceptable. Investor-Driven Governance Standards and Firm Value Ertimur, Yonca,Patrick, Paige Harrington SSRN We ope-rationalize a corporate governance framework developed and promoted by a diverse, influential group of diverse institutional investors. We find positive associations between consistency with the proposed framework and firm value for smaller firms in recent years, and negative associations for S&P 500 firms in recent years. We detect some evidence of improved monitoring outcomes for firms whose governance provisions are more consistent with provisions eventually included in the framework. However, we do not find that measures of consistency with the framework are more strongly associated with firm outcomes than the much simpler entrenchment index is. Is Gold a Hedge or Safe Haven Asset during COVIDâ€"19 Crisis? Akhtaruzzaman, Md,Boubaker, Sabri,Lucey, Brian M.,Sensoy, Ahmet SSRN The COVIDâ€"19 pandemic has shaken the global financial markets. Our study examines the role of gold as a safe haven asset during the different phases of this COVIDâ€"19 crisis by utilizing an intraday dataset. The empirical findings show that dynamic conditional correlations (DCCs) between intraday gold and international equity returns (S&P500, Euro Stoxx 50, Nikkei 225, and China FTSE A50 indices) are negative during Phase I (December 31, 2019âˆ'March 16, 2020) of the COVIDâ€"19 pandemic, indicating that gold is a safe haven asset for these stock markets. However, gold has lost its property as a safe haven asset for these markets during Phase II (March 17âˆ'April 24, 2020). The optimal weights of gold in the portfolios of S&P500, Euro Stoxx 50, Nikkei 225 and WTI crude oil has significantly increased during Phase II, suggesting that investors have increased the optimal weights of gold as â€˜flight-to-safety assetsâ€™ during the crisis period. The results also show that hedging costs have significantly increased during Phase II. The hedging effectiveness (HE) index shows that the hedge is effective for portfolios containing gold and major financial assets. Our results are robust to alternative specifications of the DCC-GARCH model. Litigating Dividends or Not? The Case of Derivative Lawsuits Ni, Xiaoran,Zhang, Huilin SSRN Recent anecdotal evidence suggests that high litigation risk may induce firms to cut dividends. By comparison, litigation can be an effective governance tool for shareholders to force firms to distribute cash. Therefore, it is unclear how litigation risk affects dividend payouts on average. To address this issue, we exploit the staggered adoption of universal demand (UD) laws across various U.S. states as quasi-exogenous shocks. We find that firms increase dividend payouts significantly after UD laws raise the hurdle of filing derivative lawsuits. In particular, the adoption of UD laws discourages the emission of cash dividends while encourages the initiation of share repurchases. The main effect is more pronounced for firms faced with higher litigation risk, that are more financially distressed, and operating in more competitive product markets. Our overall findings suggest that excessive threats of derivative lawsuits may dampen financial flexibility and deter the distribution of cash to shareholders. Machine Learning SABR Model of Stochastic Volatility With Lookup Table Lokvancic, Mahir SSRN We present an embarrassingly simple method for supervised learning of SABR modelâ€™s European option price function based on lookup table or rote machine learning. The performance in time domain is comparable to generally used analytic approximations utilized in financial industry. However, unlike the approximation schemes based on asymptotic methods â€" universally deemed fastest â€" the methodology admits arbitrary calculation precision to the true pricing function without detrimental impact on time performance apart from memory access latency. The idea is plainly applicable to any function approximation or supervised learning domain with low dimension. Maintaining Confidence Murphy, David SSRN This paper proposes the solvency/liquidity spiral as an failure mode affecting large financial institutions in the recent crisis. The essential features of this mode are that a combination of funding liquidity risk and investor doubts over the solvency of an institution can lead to its failure.We analyse the failures of Lehman Brothers and RBS in detail, and find considerable support for the spiral model of distress.Our model suggests that a key determinant of the financial stability of many large banks is the confidence of the funding markets. This has consequences for the design of financial regulation, suggesting that capital requirements, liquidity rules, and disclosure should be explicitly constructed so as not just to mitigate solvency risk and liquidity risk, but also to be seen to do so even in stressed conditions. Optimal Equilibria for Multi-dimensional Time-inconsistent Stopping Problems Yu-Jui Huang,Zhenhua Wang arXiv We study an optimal stopping problem under non-exponential discounting, where the state process is a multi-dimensional continuous strong Markov process. The discount function is taken to be log sub-additive, capturing decreasing impatience in behavioral economics. On strength of probabilistic potential theory, we establish the existence of an optimal equilibrium among a sufficiently large collection of equilibria, consisting of finely closed equilibria satisfying a boundary condition. This generalizes the existence of optimal equilibria for one-dimensional stopping problems in prior literature. Past production constrains current energy demands: persistent scaling in global energy consumption and implications for climate change mitigation Timothy J. Garrett,Matheus R. Grasselli,Stephen Keen arXiv Climate change has become intertwined with the global economy. Here, we describe the importance of inertia to continued growth in energy consumption. Drawing from thermodynamic arguments, and using 38 years of available statistics between 1980 to 2017, we find a persistent time-independent scaling between the historical time integral$W$of world inflation-adjusted economic production$Y$, or$W\left(t\right) = \int_0^t Y\left(t'\right)dt'$, and current rates of world primary energy consumption$\mathcal E$, such that$\lambda = \mathcal{E}/W = 5.9\pm0.1$Gigawatts per trillion 2010 US dollars. This empirical result implies that population expansion is a symptom rather than a cause of the current exponential rise in$\mathcal E$and carbon dioxide emissions$C$, and that it is past innovation of economic production efficiency$Y/\mathcal{E}$that has been the primary driver of growth, at predicted rates that agree well with data. Options for stabilizing$C$are then limited to rapid decarbonization of$\mathcal E$through sustained implementation of over one Gigawatt of renewable or nuclear power capacity per day. Alternatively, assuming continued reliance on fossil fuels, civilization could shift to a steady-state economy that devotes economic production exclusively to maintenance rather than expansion. If this were instituted immediately, continual energy consumption would still be required, so atmospheric carbon dioxide concentrations would not balance natural sinks until concentrations exceeded 500 ppmv, and double pre-industrial levels if the steady-state was attained by 2030. Prediction defaults for networked-guarantee loans Dawei Cheng,Zhibin Niu,Yi Tu,Liqing Zhang arXiv Networked-guarantee loans may cause the systemic risk related concern of the government and banks in China. The prediction of default of enterprise loans is a typical extremely imbalanced prediction problem, and the networked-guarantee make this problem more difficult to solve. Since the guaranteed loan is a debt obligation promise, if one enterprise in the guarantee network falls into a financial crisis, the debt risk may spread like a virus across the guarantee network, even lead to a systemic financial crisis. In this paper, we propose an imbalanced network risk diffusion model to forecast the enterprise default risk in a short future. Positive weighted k-nearest neighbors (p-wkNN) algorithm is developed for the stand-alone case -- when there is no default contagious; then a data-driven default diffusion model is integrated to further improve the prediction accuracy. We perform the empirical study on a real-world three-years loan record from a major commercial bank. The results show that our proposed method outperforms conventional credit risk methods in terms of AUC. In summary, our quantitative risk evaluation model shows promising prediction performance on real-world data, which could be useful to both regulators and stakeholders. Predictive Regression with p-Lags and Order-q Autoregressive Predictors Jayetileke, Harshanie L.,Wang, You-Gan,Zhu, Min SSRN This paper considers predictive regressions, where yt is predicted by all p lags of x, here with x being autoregressive of order q, PR(p,q). The literature considers model properties in the cases where p=q. We demonstrate that the current augmented regression method can still reduce the bias in predictive coefficients, but its efficiency depends on correctly specifying both p and q. We propose an estimation framework for the predictive regression, PR(p,q), with a data-driven auto-selection of p and q to achieve the best bias reduction in predictive coefficients. The corresponding hypothesis testing procedure is also derived. The efficiency of the proposed method is demonstrated with simulations. Empirical applications to equity premium prediction illustrate the substantial difference between the estimates of our method and those obtained by the common predictive regressions with p=q. Reducing The Wealth Gap Through Fintech 'Advances' in Consumer Banking and Lending Foohey, Pamela,Martin, Nathalie SSRN Research shows that Black, Latinx, and other minorities pay more for credit and banking services, and that wealth accumulation differs starkly between their households and white households. The link between debt inequality and the wealth gap, however, remains less thoroughly explored, particularly in light of new credit products and debt-like banking services, such as early wage access and other fintech innovations. These innovations both hold the promise of reducing racial and ethnic disparities in lending and bring concerns that they may be exploited in ways that perpetuate inequality. They also come at a time when policy makers are considering how to help communities of color rebuild their wealth, presenting an opportunity to critique policy proposals. This Article leverages that opportunity by synthesizing research about the long-term costs of debt inequality on communities of color, adding an in-depth analysis of several new advances in banking and lending, and proposing several key principles for reducing debt inequality as an input to the wealth gap. Response to Welch (2020): Real Estate Collateral Does Affect Corporate Investment Chaney, Thomas,Sraer, David Alexandre,Thesmar, David SSRN This short note is a response to Welch (2020), who claims that our results in Chaney, Sraer and Thesmar (2012) are not robust. We show that none of his findings invalidate our results. Welch makes three major points. First, he correctly points out that our baseline specification uses a common scaling factor (lagged capital stock) for our dependent (investment) and independent (real estate collateral) variables, creating a mechanical correlation between left- and right-hand side variables. We show in this note that, while this point is formally correct, our results are robust to controlling for or removing entirely this mechanical correlation. Second, Welch correctly, stresses that real estate prices are serially correlated, so that identification of a real estate collateral channel is potentially complex. We show in this note that our results are robust to controlling for the serial correlation in real estate prices. Third, Welch correctly worries about the fact that real estate prices are driven not just by local shocks (MSA or State), but also by common shocks (national). We show in this note that our results are robust to controlling for common national real estate shocks. In other words, while we recognize that Welch raises several important points, we argue that none of those results invalidate the baseline findings in Chaney, Sraer and Thesmar (2012). Yet, some of these objections suggest interesting leads for further analysis on corporate investment. We describe these leads in the note, hoping that they will inspire future research. Scoring Functions for Multivariate Distributions and Level Sets Xiaochun Meng,James W. Taylor,Souhaib Ben Taieb,Siran Li arXiv Interest in predicting multivariate probability distributions is growing due to the increasing availability of rich datasets and computational developments. Scoring functions enable the comparison of forecast accuracy, and can potentially be used for estimation. A scoring function for multivariate distributions that has gained some popularity is the energy score. This is a generalization of the continuous ranked probability score (CRPS), which is widely used for univariate distributions. A little-known, alternative generalization is the multivariate CRPS (MCRPS). We propose a theoretical framework for scoring functions for multivariate distributions, which encompasses the energy score and MCRPS, as well as the quadratic score, which has also received little attention. We demonstrate how this framework can be used to generate new scores. For univariate distributions, it is well-established that the CRPS can be expressed as the integral over a quantile score. We show that, in a similar way, scoring functions for multivariate distributions can be "disintegrated" to obtain scoring functions for level sets. Using this, we present scoring functions for different types of level set, including those for densities and cumulative distributions. To compute the scoring functions, we propose a simple numerical algorithm. We illustrate our proposals using simulated and stock returns data. Sovereign Default Risk and Credit Supply: Evidence from the Euro Area Olli Palmén arXiv Did sovereign default risk affect macroeconomic activity through firms' access to credit during the European sovereign debt crisis? We investigate this question by a estimating a structural panel vector autoregressive model for Italy, Spain, Portugal, and Ireland, where the sovereign risk shock is identified using sign restrictions. The results suggest that decline in the creditworthiness of the sovereign contributed to a fall in private lending and economic activity in several euro-area countries by reducing the value of banks' assets and crowding out private lending. Tail probabilities of random linear functions of regularly varying random vectors Bikramjit Das,Vicky Fasen-Hartmann,Claudia Klüppelberg arXiv We provide a new extension of Breiman's Theorem on computing tail probabilities of a product of random variables to a multivariate setting. In particular, we give a complete characterization of regular variation on cones in$[0,\infty)^d\$ under random linear transformations. This allows us to compute probabilities of a variety of tail events, which classical multivariate regularly varying models would report to be asymptotically negligible. We illustrate our findings with applications to risk assessment in financial systems and reinsurance markets under a bipartite network structure.

The Effects of Access to Credit on Productivity Among Microenterprises: Separating Technological Changes from Changes in Technical Efficiency
Nusrat Abedin Jimi,Plamen Nikolov,Mohammad Abdul Malek,Subal Kumbhakar
arXiv

Improving productivity among farm microenterprises is important, especially in low-income countries where market imperfections are pervasive and resources are scarce. Relaxing credit constraints can increase the productivity of farmers. Using a field experiment involving microenterprises in Bangladesh, we estimate the impact of access to credit on the overall productivity of rice farmers, and disentangle the total effect into technological change (frontier shift) and technical efficiency changes. We find that relative to the baseline rice output per decimal, access to credit results in, on average, approximately a 14 percent increase in yield, holding all other inputs constant. After decomposing the total effect into the frontier shift and efficiency improvement, we find that, on average, around 11 percent of the increase in output comes from changes in technology, or frontier shift, while the remaining 3 percent is attributed to improvements in technical efficiency. The efficiency gain is higher for modern hybrid rice varieties, and almost zero for traditional rice varieties. Within the treatment group, the effect is greater among pure tenant and mixed-tenant farm households compared with farmers that only cultivate their own land.

The Importance of Cognitive Domains and the Returns to Schooling in South Africa: Evidence from Two Labor Surveys
Plamen Nikolov,Nusrat Jimi
arXiv

Numerous studies have considered the important role of cognition in estimating the returns to schooling. How cognitive abilities affect schooling may have important policy implications, especially in developing countries during periods of increasing educational attainment. Using two longitudinal labor surveys that collect direct proxy measures of cognitive skills, we study the importance of specific cognitive domains for the returns to schooling in two samples. We instrument for schooling levels and we find that each additional year of schooling leads to an increase in earnings by approximately 18-20 percent. The estimated effect sizes-based on the two-stage least squares estimates-are above the corresponding ordinary least squares estimates. Furthermore, we estimate and demonstrate the importance of specific cognitive domains in the classical Mincer equation. We find that executive functioning skills (i.e., memory and orientation) are important drivers of earnings in the rural sample, whereas higher-order cognitive skills (i.e., numeracy) are more important for determining earnings in the urban sample. Although numeracy is tested in both samples, it is only a statistically significant predictor of earnings in the urban sample.

The Importance of Compound Risk in the Nexus of COVID-19, Climate Change and Finance
Monasterolo, Irene,Billio, Monica,Battiston, Stefano
SSRN
Current approaches to manage the COVID-19 pandemic have a narrow focus on public health and on the short-term economic and financial repercussions. This prevents us to look at how pandemic risk interplays with sustainable and inclusive development goals in the next decade. To fill this gap, we analyse how risk can compound in the nexus of non-linear interactions among pandemic, climate change and finance. We show that neglecting compound risk can lead to a massive underestimation of losses, which can be amplified by financial complexity, as well as to policies that impose unnecessary trade-offs among the economic recovery, health and climate objectives. To address these challenges, we propose an interdisciplinary research agenda to inform effective policies and improve the resilience of our socio-economic systems.

The Rise of Finance Companies and FinTech Lenders in Small Business Lending
Gopal, Manasa,Schnabl, Philipp
SSRN
We document that finance companies and FinTech Lenders increased lending to small businesses after the 2008 financial crisis. We show that most of the increase substituted for a reduction in lending by banks. In counties where banks had a larger market share before the crisis, finance companies and FinTech lenders increased their lending more. By 2016, the increase in finance company and FinTech lending almost perfectly offset the decrease in bank lending. We control for firms' credit demand by examining lending by different lenders to the same firm, by comparing firms within the same narrow industry, and by comparing firms pledging the same collateral. Consistent with the substitution of bank lending with finance company and FinTech lending, we find limited long-term effects on employment, wages, new business creation, and business expansion. Our results show that finance companies and FinTech lenders are major suppliers of credit to small businesses and played an important role in the recovery from the 2008 financial crisis.

The Theory of Insurance and Gambling
Nyman, John A.
SSRN
This paper suggests that insurance represents a quid pro quo transaction across states of the world and is purchased because consumers desire to transfer income to a state where it is more valued. Preferences for certainty have little to do with the demand for insurance, but uncertainty itself plays a large role because it operates mechanically to make the payout a multiple of the premium. It also suggests that casino and other forms of institutional gambling represent a similar quid pro quo transaction across states of the world and that consumers gamble to transfer income to a state where it is less costly to obtain. Again, preferences for uncertainty do not motivate gambling, but uncertainty does allow for the augmentation of the payout compared to the wager. These motivations do not conflict with the empirical evidence supporting prospect theory and can accommodate the insurance-purchasing gambler. Both the demand and supply sides are included in the definitions of insurance and gambling presented herein.

The Unprecedented Fall in U.S. Revolving Credit
Raveendranathan, Gajendran,Stefanidis, Georgios
SSRN
Revolving credit in the U.S. declined drastically in the last decade after several years of upward trending growth. We show that the Ability to Pay provision of the Credit CARD Act of 2009, which places restrictions on credit card limits, accounts for this decline. Extending a model of revolving credit to analyze this policy, we account for changes in credit statistics by income and age. Although the goal was consumer protection, the policy has led to welfare losses. Even consumers with time inconsistent preferences who could benefit from tighter credit constraints are worse off. An alternative policy considered by policymakers - an interest rate cap - improves welfare.

Theoretical Guarantees for Learning Conditional Expectation using Controlled ODE-RNN
Calypso Herrera,Florian Krach,Josef Teichmann
arXiv

Continuous stochastic processes are widely used to model time series that exhibit a random behaviour. Predictions of the stochastic process can be computed by the conditional expectation given the current information. To this end, we introduce the controlled ODE-RNN that provides a data-driven approach to learn the conditional expectation of a stochastic process. Our approach extends the ODE-RNN framework which models the latent state of a recurrent neural network (RNN) between two observations with a neural ordinary differential equation (neural ODE). We show that controlled ODEs provide a general framework which can in particular describe the ODE-RNN, combining in a single equation the continuous neural ODE part with the jumps introduced by RNN. We demonstrate the predictive capabilities of this model by proving that, under some regularities assumptions, the output process converges to the conditional expectation process.

Vertical vs. Horizontal Policy in a Capabilities Model of Economic Development
Alje van Dam,Koen Frenken
arXiv

Against the background of renewed interest in vertical support policies targeting specific industries or technologies, we investigate the effects of vertical vs. horizontal policies in a combinatorial model of economic development. In the framework we propose, an economy develops by acquiring new capabilities allowing for the production of an ever greater variety of products with an increasing complexity. Innovation policy can aim to expand the number of capabilities (vertical policy) or the ability to combine capabilities (horizontal policy). The model shows that for low-income countries, the two policies are complementary. For high-income countries that are specialised in the most complex products, focusing on horizontal policy only yields the highest returns. We reflect on the model results in the light of the contemporary debate on vertical policy.

What Factors Drive Individual Misperceptions of the Returns to Schooling in Tanzania? Some Lessons for Education Policy
Plamen Nikolov,Nusrat Jimi
arXiv

Evidence on educational returns and the factors that determine the demand for schooling in developing countries is extremely scarce. Building on previous studies that show individuals underestimating the returns to schooling, we use two surveys from Tanzania to estimate both the actual and perceived schooling returns and subsequently examine what factors drive individual misperceptions regarding actual returns. Using ordinary least squares and instrumental variable methods, we find that each additional year of schooling in Tanzania increases earnings, on average, by 9 to 11 percent. We find that on average individuals underestimate returns to schooling by 74 to 79 percent and three factors are associated with these misperceptions: income, asset poverty and educational attainment. Shedding light on what factors relate to individual beliefs about educational returns can inform policy on how to structure effective interventions in order to correct individual misperceptions.

Where Is the Risk in Risk Factors? Evidence from the Vietnam War to the COVID-19 Pandemic.
Geertsema, Paul,Lu, Helen
SSRN
During the COVID-19 pandemic (Jan 2020 - Mar 2020) all of the Fama and French (2018) factors except momentum lost money. Negative payoffs in a bad state would appear to justify the positive premia generated by these risk factors. But this is atypical â€" historically the value, profitability, investment and momentum factors are all more profitable in bear markets. The five non-market factors exhibit their own bull and bear market phases, but these do not correlate with the economic cycle. Factor profitability in bear markets arise primarily from the short side. Biased expectations corrected around earnings announcement offer only a partial explanation.

Why is Dollar Debt Cheaper? Evidence from Peru
GutiÃ©rrez, Bryan,Ivashina, Victoria,Salomao, Juliana
SSRN
In emerging markets, a significant share of corporate loans are denominated in dollars. Using novel data that enables us to see currency and the cost of credit, in addition to several other transaction-level characteristics, we re-examine the reasons behind dollar credit popularity. We find that a dollar-denominated loan has an interest rate that is 2% lower per year than a loan in Peruvian Soles. Expectations of exchange rate movements do not explain this difference. We show that this interest rate differential for lending rates is closely matched by the differential in the deposit market. Our results suggest that the preference for dollar loans is rooted on the local household preference for dollar savings and a banking sector that is closely matching its foreign assets and liabilities. We find that borrower competitive pressure increases the pass-through of this differential.