# Research articles for the 2021-06-14

A News-based Machine Learning Model for Adaptive Asset Pricing
Liao Zhu,Haoxuan Wu,Martin T. Wells
arXiv

The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

A Numerical Approach to Pricing Exchange Options under Stochastic Volatility and Jump-Diffusion Dynamics
Len Patrick Dominic M. Garces,Gerald H. L. Cheang
arXiv

We consider a method of lines (MOL) approach to determine prices of European and American exchange options when underlying asset prices are modelled with stochastic volatility and jump-diffusion dynamics. As the MOL, as with any other numerical scheme for PDEs, becomes increasingly complex when higher dimensions are involved, we first simplify the problem by transforming the exchange option into a call option written on the ratio of the yield processes of the two assets. This is achieved by taking the second asset yield process as the numeraire. We also characterize the near-maturity behavior of the early exercise boundary of the American exchange option and analyze how model parameters affect this behavior. Using the MOL scheme, we conduct a numerical comparative static analysis of exchange option prices with respect to the model parameters and investigate the impact of stochastic volatility and jumps to option prices. We also consider the effect of boundary conditions at far-but-finite limits of the computational domain on the overall efficiency of the MOL scheme. Toward these objectives, a brief exposition of the MOL and how it can be implemented on computing software are provided.

A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface
Wenyong Zhang,Lingfei Li,Gongqiu Zhang
arXiv

We propose a two-step framework for predicting the implied volatility surface over time without static arbitrage. In the first step, we select features to represent the surface and predict them over time. In the second step, we use the predicted features to construct the implied volatility surface using a deep neural network (DNN) model by incorporating constraints that prevent static arbitrage. We consider three methods to extract features from the implied volatility data: principal component analysis, variational autoencoder and sampling the surface, and we predict these features using LSTM. Using a long time series of implied volatility data for S\&P500 index options to train our models, we find that sampling the surface with DNN for surface construction achieves the smallest error in out-of-sample prediction. Furthermore, the DNN model for surface construction not only removes static arbitrage, but also significantly reduces the prediction error compared with a standard interpolation method. Our framework can also be used to simulate the dynamics of the implied volatility surface without static arbitrage.

A general framework for a joint calibration of VIX and VXX options
Martino Grasselli,Andrea Mazzoran,Andrea Pallavicini
arXiv

We analyze the VIX futures market with a focus on the exchange-traded notes written on such contracts, in particular we investigate the VXX notes tracking the short-end part of the futures term structure. Inspired by recent developments in commodity smile modelling, we present a multi-factor stochastic-local volatility model that is able to jointly calibrate plain vanilla options both on VIX futures and VXX notes, thus going beyond the failure of purely stochastic or simply local volatility models. We discuss numerical results on real market data by highlighting the impact of model parameters on implied volatilities.

A new measure to study erratic financial behaviors and time-varying dynamics of equity markets
Nick James,Max Menzies
arXiv

This paper introduces a new framework to quantify distance between finite sets with uncertainty present, where probability distributions determine the locations of individual elements. Combining this with a Bayesian change point detection algorithm, we produce a new measure of similarity between time series with respect to their structural breaks. Next, we apply this to financial data to study the erratic behavior profiles of 19 countries and 11 sectors over the past 20 years. Then, we take a closer examination of individual equities and their behavior surrounding market crises, times when change points are consistently observed. Combining new and existing methods, we study the dynamics of our collection of equities and highlight an increase in equity similarity in recent years, particularly during such crises. Finally, we show that our methodology may provide a new outlook on diversification and risk-reduction during times of extraordinary correlation between assets, where traditional portfolio optimization algorithms encounter difficulties.

Active Carbon Attribution: Avoid Carbon Greenwashing
Bolliger, Guido,Cornilly, Dries
SSRN
This paper proposes a method to decompose the carbon intensity of a portfolio with respect to a benchmark into an allocation and a selection component. The carbon intensity decomposition allows to better understand the source of the difference between the carbon footprint of a portfolio and that of its benchmark. As such, it prevents greenwashing by looking whether the carbon exposure of a portfolio results from active stock selection choices on the part of the manager or from passive sector exclusion decisions. Our approach is based on methods developed for performance attribution. We discuss an equity example using the MSCI ACWI Sustainable Impact Index and a fixed income example around the ICE BofA Global Corporate Green Bond Index. In the latter example, we show that a higher portfolio carbon intensity does not necessarily contradict the portfolioâ€™s stated ESG or Impact objectives. Our methodology can easily be extended to any other sustainability or impact metric that is constructed as a weighted average of asset scores.

Algorithmic market making in foreign exchange cash markets: a new model for active market makers
Alexander Barzykin,Philippe Bergault,Olivier Guéant
arXiv

In OTC markets, one of the main tasks of dealers / market makers consists in providing prices at which they agree to buy and sell the assets and securities they have in their scope. With ever increasing trading volume, this quoting task has to be done algorithmically. Over the last ten years, many market making models have been designed that can be the basis of quoting algorithms in OTC markets. However, in the academic literature, most market making models adapted to OTC markets are general and only a few focus on specific market characteristics. In particular, to the best of our knowledge, in all OTC market making models, the market maker only sets quotes and/or waits for clients. However, on many markets such as foreign exchange cash markets, market makers have access to liquidity pools where they can unwind part of their inventory. In this paper, we propose a model taking this possibility into account, therefore allowing market makers to trade actively'' in the market. The model displays an important feature well known to practitioners that in a certain inventory range the market maker does not actually want to capitalize on this active trading opportunity but should rather internalize'' the flow by appropriately adjusting the quotes. The larger the market making franchise, the wider is the inventory range suitable for internalization. The model is illustrated numerically with realistic parameters for USDCNH spot market.

An Integration and Operation Framework of Geothermal Heat Pumps in Distribution Networks
Lei Liang,Xuan Zhang,Hongbin Sun
arXiv

The application of the energy-efficient thermal and energy management mechanism in Geothermal Heat Pumps (GHPs) is indispensable to reduce the overall energy consumption and carbon emission across the building sector. Besides, in the Virtual Power Plant (VPP) system, the demand response of clustered GHP systems can improve the operating flexibility of the power grid. This paper presents an integration and operation framework of GHPs in the distribution network, applying a layered communication and optimization method to coordinate multiple clustered GHPs in a community. In the proposed hierarchical operation scheme, the operator of regional GHPs collects the thermal zone information and the disturbance prediction of buildings in a short time granularity, predicts the energy demand, and transmits the information to an aggregator. Using a novel linearized optimal power flow model, the aggregator coordinates and aggregates load resources of GHP systems in the distribution network. In this way, GHP systems with thermal and energy management mechanisms can be applied to achieve the demand response in the VPP and offer more energy flexibility to the community.

Arbitrage Capital of Global Banks
Anderson, Alyssa G.,Du, Wenxin,Schlusche, Bernd
SSRN
We show that the role of unsecured, short-term wholesale funding for global banks has changed significantly in the post-financial-crisis regulatory environment. Global banks mainly use such funding to finance liquid, near risk-free arbitrage positions---in particular, the interest on excess reserves arbitrage and the covered interest rate parity arbitrage. In this environment, we examine the response of global banks to a large negative wholesale funding shock as a result of the U.S. money market mutual fund reform implemented in 2016. In contrast to past episodes of wholesale funding dry-ups, we find that the primary response of global banks to the reform was a cutback in arbitrage positions that relied on unsecured funding, rather than a reduction in loan provision.

Are Repo Markets Fragile? Evidence from September 2019
Anbil, Sriya,Anderson, Alyssa G.,Senyuz, Zeynep
SSRN
We show that the segmented structure of the U.S. Treasury repo market, in which some participants have limited access across the segments, leads to rate dispersion, even in this essentially riskless market. Using confidential data on repo trading, we demonstrate how the rate dispersion between the centrally cleared and over-the-counter (OTC) segments of the Treasury repo market was exacerbated during the stress episode of September 2019. Our results highlight that, while segmentation can increase fragility in the repo market, the presence of strong trading relationships in the OTC segment helps mitigate it by reducing rate dispersion.

Capital Buï¬€ers in a Quantitative Model of Banking Industry Dynamics
D'Erasmo, Pablo
SSRN
We develop a model of banking industry dynamics to study the quantitative impact of regulatory policies on bank risk taking and market structure as well as the feedback eï¬€ect of market structure on the eï¬ƒcacy of policy. Since our model is matched to U.S. data, we propose a market structure where big banks with market power interact with small, competitive fringe banks. Banks face idiosyncratic funding shocks in addition to aggregate shocks which aï¬€ect the fraction of performing loans in their portfolio. A nontrivial bank size distribution arises out of endogenous entry and exit, as well as banksâ€™ buï¬€er stock of net worth. We show the model predictions are consistent with untargeted business cycle properties, the bank lending channel, and empirical studies of the role of concentration on ï¬nancial stability. We then conduct a series of policy counterfactuals motivated by those proposed in the Dodd-Frank Act (size and state dependent capital requirements and liquidity requirements). We ï¬nd that regulatory policies can have an important impact on banking market structure, which, along with selection eï¬€ects, can generate changes in allocative eï¬ƒciency and stability.

Cheapest-to-Deliver Pricing, Optimal MBS Securitization, and Market Quality
Huh, Yesol,Kim, You Suk
SSRN
We study optimal securitization and its impact on market quality when the secondary market structure leads to cheapest-to-deliver pricing in the context of agency mortgage-backed securities (MBS). A majority of MBS are traded in the to-be-announced (TBA) market, which concentrates trading of heterogeneous MBS into a few liquid TBA contracts but induces adverse selection. We find that lenders segregate loans of like values into separate pools and tend to trade low-value MBS in the TBA market and high-value MBS outside the TBA market. We then present a model of optimal securitization for agency MBS. Lenders do not internalize the negative impact of such pooling and trading strategies on TBA market quality and thus create too many high-value MBS, which leads to more heterogeneity in MBS values, more adverse selection, and lower TBA liquidity. Lastly, we provide empirical evidence consistent with model predictions on how MBS pooling changes with trading costs and underlying loan value dispersion and how pooling practices affect MBS heterogeneity and TBA market adverse selection.

Computation of bonus in multi-state life insurance
arXiv

We consider computation of market values of bonus payments in multi-state with-profit life insurance. The bonus scheme consists of additional benefits bought according to a dividend strategy that depends on the past realization of financial risk, the current individual insurance risk, the number of additional benefits currently held, and so-called portfolio-wide means describing the shape of the insurance business. We formulate numerical procedures that efficiently combine simulation of financial risk with more analytical methods for the outstanding insurance risk. Special attention is given to the case where the number of additional benefits bought only depends on the financial risk. Methods and results are illustrated via a numerical example.

Consumption-Based Asset Pricing When Consumers Make Mistakes
Anderson, Chris
SSRN
I analyze the implications of allowing consumers to make mistakes on the risk-return relationships predicted by consumption-based asset pricing models. I allow for consumption mistakes using a model in which a portfolio manager selects investments on a consumer's behalf. The consumer has an arbitrary consumption policy that could reflect a wide range of mistakes. For power utility, expected returns do not generally depend on exposure to single-period consumption shocks, but robustly depend on exposure to both long-run consumption and expected return shocks. I empirically show that separately accounting for both types of shocks helps explain the equity premium and cross section of stock returns.

Credit spread approximation and improvement using random forest regression
arXiv

Credit Default Swap (CDS) levels provide a market appreciation of companies' default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and proprietary approximations currently in use. This Equity-to-Credit formula (E2C), inspired by CreditGrades, obtains better CDS approximations, according to empirical analyses based on a large sample spanning 2016-2018. A random forest regression run with this E2C formula and selected additional financial data results in an 87.3% out-of-sample accuracy in CDS approximations. The transparency property of this algorithm confirms the predominance of the E2C estimate, and the impact of companies' debt rating and size, in predicting their CDS.

Distributionally Robust Martingale Optimal Transport
Zhengqing Zhou,Jose Blanchet,Peter W. Glynn
arXiv

We study the problem of bounding path-dependent expectations (within any finite time horizon $d$) over the class of discrete-time martingales whose marginal distributions lie within a prescribed tolerance of a given collection of benchmark marginal distributions. This problem is a relaxation of the martingale optimal transport (MOT) problem and is motivated by applications to super-hedging in financial markets. We show that the empirical version of our relaxed MOT problem can be approximated within $O\left( n^{-1/2}\right)$ error where $n$ is the number of samples of each of the individual marginal distributions (generated independently) and using a suitably constructed finite-dimensional linear programming problem.

Do Economic Surprises Explain Returns of Stocks: The Case of COVID-19 Pandemic
Ben Amar, Amine,Mzoughi, Hela,Guesmi, Khaled
SSRN
This paper proposes a new measure of epidemic uncertainty combining three dimensions related to the SARS-CoV-2 disease â€' (i) the total COVID-19 confirmed cases, (ii) the total COVID-19 confirmed deaths and (iii) the total COVID-19 recovered cases â€' to show how financial and macroeconomic variables respond to epidemic risk. Using the cross-wavelet coherence, we investigate the relationship between the American and European stock markets and three measures of uncertainty (financial, economic, and epidemic) in the time-frequency domain. Our empirical analysis confirms the close out-of-phase link between financial uncertainty and markets, and suggests that the impact of the epidemic uncertainty, on both the U.S. and European stock markets, exhibit different patterns over time and across frequencies.

Does Retrenchment Boost Performance? Evidence from Fallen Angels
Farroukh, Abed El Karim,Koski, Jennifer L.,Werner, Ingrid M.
SSRN
We study restructuring by firms whose stock prices experience a sharp decline to a low price levelâ€" fallen angels. In response to a price decline, firms can retrench by reducing investments and cutting the workforce, or increase leverage and investments hoping for lottery-like payoffs. We find that relative to a matched sample, fallen angels retrench. While retrenchment helps boost stock prices, reducing fixed assets and employment also increase firm risk, lower growth opportunities, and reduce the probability a firm remains listed. We find no consistent evidence that retrenchment actions undertaken by fallen angels affect future operating performance.

Duality Theory for Robust Utility Maximisation
Daniel Bartl,Michael Kupper,Ariel Neufeld
arXiv

In this paper we present a duality theory for the robust utility maximisation problem in continuous time for utility functions defined on the positive real axis. Our results are inspired by -- and can be seen as the robust analogues of -- the seminal work of Kramkov & Schachermayer [18]. Namely, we show that if the set of attainable trading outcomes and the set of pricing measures satisfy a bipolar relation, then the utility maximisation problem is in duality with a conjugate problem. We further discuss the existence of optimal trading strategies. In particular, our general results include the case of logarithmic and power utility, and they apply to drift and volatility uncertainty.

Epidemic Financing Facilities: Pandemic Bonds and Endemic Swaps
Huang, Shimeng,Tan, Ken Seng,Zhang, Jinggong,Zhu, Wenjun
SSRN
While the COVID-19 pandemic has shattered the world with severe human toll and catastrophic economic losses and sufferings, it has also heightened the need for more effective solutions for managing epidemic-related risks. In this paper, we pro- pose two capital market-based epidemic financing facilities to address two extremes of epidemic risks. The proposed pandemic bond is meant to hedge the severe pandemic outbreak while the proposed endemic swap can be used to hedge a recurrent endemic. Using coronavirus as an example of a pandemic and dengue fever as an example of an endemic, we discuss the modeling and pricing of the proposed epidemic securities. We price the proposed securities based on epidemiological models as well as actuarial models. We show that the proposed hedging securities can provide additional capital relief for pandemic recovery plans, effectively stablize hedgersâ€™ cash flows, and create attractive returns to different investors.

Exogenous and Endogenous Price Jumps Belong to Different Dynamical Classes
Riccardo Marcaccioli,Jean-Philippe Bouchaud,Michael Benzaquen
arXiv

Synchronising a database of stock specific news with 5 years worth of order book data on 300 stocks, we show that abnormal price movements following news releases (exogenous) exhibit markedly different dynamical features from those arising spontaneously (endogenous). On average, large volatility fluctuations induced by exogenous events occur abruptly and are followed by a decaying power-law relaxation, while endogenous price jumps are characterized by progressively accelerating growth of volatility, also followed by a power-law relaxation, but slower than for exogenous jumps. Remarkably, our results are reminiscent of what is observed in different contexts, namely Amazon book sales and YouTube views. Finally, we show that fitting power-laws to {\it individual} volatility profiles allows one to classify large events into endogenous and exogenous dynamical classes, without relying on the news feed.

Financial Derivatives During the COVID-19 Health Crisis
Al Janabi, Mazin A. M.
SSRN
La versiÃ³n espaÃ±ola de este artÃ­culo se puede encontrar en: http://ssrn.com/abstract=3858999During the last years, there has been an increase in the use of derivative instruments, in addition to significant losses reported by companies and financial institutions, putting the appropriate use of this type of product in the spotlight. These instruments supposedly reduce financial risks, but, paradoxically, they are accused of producing new risks. However, are the derivatives themselves or their misuse to blame for these losses? As such, a greater understanding of derivative products and their implicit risks in market disruption events is urgently required.

Geometrically Convergent Simulation of the Extrema of L\'{e}vy Processes
Jorge Ignacio González Cázares,Aleksandar Mijatović,Gerónimo Uribe Bravo
arXiv

We develop a novel approximate simulation algorithm for the joint law of the position, the running supremum and the time of the supremum of a general L\'evy process at an arbitrary finite time. We identify the law of the error in simple terms. We prove that the error decays geometrically in $L^p$ (for any $p\geq 1$) as a function of the computational cost, in contrast with the polynomial decay for the approximations available in the literature. We establish a central limit theorem and construct non-asymptotic and asymptotic confidence intervals for the corresponding Monte Carlo estimator. We prove that the multilevel Monte Carlo estimator has optimal computational complexity (i.e. of order $\epsilon^{-2}$ if the mean squared error is at most $\epsilon^2$) for locally Lipschitz and barrier-type functionals of the triplet and develop an unbiased version of the estimator. We illustrate the performance of the algorithm with numerical examples.

Hedging with Linear Regressions and Neural Networks
Johannes Ruf,Weiguan Wang
arXiv

We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.

High-Frequency Estimates of the Natural Real Rate and Inflation Expectations
Aronovich, Alex,Meldrum, Andrew
SSRN
We propose a new method of estimating the natural real rate and long-horizon inflation expectations, using nonlinear regressions of survey-based measures of short-term nominal interest rates and inflation expectations on U.S. Treasury yields. We find that the natural real rate was relatively stable during the 1990s and early 2000s, but declined steadily after the global financial crisis, before dropping more sharply to around 0 percent during the recent COVID-19 pandemic. Long-horizon inflation expectations declined steadily during the 1990s and have since been relatively stable at close to 2 percent. According to our method, the declines in both the natural real rate and long-horizon inflation expectations are clearly statistically significant. Our estimates are available at whatever frequency we observe bond yields, making them ideal for intraday event-study analysis--for example, we show that the natural real rate and long-horizon inflation expectations are not affected by temporary shocks to the stance of monetary policy.

International Yield Spillovers
Kim, Don,Ochoa, Marcelo
SSRN
This paper investigates spillovers from foreign economies to the U.S. through changes in longterm Treasury yields. We document a decline in the contribution of U.S. domestic news to the variance of long-term Treasury yields and an increased importance of overnight yield changesâ€"a rough proxy for the contribution of foreign shocks to U.S. yieldsâ€"over the past decades. Using a model that identifies U.S., Euro area, and U.K. shocks that move global yields, we estimate that foreign (non-U.S.) shocks account for at least 20 percent of the daily variation in long-term U.S. yields in recent years. We argue that spillovers occur in large part through bond term premia by showing that a low level of foreign yields relative to U.S. yields predicts a decline in distant forward U.S. yields and higher returns on a strategy that is long on a long-term Treasury security and short on a long-term foreign bond.

Is Lending Distance Really Changing? Distance Dynamics and Loan Composition in Small Business Lending
SSRN
Has information technology improved small businesses' access to credit by hardening the information used in loan underwriting and reducing the importance of proximity to lenders? Previous research, pointing to increasing average lending distances, suggests that it has. But this conclusion can obscure differences across loans and lenders. Using over 20 years of Community Reinvestment Act data on small business lending, we find that while average distances have increased substantially, distances at individual banks remain unchanged. Instead, average distance has increased because a small group of lenders specializing in high-volume, small-loan lending nationwide have increased their share of small business lending by 10 percentage points. Our findings imply that small businesses continue to depend on local banks.

Liquidity Networks, Interconnectedness, and Interbank Information Asymmetry
Brunetti, Celso
SSRN
Network analysis has demonstrated that interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately affect market health. In this paper, we propose a new directed network construct, the liquidity network, to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types complement each other: Liquidity networks reveal valuable information, particularly when information asymmetry in the market is high, and provide a more comprehensive characterization of interconnectivity in the overnight-lending market.

Los derivados financieros durante la crisis de Covid-19 (Financial Derivatives During the COVID-19 Health Crisis)
Al Janabi, Mazin A. M.
SSRN
The English version of this paper can be found at http://ssrn.com/abstract=3858991Spanish Abstract: En el Ãºltimo aÃ±o, el aumento del uso de instrumentos derivados, aunado a las importantes pÃ©rdidas que han reportado empresas e instituciones financieras, han hecho saltar las alarmas sobre el uso apropiado de este tipo de producto. Se supone que estos instrumentos reducen los riesgos financieros, pero paradÃ³jicamente se les acusa de propiciar nuevos riesgos. Pero, Â¿son los derivados culpables de estas pÃ©rdidas o lo es su uso indebido? Como tal, urge una mayor comprensiÃ³n de los productos derivados y sus riesgos implÃ­citos ante eventuales disrupciones en el mercado.English Abstract: During the last years, there has been an increase in the use of derivative instruments, in addition to significant losses reported by companies and financial institutions, putting the appropriate use of this type of product in the spotlight. These instruments supposedly reduce financial risks, but, paradoxically, they are accused of producing new risks. However, are the derivatives themselves or their misuse to blame for these losses? As such, a greater understanding of derivative products and their implicit risks in market disruption events is urgently required.

Moderating Effect of Industry Concentration on the Effect of Camels Financial Indicators on Financial Performance of Deposit Money Banks in Nigeria
Ahmed Maude, Fatima
SSRN
In Nigeria, the banking industry has witnessed many upheavals over the years, which led to the collapse of quite a number of banks. Despite several effort by government to reposition the industry to improve its efficiency and achieve overall stability of the financial system in view of its strategic importance to the countryâ€™s economy, the financial performance of banks seems to be dwindling. This study assessed the moderating effect of industry concentration on the effect of CAMELS financial indicators on financial performance of Deposit Money Banks (DMBs) in Nigeria using secondary data extracted from the financial statements of 15 out of 22 licensed banks over the period of nine years (2010-2018). The study formulated thirteen hypotheses and applied multiple regression technique to assess the causal relationship between the dependent variable, Return on Assets (ROA) and the independent variables, CAMELS, which as a set of financial indicators stands for capital adequacy, assets quality, management efficiency, earnings ability, liquidity and sensitivity to market risk. The study also analyzed the moderating effect of the interaction between industry concentration and each of the CAMELS variables on financial performance of the banks. After subjecting the data to relevant tests of robustness, the result of the clustered robust random effects regression revealed that overall, the interaction between industry concentration and CAMELS financial indicators has significant effect on financial performance of DMBs in Nigeria. In terms of specific variables, asset quality, management efficiency, liquidity and sensitivity to market risk have significant effect on financial performance. With respect to the interaction variables, the interaction between industry concentration and each of asset quality, management efficiency and earnings ability have statistically significant effect on financial performance. The study therefore concluded that overall, CAMELS financial indicators have significant effect on financial performance of DMBs in Nigeria and its effect is significantly moderated by industry concentration. Based on the results, the study recommended among other things that CBN should continue to promote and encourage industry concentration strategy in the banking sector through policies that will make banks put more effort in deposit mobilization, costs cutting and providing better and efficient financial intermediation services in order to make high profit. The CBN should also carry out its risk-based and risk assets on-site examination function at least twice in a year as against the current practice of once in a year in order to increase its surveillance on banks and ensure that imprudent and unethical behavior that erode the quality of assets and capital adequacy are spotted not only in the beginning or end of a financial year but also in the middle of the year. Furthermore, management of DMBs should look into the quality of their assets by coming up with lending policies and applying loan recovery strategies that will help in increasing performing loans and decreasing non-performing loans.

Monetary Policy and Financial Stability
Cairo, Isabel,Sim, Jae
SSRN
The 2008 Global Financial Crisis called into question the narrow focus on price stability of inflation targeting regimes. This paper studies the relationship between price stability and financial stability by analyzing alternative monetary policy regimes for an economy that experiences endogenous financial crises due to excessive household sector leverage. We reach four conclusions. First, a central bank can improve both price stability and financial stability by adopting an aggressive inflation targeting regime, in the absence of the zero lower bound (ZLB) constraint on nominal interest rates. Second, in the presence of the ZLB constraint, an aggressive inflation targeting regime may undermine both price stability and financial stability. Third, an aggressive price-level targeting regime can improve both price stability and financial stability, regardless of the presence of the ZLB constraint. Finally, a leaning against the wind policy can be detrimental to both price stability and financial stability when the credit cycle is driven by countercyclical household sector leverage. In this environment, leaning with credit spreads can be more effective.

Optimal Debt Dynamics, Issuance Costs, and Commitment
Benzoni, Luca,Garlappi, Lorenzo,Goldstein, Robert S.,Hugonnier, Julien,Ying, Chao
SSRN
We investigate optimal capital structure and debt maturity policies in the presence of fixed issuance costs. We identify the global-optimal policy that generates the highest values of equity across all states of nature consistent with limited liability. The optimal policy without commitment provides almost as much tax benefits to debt as does the global-optimal policy and, in the limit of vanishing issuance costs, allows firms to extract 100% of EBIT. This limiting case does not converge to the equilibrium of DeMarzo and He (2019), who report no tax benefits to debt when issuance costs are set to zero at the outset.

Overnight Rrp Operations as a Monetary Policy Tool: Some Design Considerations
Frost, Joshua,Logan, Lorie,McCabe, Patrick E.,Natalucci, Fabio M.,Remache, Julie
SSRN
We review recent changes in monetary policy that have led to development and testing of an overnight reverse repurchase agreement (ON RRP) facility, an innovative tool for implementing monetary policy during the normalization process. Making ON RRPs available to a broad set of investors, including nonbank institutions that are significant lenders in money markets, could complement the use of the interest on excess reserves (IOER) and help control short-term interest rates. We examine some potentially important secondary effects of an ON RRP facility, both positive and negative, including impacts on the structure of short-term funding markets and financial stability. We also investigate design features of an ON RRP facility that could mitigate secondary effects deemed undesirable. Finally, we discuss tradeoffs that policymakers may face in designing an ON RRP facility, as they seek to balance the objectives of setting an effective floor on money market rates during t he normalization process and limiting any adverse secondary effects.

Probabilistic Forecasting of Imbalance Prices in the Belgian Context
Jonathan Dumas,Ioannis Boukas,Miguel Manuel de Villena,Sébastien Mathieu,Bertrand Cornélusse
arXiv

Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel two-step probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists of computing the net regulation volume state transition probabilities. It is modeled as a matrix computed using historical data. This matrix is then used to infer the imbalance prices since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery. This approach is compared to a deterministic model, a multi-layer perceptron, and a widely used probabilistic technique, Gaussian Processes.

Quantum Portfolio Optimization with Investment Bands and Target Volatility
Samuel Palmer,Serkan Sahin,Rodrigo Hernandez,Samuel Mugel,Roman Orus
arXiv

In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms. Specifically, first we explain how to obtain the best investment portfolio with a given target risk. This is important in order to produce portfolios with different risk profiles, as typically offered by financial institutions. Second, we show how to implement individual investment bands, i.e., minimum and maximum possible investments for each asset. This is also important in order to impose diversification and avoid corner solutions. Quite remarkably, we show how to build the constrained cost function as a quadratic binary optimization (QUBO) problem, this being the natural input of quantum annealers. The validity of our implementation is proven by finding the efficient frontier, using D-Wave Hybrid and its Advantage quantum processor, on static portfolios taking assets from the S&P500. We use three different subsets of this index. First, the S&P100 which consists of 100 of the largest companies of the S&P500; second, the 200 best-performing companies of the S&P500; and third, the full S&P500 itself. Our results show how practical daily constraints found in quantitative finance can be implemented in a simple way in current NISQ quantum processors, with real data, and under realistic market conditions. In combination with clustering algorithms, our methods would allow to replicate the behaviour of more complex indexes, such as Nasdaq Composite or others, in turn being particularly useful to build and replicate Exchange Traded Funds (ETF).

Sowing the Seeds of Financial Imbalances: The Role of Macroeconomic Performance
Afanasyeva, Elena,Jerow, Sam,Lee, Seung Jung,Modugno, Michele
SSRN
The seeds of financial imbalances are sown in times of buoyant economic growth. We study the link between macroeconomic performance and financial imbalances, focusing on the experience of the United States since the 1960s. We first follow a narrative approach to review historical episodes of significant financial imbalances and find that the onset of financial disturbances typically occurs when the economy is running hot. We then look for evidence of a statistical link between measures of macroeconomic conditions and financial imbalances. In our in-sample analysis, we find that strong economic growth is followed by a build-up of financial imbalances across all dimensions of the National Financial Conditions Index. In our out-of-sample analysis, we find that the link between strong economic performance and increases in nonfinancial leverage is particularly strong and robust. Using a structural VAR identified with narrative sign restrictions, we also demonstrate that business cycle shocks are important drivers of non financial leverage.

Ten Days Late and Billions of Dollars Short: The Employment Effects of Delays in Paycheck Protection Program Financing
Doniger, Cynthia,Kay, Benjamin S.
SSRN
Delay in the provision of Paycheck Protection Program (PPP) loans due to insufficient initial funding under the CARES Act substantially and persistently reduced employment. Delayed loans increased job losses in May and persistently reduced recalls throughout the summer. The magnitude and heterogeneity of effects suggest significant barriers to obtaining external financing, particularly among small firms. Effects are inequitably distributed: larger among the self-employed, less well paid, less well educated and--importantly for the design of future programs--in very small firms. Our estimates imply the PPP saved millions of jobs but larger initial funding could have saved millions more, particularly if it had been directed toward the smallest firms. About half of the jobs lost to insufficient PPP funding are lost in firms with fewer than 10 employees, despite such firms accounting for less than 20 percent of employment.

Testing the Overreaction Hypothesis in the Mexican Stock Market
SSRN
The objective of this work is to test the overreaction hypothesis in the Mexican Stock Market for the period of 2002-2015, using monthly data and applying the Cumulative Average Residuals (CAR) methodology via the CAPM model and the three-factor model proposed by Fama and French. The CAR model is applied to test how winner and loser portfolios perform during the period under analysis. Overall, the evidence shows that average CAR for the loser portfolio is 0.706%, whereas CAR for the winner portfolio is 0.364%, and that are statistically different; nevertheless, both portfolios are co-integrated. This research contributes to the financial literature identifying overreaction in the Mexican Stock Market during the period examined.

The Dollar and Corporate Borrowing Costs
Meisenzahl, Ralf,Niepmann, Friederike,Schmidt-Eisenlohr, Tim
SSRN
We show that U.S. dollar movements affect syndicated loan terms for U.S. borrowers, even for those without trade exposure. We identify the effect of dollar movements using spread and loan amount adjustments during the syndication process. Using this high-frequency, within loan variation, we find that a one standard deviation increase in the dollar index increases spreads by up to 15 basis points and reduces loan amounts and underpricing by up to 2 percent and 7 basis points, respectively. These effects are concentrated in dollar appreciations. Our results suggest that global factors reflected in the dollar affect U.S. borrowing costs.

The Effect of the Ppplf on PPP Lending by Commercial Banks
Anbil, Sriya,Carlson, Mark A.,Styczynski, Mary-Frances
SSRN
We analyze whether the Federal Reserve's Paycheck Protection Program Liquidity Facility (PPPLF) was successful in bolstering the ability of commercial banks to provide credit to small businesses under the Small Business Administration's Paycheck Protection Program (PPP). Using an instrumental variables approach, we find a causal effect of the facility boosting PPP lending. On average, commercial banks that used the PPPLF extended over twice as many PPP loans, relative to their total assets, as banks that did not use the PPPLF. Our instrument is a measure of banks' familiarity with the operation of the Federal Reserveâ€™s discount window; this measure is strongly related to both the propensity to sign up for and to utilize the PPPLF. Further, using a similar instrumental variables approach, we find evidence that the availability of the facility as a backstop source of funds may also have supported bank PPP lending, especially for larger banks.

The Financial (In)Stability Real Interest Rate, R*
Akinci, Ozge,Del Negro, Marco,Queralto, Albert
SSRN
We introduce the concept of financial stability real interest rate using a macroeconomic banking model with an occasionally binding financing constraint as in Gertler and Kiyotaki (2010). The financial stability interest rate, r**, is the threshold interest rate that triggers the constraint being binding. Increasing imbalances in the financial sector measured by an increase in leverage are accompanied by a lower threshold that could trigger financial instability events. We also construct a theoretical implied financial condition index and show how it is related to the gap between the natural and financial stability interest rates.

The Global Determinants of International Equity Risk Premiums
Londono, Juan M.
SSRN
We examine the commonality in international equity risk premiums by linking empirical evidence for the international stock return predictability of US downside and upside variance risk premiums (DVP and UVP, respectively) with implications from an international asset pricing framework, which takes the perspective of a US/global investor and features asymmetric global macroeconomic, financial market, and risk aversion shocks. We find that DVP and UVP predict international stock returns through different global equity risk premium determinants: bad and good macroeconomic uncertainties, respectively. Across countries, US investors demand lower macroeconomic risk compensation but higher financial market risk compensation for more-integrated countries.

The Global Transmission of Real Economic Uncertainty
Londono, Juan M.,Ma, Sai,Wilson, Beth Anne
SSRN
Using a sample of 30 countries representing about 65% of the global GDP, we find that real economic uncertainty (REU) has negative long-lasting domestic economic effects and transmits across countries. The international spillover effects of REU are (i) additional to those of domestic REUs, (ii) statistically significant, and (iii) economically meaningful. Trade ties play a key role in explaining why uncertainty generated in one country can affect economic outcomes in other countries. Based on this evidence, we construct a novel index for global REU as the trade-weighted average of all countries' REUs. We disentangle the effects of the domestic and foreign components of global REU and find that, on average, innovations to the foreign component can contribute up to 28% of the future variation in domestic industrial production, with the effect being disproportionately larger on its manufacturing component, the component contributing the most to the tradable goods sector, than on its retail sales component.

The Information Content of Stress Test Announcements
Guerrieri, Luca,Modugno, Michele
SSRN
We exploit institutional features of the U.S. banking stress tests to disentangle different types of information garnered by market participants when the stress test results are released. By examining the reaction of different asset prices, we find evidence that market participants value the stress test announcements not only for the information on possible future capital distributions but also for the signals about bank resilience. These results back the use of stress tests by central banks to inform the broader public about the soundness of the banking system.

The Internal Capital Markets of Global Dealer Banks
Gupta, Arun
SSRN
This study uncovers the existence of a trillion-dollar internal capital market that played a central role in the financing of dealer banks during the 2008 Global Financial Crisis. Hand-collecting a novel set of dealer microdata at the subsidiary level, I present the first set of facts on the evolution of interaffiliate loans between U.S. primary dealers and their (primarily foreign) siblings. First, the aggregate size of these dealer internal capital markets quadrupled from $335 billion in 2001 to$1.2 trillion by 2007. Second, 25 percent of total repurchase agreements and 61 percent of total securities lending reported on U.S. primary dealer balance sheets were sourced internally from sibling dealers by year-end 2007. Third, internal securities lending collapsed by 55 percent during the 2008 crisis. These facts suggest that incorporating internal capital market dynamics may be fruitful for future research on dealer behavior and market liquidity.

The Long-Term Effects of Capital Requirements
De Nicolo, Gianni,Klimenko, Nataliya,Pfeil, Sebastian,Rochet, Jean-Charles
SSRN
We build a stylized dynamic general equilibrium model with financial frictions to analyze costs and benefits of capital requirements in the short-term and long-term. We show that since increasing capital requirements limits the aggregate loan supply, the equilibrium loan rate spread increases, which raises bank profitability and the market-to-book value of bank capital. Hence, banks build up larger capital buffers which (i) lowers the public losses in case of a systemic crisis and (ii) restores the banking sectorâ€™s lending capacity after the short-term credit crunch induced by tighter regulation. We confirm our modelâ€™s dynamic implications in a panel VAR estimation, which suggests that bank lending has even increased in the long-run after the implementation of Basel III capital regulation.

The Power of Narratives in Economic Forecasts
Hollrah, Christopher A.,Sharpe, Steven A.,Sinha, Nitish Ranjan
SSRN
We apply textual analysis tools to the narratives that accompany Federal Reserve Board economic forecasts to measure the degree of optimism versus pessimism expressed in those narratives. Text sentiment is strongly correlated with the accompanying economic point forecasts, positively for GDP forecasts and negatively for unemployment and inflation forecasts. Moreover, our sentiment measure predicts errors in FRB and private forecasts for GDP growth and unemployment up to four quarters out. Furthermore, stronger sentiment predicts tighter than expected monetary policy and higher future stock returns. Quantile regressions indicate that most of sentimentâ€™s forecasting power arises from signaling downside risks to the economy and stock prices.

The Triumvirate Investment Paradigm : Strategic Asset Allocation in Property, Business, and Country Assets
Khan, Taher
SSRN
The purpose of this document is to outline the triumvirate investment paradigm as a strategic asset allocation blueprint for portfolio construction by categorizing assets into three distinct groups: Property, Business, and Country.This triumvirate taxonomy fits better within the economic context of the Consumption Capital Asset Pricing Model, where we detail the macroeconomy in terms of its constituents of Consumption, Business Investment, and Government Expenditures. This segments into broadly equal market capitalization for these groups as well, with an equal importance for each part of the whole. For asset pricing, this leads to the development of Investment Selection within a particular group based on risk and reward characteristics, and the development of Investment Risk as an integral part of the investment process for Portfolio Construction in determining the diversification benefits across each group, where each group is a distinct correlation cluster.In a zero rate economy, adoption of a triumvirate taxonomy in risk budgeting will lead to a shift of sovereign assets towards more equity-like participation in Infrastructure, which fits under the Country asset class. Equally, it will lead to less investment exposure in Private Equity. For ALM providers, it will involve a shift towards more illiquid assets, such as Property, Infrastructure and Private Credit, by recognition that the long-term liabilities are equally illiquid and well matched.

The classification of term structure shapes in the two-factor Vasicek model -- a total positivity approach
Martin Keller-Ressel
arXiv

We provide a full classification of all attainable term structure shapes in the two-factor Vasicek model of interest rates. In particular, we show that the shapes normal, inverse, humped, dipped and hump-dip are always attainable. In certain parameter regimes up to four additional shapes can be produced. Our results apply to both forward and yield curves and show that the correlation and the difference in mean-reversion speeds of the two factor processes play a key role in determining the scope of attainable shapes. The key mathematical tool is the theory of total positivity, pioneered by Samuel Karlin and others in the 1950ies.

Nektarios Aslanidis,Aurelio F. Bariviera,Óscar G. López
arXiv

This paper shows that Bitcoin is not correlated to a general uncertainty index as measured by the Google Trends data of Castelnuovo and Tran (2017). Instead, Bitcoin is linked to a Google Trends attention measure specific for the cryptocurrency market. First, we find a bidirectional relationship between Google Trends attention and Bitcoin returns up to six days. Second, information flows from Bitcoin volatility to Google Trends attention seem to be larger than information flows in the other direction. These relations hold across different sub-periods and different compositions of the proposed Google Trends Cryptocurrency index.

The relationship between the US broad money supply and US GDP for the time period 2001 to 2019 with that of the corresponding time series for US national property and stock market indices, using an information entropy methodology
Laurence Lacey
arXiv

The primary objective of this paper was to investigate whether the growth in the major US asset indices could be a function of the US broad money supply and/or US GDP, over the time period 2001 to 2019, using an information entropy methodology. The four US asset indices investigated were: (1) US National Property index; (2) Russell 2000 index; (3) S&P 500 index; and (4) NASDAQ index. Notwithstanding the financial crisis of 2007-2008, US real GDP increased exponentially over the period 2001 to 2019, with an average annual growth rate of approximately 2%. However, over this time period, the average annual rate of growth of US GDP was considerably lower than the average annual rate of growth of the US broad money supply (5.7%). The main determinant of the average growth rate for all four US asset indices studied would appear to be the growth rate in the US broad money supply. In addition, the growth rate in the US Russell 2000 stock index and the NASDAQ index would appear to be a function of the combined positive effects of both the growth rate in the US Broad Money Supply and the growth rate of US GDP.

The value of travel speed
Cornelis Dirk van Goeverden
arXiv

Travel speed is an intrinsic feature of transport, and enlarging the speed is considered as beneficial. The benefit of a speed increase is generally assessed as the value of the saved travel time. However, this approach conflicts with the observation that time spent on travelling is rather constant and might not be affected by speed changes. The paper aims to define the benefits of a speed increase and addresses two research questions. First, how will a speed increase in person transport work out, which factors are affected? Second, is the value of time a good proxy for the value of speed? Based on studies on time spending and research on the association between speed and land use, we argue that human wealth could be the main affected factor by speed changes, rather than time or access. Then the value of time is not a good proxy for the value of speed: the benefits of a wealth increase are negatively correlated with prosperity following the law of diminishing marginal utility, while the calculated benefits of saved travel time prove to be positively correlated. The inadequacy of the value of time is explained by some shortcomings with respect to the willingness to pay that is generally used for assessing the value of time: people do not predict correctly the personal benefits that will be gained from a decision, and they neglect the social impacts.

What Drives Bank Peformance?
SSRN
Focusing on some key metrics of bank performance, such as revenues and loan charge-off rates, we estimate the fraction of the observed variation in these metrics that can be attributed to changes in economic conditions. Macroeconomic factors can explain the preponderance of the fluctuations in charge-off rates. By contrast, bank-specific, idiosyncratic factors account for a sizable share of the variation in bank revenues. These results point to importance of bank-specific business models as a driver of performance.

What Drives U.S. Treasury Re-Use?
Infante, Sebastian,Saravay, Zack
SSRN
We study what drives the re-use of U.S. Treasury securities in the financial system. Using confidential supervisory data, we estimate the degree of collateral re-use at the dealer level through their collateral multiplier : the ratio between a dealer's secured funding and their outright holdings. We find that Treasury re-use increases as the supply of available securities decreases, especially when supply declines due to Federal Reserve asset purchases. We also find that non-U.S. dealers' re-use increases when profits from intermediating cash are high, U.S. dealers' re-use increases when demand to source on-the-run Treasuries is high, and both types of dealers' re-use can alleviate safe asset scarcity. Finally, we document a sharp drop in Treasury re-use at the onset of the COVID-19 pandemic, with a subsequent reversal after the Federal Reserve's intervention to support market functioning.

What are the Financial Systemic Implications of Access and Non-Access to Federal Reserve Deposit Accounts for Central Counterparties?
Sklar, Maggie
SSRN
In this working paper, I examine the interconnections between designated derivatives central counterparties (CCPs) with Federal Reserve deposit accounts and non-designated CCPs and the potential financial stability implications. This working paper notes the interconnections between the non-designated and designated derivatives CCPs through their clearing members and the commercial custodial banks they utilize to hold and transfer collateral. The paper then identifies additional potential contagion risks and financial stability risks, including liquidity risk, market risk, concentration risk, and loss of confidence more broadly. Although there are a number of research articles addressing these topics with respect to designated CCPs or OTC derivatives, this working paper includes the perspective looking at U.S. futures CCPs and non-designated CCPs.

Why Are Rational Expectations Violated in Social Interactions?
Mohsen Foroughifar
arXiv

Individuals often interact with each other through observation -- they observe the choices of other people who possess private information. In empirical models of social interactions, it is typically assumed that decision makers are Bayesian rational, therefore their beliefs are identified by the empirical distribution of other agents' decisions. In this study, I assess the validity of the rational expectations assumption in a social interaction experiment. I use a simple and transparent experimental setting to show that decision makers often fail to exhibit rational expectations in social interactions and this behavior is independent of commonly documented errors in statistical reasoning: subjects exhibit a higher level of irrationality in the presence than in the absence of social interaction, even when they receive informationally equivalent signals across the two conditions. A series of treatments aimed at identifying mechanisms suggests that a decision maker is often uncertain about the behavior of other people. So, she might have difficulty in inferring the information contained in others' choices. A general decision-making process is then non-parametrically estimated and sheds light on the identification of three types of error in subjects' behavior.