# Research articles for the 2020-05-06

A Closed-form Model-free Implied Volatility Formula through Delta Sequences
Cui, Zhenyu,Kirkby, Justin,Nguyen, Duy,Taylor, Stephen Michael
SSRN
In this paper, we derive a closed-form explicit model-free formula for the (Black-Scholes) implied volatility. The method is based on the novel use of the Dirac Delta function, corresponding delta sequences, and the change of variable technique. The formula is expressed through either a limit or as an infinite series of elementary functions, and we establish that the proposed formula converges to the true implied volatility value. In numerical experiments, we verify the convergence of the formula, and consider several benchmark cases, for which the data generating processes are respectively the stochastic volatility inspired (SVI) model, the stochastic alpha beta rho (SABR) model. We also establish an explicit formula for the implied volatility expressed directly in terms of respective model parameters, and use the Heston model to illustrate this idea. The delta sequence and change of variable technique that we develop is of independent interest and can be used to solve inverse problems arising in other applications.

A Finance Approach to Climate Stress Testing
Reinders, Henk Jan,Schoenmaker, Dirk,Van Dijk, Mathijs A.
SSRN
There is increasing interest in assessing the impact of climate policies on the value of financial sector assets, and consequently on financial stability. Prior studies either take a â€œblack boxâ€ macro-modelling approach to climate stress testing or focus solely on equity instruments â€" though banksâ€™ exposures predominantly consist of debt. We take a more tractable finance (valuation) approach at the industry-level and use a Merton contingent claims model to assess the impact of a carbon tax shock on the market value of corporate debt and residential mortgages. We calibrate the model using detailed, proprietary exposure data for the Dutch banking sector. For a â‚¬100 to â‚¬200 per tonne carbon tax we find a substantial decline in the market value of banksâ€™ assets equivalent to 4-63% of core capital, depending on policy choices.

A Mean-Field Game Approach to Equilibrium Pricing, Optimal Generation, and Trading in Solar Renewable Energy Certificate Markets
Arvind Shrivats,Dena Firoozi,Sebastian Jaimungal
arXiv

Solar Renewable Energy Certificate (SREC) markets are a market-based system designed to incentivize solar energy generation. A regulatory body imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a certificate for each MWh generated. Regulated firms seek to navigate the market to minimize the cost imposed on them, by modulating their SREC generation and trading activities. As such, the SREC market can be viewed through the lens of a large stochastic game with heterogeneous agents, where agents interact through the market price of the certificates. We study this stochastic game by solving the mean-field game (MFG) limit with sub-populations of heterogeneous agents. Our market participants optimize costs accounting for trading frictions, cost of generation, non-linear non-compliance penalty, and generation uncertainty. Moreover, we endogenize SREC price through market clearing. Using techniques from variational analysis, we characterize firms' optimal controls as the solution of McKean-Vlasov (MV) FBSDEs and determine the equilibrium SREC price. We establish the existence and uniqueness of a solution to this MV-FBSDE, and further prove that the MFG strategies have the $\epsilon$-Nash property for the finite player game. Finally, we develop a numerical scheme for solving the MV-FBSDEs and conclude by demonstrating how firms behave in equilibrium using simulated examples.

A closed formula for illiquid corporate bonds and an application to the European market
Roberto Baviera,Aldo Nassigh,Emanuele Nastasi
arXiv

We propose an option approach for pricing bond illiquidity that is reminiscent of the celebrated work of Longstaff (1995) on the non-marketability of some non-dividend-paying shares in IPOs. This approach describes a quite common situation in the fixed income market: it is rather usual to find issuers that, besides liquid benchmark bonds, issue some other bonds that either are placed to a small number of investors in private placements or have a limited issue size.

We model interest rate and credit risks via a convenient reduced-form approach. We deduce a simple closed formula for illiquid corporate coupon bond prices when liquid bonds with similar characteristics (e.g. maturity) are present in the market for the same issuer. The key model parameter is the time-to-liquidate a position, i.e. the time that an experienced bond trader takes to liquidate a given position on a corporate coupon bond. We show that illiquid bonds present an additional liquidity spread that depends on the time-to-liquidate aside from bond volatility.

We provide a detailed application for two issuers in the European market.

A generative adversarial network approach to calibration of local stochastic volatility models
Christa Cuchiero,Wahid Khosrawi,Josef Teichmann
arXiv

We propose a fully data driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows to calculate model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analyis on many samples of implied volatility smiles, showing the accuracy and stability of the method.

Another Use for Active Share â€" Understanding Portfolio Exposures
Elimelakh, Simon,Gillman, Barry,Warren, Geoff
SSRN
We show how active share can be decomposed into segment and stock-specific exposures to create an â€˜active share risk profileâ€™. The method is demonstrated for global equity portfolios by attributing active share into contributions from country, industry sector, stock-specific and non-equity positions within portfolios. Active share risk profile can be used to identify the underlying sources of benchmark-relative risk within a portfolio both for a point in time and over time, and to compare the exposures in a portfolio relative to competitors. The method is straightforward to implement and may form a useful part of the toolkit for understanding benchmark-relative risk exposures within actively managed portfolios.

Are the CDCâ€™s Corona Virus Statistics Fraudulent? An Accounting and Legal Analysis
McGee, Robert W.
SSRN
This paper presents an overview of the Corona virus situation and examines the literature that seems to suggest that some, or perhaps much of the reporting of Corona virus deaths is actually the result of deliberate misclassification. The accounting and legal literature is also examined to determine whether the misclassifications amount to fraud.

Bank Intermediation and Economic Growth in Nigeria
Amalaha, Obinna
SSRN
The study is carried out to examine the macroeconomic determinants of bank intermediation and to explore its contribution to the growth of the Nigerian economy, using secondarily sourced time series data spanning from 1970 to 2004. From the available literature reviewed, it was explicit that the results obtained from cross-country studies are not able to address this issue satisfactorily and highlight the importance of country-specific studies. The time series study based on the Distributed Lag Method of Cointegration (DL-ECM) and Regression Analysis finds firm and colossal evidence suggesting that some form of financial determinants like ratio of Liquid liabilities to GDP, Domestic Credit to the Private Sector relative to GDP, Branch expansion of deposit money banks (DMBs) and Interest Rate Spread as well as macroeconomic variables such as inflation, investment and trade openness has indeed contributed to bank intermediation and economic growth. This study drew the conclusion that better bank intermediation has a distinct significant positive impact on intermediation financial development leads to economic growth. in the context of the empirical findings above, the researcher advanced several recommendations for policy.

Bayesian Estimation of Long-Run Risk Models Using Sequential Monte Carlo
Fulop, Andras,Heng, Jeremy,Li, Junye,Liu, Hening
SSRN
We propose a likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to efficiently estimate long-run risk models in which the conditional variance of consumption growth follows either an autoregressive (AR) process or an autoregressive gamma (ARG) process. We use the U.S. quarterly consumption and asset returns data from the postwar period to implement estimation. Our findings are: (1) informative priors on the preference parameters can help to improve model performance; (2) expected consumption growth has a very persistent component, whereas consumption volatility is less persistent; (3) while the ARG-based model performs better than the AR-based one statistically, the latter could fit asset returns better; and (4) the solution method matters more for estimation in the AR-based model than in the ARG-based model.

Beyond the Doomsday Economics of
Auer, Raphael
SSRN
This paper discusses the economics of how Bitcoin achieves data immutability, and thus payment finality, via costly computations, i.e., proof-of-work." Further, it explores what the future might hold for cryptocurrencies modelled on this type of consensus algorithm. The conclusions are, first, that Bitcoin counterfeiting via "double-spending" attacks is inherently profitable, making payment finality based on proof-of-work extremely expensive. Second, the transaction market cannot generate an adequate level of "mining" income via fees as users free-ride on the fees of other transactions in a block and in the subsequent blockchain. Instead, newly minted bitcoins, known as block rewards, have made up the bulk of mining income to date. Looking ahead, these two limitations imply that liquidity is set to fall dramatically as these block rewards are phased out. Simple calculations suggest that once block rewards are zero, it could take months before a Bitcoin payment is final, unless new technologies are deployed to speed up payment finality. Second-layer solutions such as the Lightning Network might help, but the only fundamental remedy would be to depart from proof-of-work, which would probably require some form of social coordination or institutionalisation.

Competition or Contagion? Evidence from Cryptocurrency Peers
Schwenkler, Gustavo,Zheng, Hannan
SSRN
Are bad news for one asset good or bad news for peer assets? The former is a competition effect while the latter constitutes a contagion channel. Disentangling contagion and competition for stocks or bonds is difficult because, often, peer firms share many different types of economic linkages with each other. We answer this question by analyzing cryptocurrency markets that are free of contractual linkages, and consider crypto assets to be peers when they are jointly mentioned in online news. Our results show that the competition effect outweighs the contagion effect when large price or uncertainty shocks occur. Our findings highlight the competition channel as a key mechanism driving the co-movement of peer assets in financial markets.

Deep xVA solver -- A neural network based counterparty credit risk management framework
Alessandro Gnoatto,Athena Picarelli,Christoph Reisinger
arXiv

In this paper, we present a novel computational framework for portfolio-wide risk management problems where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.

Determining Affecting Macroeconomic Indicators on Interest Rates in Emerging Countries: A Comparative Examination upon China, Brazil, and Turkey with Multivariate Adaptive Regression Splines (MARS)
Kartal, Mustafa Tevfik
SSRN
China, Brazil, and Turkey are important emerging countries and have different interest rate trends. China and Brazil enjoyed the lower interest rate of approximately 1.5% and 6.8% respectively whereas Turkey was faced with increasing interest rate problems reaching 23.35% as of 2018 end. The study aims to analyze and define macroeconomic determinants of interest rates. 11 independent variables, yearly data between 2002 and 2018 were examined with the MARS method. The study determines that growth and reserves have effects in China; credits and net export have effects in Brazil whereas inflation and money supply are influential in Turkey on interest rates.

Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk
Derek Singh,Shuzhong Zhang
arXiv

This paper investigates calculations of robust XVA, in particular, credit valuation adjustment (CVA) and funding valuation adjustment (FVA) for over-the-counter derivatives under distributional uncertainty using Wasserstein distance as the ambiguity measure. Wrong way counterparty credit risk and funding risk can be characterized (and indeed quantified) via the robust XVA formulations. The simpler dual formulations are derived using recent infinite dimensional Lagrangian duality results. Next, some computational experiments are conducted to measure the additional XVA charges due to distributional uncertainty under a variety of portfolio and market configurations. Finally some suggestions for future work are discussed.

Do Entry Barriers to the Public Company Audit Market Deter Low Quality Audit Firms?
Kitto, Andrew,Lamoreaux, Phillip T.,Williams, Devin
SSRN
Many legal jurisdictions, including the U.S. and U.K., have passed regulations to address the potential negative impacts of a lack of competition and high concentration in public company audit markets. One consequence of increased regulations, desired or not, is that they have presumably increased barriers to enter the public company audit market. An alternative view is that high barriers prevent the entry of low-quality entrants (von Weizsacker, 1980; Grossman and Horn, 1988). This study investigates the supply of, and demand for, first time public company audit firms in the U.S., firms that presumably overcame these barriers to enter the market. We document that since 2004, 275 unique audit firms have entered the U.S. public company audit market. We find evidence that these first time auditors provide lower quality audits as measured by a higher likelihood of client restatements, PCAOB-identified audit deficiencies, PCAOB enforcement actions, and lower auditor effort as measured by audit engagement hours. We also find evidence that these firms receive lower fees and that clients are significantly more likely to subsequently switch away from first time audit firms. Collectively, our results suggest that existing barriers to entry to the public company audit market do not prevent the entry of low-quality auditors, and clients do not fully infer quality based on first timersâ€™ pre-entry quality signals.

Do Firms Adapt to Climate Change? Evidence from Establishment-Level Data
Li, Frank Weikai,Lin, Yupeng,JIN, Zuben,Zhang, Zilong
SSRN
This paper examines firmsâ€™ adaptation to long-term changes in climatic conditions. Using detailed information of establishments owned by U.S. public firms from 1990 to 2012, we show that higher abnormal temperatures over the previous five years in a county lead to a significant reduction in local employment and the number of establishments. Further tests suggest that the decline in employment and establishments is largely due to a decline in local consumer demand rather than lower labor productivity. We also find that firms more likely take adaptive actions when their managers are more likely to believe in, or are concerned about, climate change. Overall, we provide large-sample evidence on firm adaptation to climate change.

Do Foreign Institutional Investors Improve Price Efficiency?
Kacperczyk, Marcin T.,Sundaresan, Savitar,Wang, Tianyu
SSRN
We study the impact of foreign institutional investors on price efficiency with firm-level international data. Using MSCI index inclusion and the U.S. Jobs and Growth Tax Relief Reconciliation Act as exogenous shocks to foreign ownership, we show that greater foreign ownership increases stock price informativeness, especially in developed economies. This increase arises from new information that foreign investors bring in, and displacement of less informed domestic retail investors. Finally, we show that foreign ownership, particularly from active investors, increases market liquidity, reduces firms' cost of equity, and increases firms' real investment growth.

Does the Right to Choose Matter for Defined Contribution Plans?
Wong, Kin Ming,Tsang, Kwok Ping
SSRN
We find that sensitivity of fund flows and fund performance are both related to participants' right to choose their investments in defined contribution plans. Under the Mandatory Provident Fund system of Hong Kong, both employers and employees are required to contribute to a retirement account. Originally, employees' investment choices were restricted to a subset of funds chosen by their employers. The system was later modified so that employees are allowed to invest in any fund within the system. We present evidence that flows of fund have become more sensitive to past fund performance after this policy change, and that average fund performance in the system has also improved. Based on the improvement in fund performance, we estimate the accumulated cost of the lack of choice to be around 10% of the current total asset value of the system.

Dollar and Exports
Bruno, Valentina,Shin, Hyun Song
SSRN
The strength of the US dollar has attributes of a barometer of dollar credit conditions, whereby a stronger dollar is associated with tighter dollar credit conditions. Using finely disaggregated data on export shipments, we examine how dollar strength impacts exports through the lens of dollar financing availability. We find that exporters who are reliant on dollar-funded bank credit suffer a decline in exports due to increased funding costs. We argue that the US dollar is a global financial factor with real effects on the economy.

ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction
Tian Guo,Nicolas Jamet,Valentin Betrix,Louis-Alexandre Piquet,Emmanuel Hauptmann
arXiv

Incorporating environmental, social, and governance (ESG) considerations into systematic investments has drawn numerous attention recently. In this paper, we focus on the ESG events in financial news flow and exploring the predictive power of ESG related financial news on stock volatility. In particular, we develop a pipeline of ESG news extraction, news representations, and Bayesian inference of deep learning models. Experimental evaluation on real data and different markets demonstrates the superior predicting performance as well as the relation of high volatility prediction to stocks with potential high risk and low return. It also shows the prospect of the proposed pipeline as a flexible predicting framework for various textual data and target variables.

FinTech, Lending and Payment Innovation: A Review
Agarwal, Sumit,Zhang, Jian
SSRN
The global landscape has seen the advent of new technology in offering innovative financial services and products and reshaping the financial sector, namely FinTech. In this review, we discuss the literature on recent FinTech development and its interaction with both banks and consumers. We synthesize the insights it provides into two domains: credit supply and payment and clearing services. The rise of FinTech has introduced digital transformation of the â€œbricksâ€andâ€mortarâ€ banking model and dramatically changed the way financial services are delivered. We also present several future questions and directions that are worthy of investigation for researchers and policyâ€makers.

Financial Hedging and Corporate Cash Policy
Sun, Wenyi,Yin, Chao,Zeng, Yeqin
SSRN
We study the implications of financial hedging for corporate cash policy. Using a web crawler program to collect data on the use of financial derivatives, we find that firms with financial hedging programs have smaller cash reserves but a higher value of cash than firms without hedging contracts in place. Our empirical results are robust when controlling for potential endogeneity issues, corporate governance, cash regimes, and alternative measures of cash holdings. Further, we find that the positive effect of financial hedging on the value of cash is more pronounced for financially constrained firms and firms with higher asymmetric information. Collectively, our paper highlights the importance of corporate cash policy as a channel through which corporate risk management increases firm value.

Financial Reforms and The Differential Impact of Foreign versus Domestic Banking Relationships on Firm Value
Yu, Hai-Chin,Lee, Cheng-Few,Sopranzetti, Ben J.
SSRN
This study documents a substantial difference in impact on an emerging market firmâ€™s value due to its use of foreign bank debt relative to domestic bank debt. It finds a positive association between the use of collateral by foreign banks and firm value, however, finds no such corresponding association for the use of collateral by domestic banks. The results suggest that as an emerging marketâ€™s banking system matures and becomes more sophisticated, the differences between the information contained in local versus foreign bank lending diminishes; this diminishment erodes the differential impact on firm value of foreign versus local bank lending.

Financial Reporting Consequences of CEOsâ€™ Early-life Exposure to Disasters and Violent Crime
Golden, Joanna,Kohlbeck, Mark J.
SSRN
Understanding the behavior of chief executive officers (CEOs) enables investors, regulators, and others to better appreciate the corporate decisions a CEO makes. Among the many aspects that determine CEO behavior are early-life experiences, we examine whether a CEOâ€™s exposure to two important events â€" fatal natural disasters and violent crime â€" during the individualâ€™s formative years is associated with the firmâ€™s financial reporting quality. We provide evidence of a non-monotonic association between early-life exposures to such events and financial reporting outcomes. When a CEO has moderate levels of early-life exposure to deaths associated with natural disasters or to violent crimes, financial reporting quality is lower, consistent with the CEO being over-confident in dealing with risk. However, as exposure increases to extreme levels, the CEO becomes more aware of the risk effect and, as a result, more careful about decisions that could elevate the firmâ€™s risk; thus, we find evidence of higher financial reporting quality.

Housing Price Dynamics within the U.S.: Evidence from Zip Codes with Different Demographics
SSRN
We study time-series fluctuations in the United States housing market from 2010 to 2019 using the Gordon growth model. We apply a vector autoregressive model (VAR) with fixed coefficients to measure expectations at each point in time. Our results show that, using zip code level data, we are able to explain the broad movements in housing volatility with higher prediction power compared to previous studies. Using variance decomposition analysis, we find that the housing premium is the main driver of housing market fluctuations. Motivated by previous studies and using impulse response functions, we show how different components of the housing market respond over time to a shock in the interest rate in regions with different levels of income or demographics. Our findings suggest that the impact of monetary policy is bigger in the U.S. housing market when households have less income, more female members, more African Americans, or less well-educated members; a combination of these demographics and lower income in households results in a bigger impact of monetary policy in housing market, due to the necessity of housing for these families.

Independent Women: Investing in British Railways, 1870-1922
Acheson, Graeme,Campbell, Gareth,Gallagher, Aine,Turner, John D.
SSRN
The early twentieth century saw the British capital market reach a state of maturity before any of its global counterparts. This coincided with more women participating directly in the stock market. In this paper, we analyse whether these female shareholders chose to invest independently of men. Using a novel dataset of almost 500,000 shareholders in some of the largest British railways, we find that women were much more likely to be solo shareholders than men. There is also evidence that they prioritised their independence above other considerations such as where they invested or how diversified they could be.

Influence of Socio-economic Factors in Deciding the Investment Pattern of Individual Households
Vijayalakshmi, P,R, Sathishkumar
SSRN
The authors conducted a questionnaire survey among in Tamil Nadu households by selecting highly populated household areas to observe the investment pattern and factors, which determine and influence the investors to take investment decisions. By applying the chi-square test, the authors prove that the investment pattern of the households in Tamil Nadu is unique and investors have certain references to consider while making investment decisions.

Intermediation Frictions in Equity Markets
Seegmiller, Bryan
SSRN
Stocks with similar characteristics but different levels of ownership by financial institutions have returns and risk premia that comove very differently with shocks to the risk bearing capacity of financial intermediaries. After accounting for observable stock characteristics, excess returns on more intermediated stocks have higher betas on contemporaneous shocks to intermediary willingness to take risk and are more predictable by state variables that proxy for intermediary health. The empirical evidence suggests that asset pricing models featuring financial intermediaries as marginal investors and frictions that induce changes in intermediary risk bearing capacity are useful in explaining price movements even in asset classes with comparatively low barriers to household participation.

Keiretsu Style Main Bank Relationships, R&D Investment, Leverage, and Firm Value: Quantile Regression Approach
Yu, Hai-Chin,Hsieh, Der-Tzon, Chen, Chih-Sean
SSRN
Using quantile regression, our results provide explanations for the inconsistent findings that use conventional OLS regression in the extant literature. While the direct effects of RD while firmsâ€™ advantages with low RD whereas it is decreasing in high Q firms. Main banks add value for low to median Q firms, while the value is destroyed for high Q firms. Meanwhile, we find the interacted effect of the main bank and R&D investment which increases with firm value, only appears in medium quantiles, instead of low or high quantiles. The results of this work provide relevant implications for policymakers. Finally, we document that industry quantile effect is larger than the industry effect itself, given that most of the firms in higher quantiles gain from industry effects while lower quantile firms suffer negative effects. We also find the results of OLS are seriously influenced by outliers. In stark contrast, quantile regression results are impervious to either inclusion or exclusion outliers.

Lattice-Based Hedging Schemes Under GARCH Models
SSRN
This paper proposes an efficient way to implement quadratic hedging schemes for European options when the asset return process follows an asymmetric non-affine GARCH model driven by Gaussian innovations. More specifically, using a lattice approximation for the underlying, we construct locally risk-minimizing (LRM) hedge ratios under both physical and risk-neutral measures, as well as standard delta strategies. We investigate the convergence of option prices and hedges resulting from the LRM strategies relative to the number of intra-daily periods used in the lattice. Several numerical experiments are conducted to assess the sensitivity of the hedge ratios to the equity risk premium and leverage effect parameters, and to compare their performance by computing the corresponding one-period and terminal hedging errors. Our results suggest that the LRM scheme under the physical measure consistently outperforms competing hedging strategies.

Levered and Inverse ETPs: Blessing or Curse?
Pessina, Colby,Whaley, Robert E.
SSRN
Levered and inverse ETPs are designed to provide geared long and short exposures to the daily returns of different benchmark indexes. The benchmarks can be any reference index. The popular ones are on stocks, bonds, commodities and volatility. The problem with these products is that they are not generally well-understood. They are neither suitable buy-and-hold investments nor effective hedging tools. They are unstable and exist only as a mechanism for placing short-term directional bets. But, if that is their sole purpose, how is society better served? Traditionally, securities markets have existed as a means of capital formation and price discovery. The objective of this paper is to explain the mechanics of levered and inverse ETP returns, simulate their expected return performance based on the most popular benchmarks, and document the actual performance of 35 popular products. Ultimately, the decision about whether to trade these products rests with the investor. But, at a more basic level, why do the products exist?

Margin-Trading Volatility and Stock Price Crash Risk
Lv, Dayong,Wu, Wenfeng
SSRN
Previous studies rarely discuss the effect of margin trading on future stock price crash risk, though margin trading is often blamed for destabilizing stock market. We propose three possible mechanisms through which margin trading may affect crash risk. Our empirical results show that neither margin-buying activity nor margin debt are associated with future crash risk, rejecting mechanisms of both â€œliquidity provisionâ€ and â€œfire salesâ€. In contrasts, stocks with more margin-trading volatility are predicted to have more crash risk, supporting the view of â€œarbitrage risk mechanismâ€. Furthermore, we find that higher margin-trading volatility results in higher overpricing and less information content.

Market Reactions to Quest for Decentralization and Independence: Evidence from Catalonia
Galasso, Vincenzo
SSRN
Regions seeking more autonomy aim at making less (or no) fiscal transfers to central governments and at reaching more (or total) control on regional spending. However, decentralization may lead to joint central-regional responsibility that increases regulatory uncertainty regarding the bureaucratic and fiscal burdens on firms. Moreover, quests for independence create political uncertainty. To evaluate economic costs and benefits for firms from decentralization or independence, we analyze the Catalan-Spanish negotiation leading to the 2006 Catalan Statute and the more recent quest for independence. We use an event approach methodology to estimate the immediate stock market reaction to new events. Our results suggest that the stock market had a conservative reaction both to more decentralization and to independence. The approval of the Catalan Statute was associated with negative returns for Catalan firms, particularly in the tradable sector. These firms later benefitted from the partial reversal imposed by the Spanish Constitutional Court ruling. The large increase in the political uncertainty that emerged at the referendum day had a strong negative effect on all Catalan firms and on Spanish firms in the tradable sector. This uncertainty was partially reduced, when the Spanish Senate rejected the declaration of Catalan independence. Markets reacted positively, with Catalan, but also Spanish, firms in all sectors posting large gains that largely compensated the previous losses.

Modality for Scenario Analysis and Maximum Likelihood Allocation
Takaaki Koike,Marius Hofert
arXiv

We analyze dependence, tail behavior and multimodality of the conditional distribution of a loss random vector given that the aggregate loss equals an exogenously provided capital. This conditional distribution is a building block for calculating risk allocations such as the Euler capital allocation of Value-at-Risk. A level set of this conditional distribution can be interpreted as a set of severe and plausible stress scenarios the given capital is supposed to cover. We show that various distributional properties of this conditional distribution are inherited from those of the underlying joint loss distribution. Among these properties, we find that multimodality of the conditional distribution is an important feature related to the number of risky scenarios likely to occur in a stressed situation. Moreover, Euler allocation becomes less sound under multimodality than under unimodality. To overcome this issue, we propose a novel risk allocation called the maximum likelihood allocation (MLA), defined as the mode of the conditional distribution given the total capital. The process of estimating MLA turns out to be beneficial for detecting multimodality, evaluating the soundness of risk allocations, and constructing more flexible risk allocations based on multiple risky scenarios. Properties of the conditional distribution and MLA are demonstrated in numerical experiments. In particular, we observe that negative dependence among losses typically leads to multimodality, and thus to multiple risky scenarios and less sound risk allocations.

Multidimensional Corporate Governance
Chen, Kevin D.,Guay, Wayne R.,Lambert, Richard
SSRN
Corporate governance is a multidimensional construct, with many interactive mechanisms that must be simultaneously managed for efficiency. We develop a model where multiple governance mechanisms (board independence, board expertise, and CEO equity incentives) are endogenously selected to encourage information sharing by the CEO, which in turn optimizes the boardâ€™s ability to monitor and advise the CEOâ€™s project selection. In equilibrium, we find that boards with greater independence also have higher expertise; board expertise and equity incentives are substitutes (complements) when the board has high (low) independence; and equity incentives may be positively or negatively related to board independence, depending on the benefit of expert advice. The analysis offers new predictions about the correlations between board independence, board expertise, and incentive compensation.

Neural Networks and Value at Risk
Alexander Arimond,Damian Borth,Andreas Hoepner,Michael Klawunn,Stefan Weisheit
arXiv

Utilizing a generative regime switching framework, we perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation. Using equity markets and long term bonds as test assets in the global, US, Euro area and UK setting over an up to 1,250 weeks sample horizon ending in August 2018, we investigate neural networks along three design steps relating (i) to the initialization of the neural network, (ii) its incentive function according to which it has been trained and (iii) the amount of data we feed. First, we compare neural networks with random seeding with networks that are initialized via estimations from the best-established model (i.e. the Hidden Markov). We find latter to outperform in terms of the frequency of VaR breaches (i.e. the realized return falling short of the estimated VaR threshold). Second, we balance the incentive structure of the loss function of our networks by adding a second objective to the training instructions so that the neural networks optimize for accuracy while also aiming to stay in empirically realistic regime distributions (i.e. bull vs. bear market frequencies). In particular this design feature enables the balanced incentive recurrent neural network (RNN) to outperform the single incentive RNN as well as any other neural network or established approach by statistically and economically significant levels. Third, we half our training data set of 2,000 days. We find our networks when fed with substantially less data (i.e. 1,000 days) to perform significantly worse which highlights a crucial weakness of neural networks in their dependence on very large data sets ...

On the Effectiveness of Case Management
Draheim, Matthias,Schanbacher, Peter,Seiberlich, Ruben R.
SSRN
Case managers provide individual and comprehensive support to employees who have become incapable of working. Using data from a large insurance company we find that overall 43.9% of the people in our sample could be reintegrated. Controlling for personal characteristics, we analyze the effectiveness of case management by modelling the probability of reintegrating people being incapable of working into the labor market. We find that the estimated probability of reintegration is about 20% or eight percentage points higher if people participate in case management. Using parametric and semiparametric decomposition methods, we control for selection biases and analyze how much of the difference in the reintegration rate between people who participate in case management and those who do not, is due to differences in characteristics and how much is due to case management itself. Our results show that no more than 15% are due to differences in characteristics and at least 85% can be attributed to case management itself.

On the Evolution of Cryptocurrency Market Efficiency
Akihiko Noda
arXiv

This study examines whether the efficiency of cryptocurrency markets (Bitcoin and Ethereum) evolve over time based on Lo's (2004) adaptive market hypothesis (AMH). In particular, we measure the degree of market efficiency using a generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods. The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) Bitcoin's market efficiency level is higher than that of Ethereum over most periods, and (3) a market with high market liquidity has been evolving. We conclude that the results support the AMH for the most established cryptocurrency market.

Political Connections, Tax Benefits and Firm Performance: Evidence from China
Wu, Wenfeng,Wu, Chongfeng,Zhou, Chunyang,Wu, Jun
SSRN
This paper investigates the different effects of political connections on the firm performance of state-owned enterprises (SOEs) and privately owned enterprises. Using data on Chinese listed firms from 1999 to 2007, we find that private firms with politically connected managers outperform those without such managers, whereas local SOEs with connected managers underperform those without such managers. Moreover, we find that private firms with politically connected managers enjoy tax benefits, whereas local SOEs with politically connected managers are prone to more severe over-investment problems. Our study reconciles the mixed findings of previous studies on the effect of political connections on firm performance.

Portfolio Pumping and Fund Performance Ranking: A Performance-Based Compensation Contract Perspective
Li, Xiangwen,Wu, Wenfeng
SSRN
We collect compensation policy data from 60 Chinese mutual fund companies, which covers 88% of assets under management by all active stock and stock-oriented hybrid mutual funds in China. Using the collected data, we investigate the portfolio pumping from a performance-based perspective. We find that portfolio pumping is stronger for funds ranking around critical points of performance distribution (i.e. top one-tenth, one-fourth, one third and one half cutoffs). Moreover, this finding is mainly driven by funds from companies setting these critical points to grade managersâ€™ bonus levels. Our findings provide evidence of portfolio pumping motivated by performance ranking, instead of flow-performance relationship that prior studies documented.

Post Tax Reform and Corporate Effective Tax Rate: Evidence from Tunisia
SSRN
This study examines the impact of the tax reform on corporate effective tax rate (ETR) and firm-specifics in Tunisia for the post tax reform period (after the fiscal year 2014).The corporate effective tax rate is a component by major firm-specific characteristics, especially firm size, capital structure (leverage), inventory intensity, capital intensityâ€¦The ETR provides information about the tax burdens and can be used as a political instrument to boost the economic reliance. The post tax reform period reflects the impact of lower corporate tax rate on the firm characteristics.The sample consists of 112 firm-year observations from 16 listed companies in Tunis Stock Exchange (known Bourse de Tunis- BVMT) covering seven years from 2010 to 2016.Our result indicates that the tax reform had a significant impact only on the inventory variable but no significant results on the others firm characteristics for the post-tax reform period.These findings urge the Tunisianâ€™s tax authority to reformulate the corporate tax system.

Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
Bart Cockx,Michael Lechner,Joost Bollens
arXiv

Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that 'black-box' rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain.

Private Markets, Public Options, and the Payment System
Conti-Brown, Peter,Wishnick, David A.
SSRN
The speed at which money moves between people and businesses in the United States lags well behind international standards. Far from being a mere inconvenience, slow payment speeds create needless financial uncertainty, lead to inefficiencies across the economy, and drive demand for high-cost credit products like payday loans and overdraft protection. To speed up the payment system, the Federal Reserve has announced â€œFedNowâ€ a platform due in 2023 that would operate as a public real-time payment rail, competing with a privately-run platform in the interbank payment market. This Article analyzes the problem of slow payments and the Fedâ€™s many roles in addressing it. Against the Fedâ€™s critics, we argue that the Fedâ€™s operational involvement in the payment system holds the capacity to achieve three objectives at the heart of payment policy in the United States: to catalyze innovation, enhance access to developing payment networks, and shore up financial stability. Fed participation in the payment system and public-private competition are not troublesome bugs or unfortunate byproducts of political compromise. Rather, they represent valuable features of the Fedâ€™s hybrid, public-private system and are likely to drive faster payment development in the United States.We also argue for an expanded use of Fed tools to achieve payment objectives well beyond FedNow, including by using the Fedâ€™s unique status as operator, market participant, regulator, and supervisor of the payment system and the private financial institutions that participate in it. These are different roles that can be harmonized for the same public policy outcome.

Product Market Competition and Analyst Coverage Decisions
Hsu, Charles,Li, Xi,Ma, Zhiming,Phillips, Gordon M.
SSRN
We analyze whether product market competition is an important factor in analyst coverage decisions and whether analysts benefit from covering product market competitors. We find that analysts are more likely to cover a firm When this firm competes with and offers more similar products to the firms already covered by the analyst. We also find that analysts who cover product market competitors are more likely to obtain star status and issue more informative recommendations. Collectively, these results are consistent with the importance of industry and product market knowledge obtained through covering product market competitors to analysts.

Recursive Preferences, the Value of Life, and Household Finance
Bommier, Antoine,Harenberg, Daniel,Le Grand, Francois,Oâ€™Dea, Cormac
SSRN
We analyze lifecycle saving strategies using a recursive utility model calibrated to match empirical estimates for the value of a statistical life. We show that, with a positive value of life, risk aversion reduces savings and annuity purchase. Risk averse agents are willing to make an early death a not-so-adverse outcome by enjoying greater consumption when young and bequeathing wealth in case of death. We also ï¬nd that greater risk aversion lowers stock market participation. We show that this model can rationalize low annuity demand while also matching empirically documented levels of wealth and private investments in stocks. Our ï¬ndings stand in contrast to studies that implicitly assume a negative value of life.

SABR smiles for RFR caplets
Sander Willems
arXiv

We present a natural extension of the SABR model to price both backward and forward-looking RFR caplets in a post-Libor world. Forward-looking RFR caplets can be priced using the market standard approximations of Hagan et al. (2002). We provide closed-form effective SABR parameters for pricing backward-looking RFR caplets. These results are useful for smile interpolation and for analyzing backward and forward-looking smiles in normalized units.

Social Media, Bank Relationships and Firm Value
Chao, Chia-Hui,Yu, Hai-Chin
SSRN
This study examines how a firmâ€™s usage of social media and banking relationships influence its value. Using a sample of 6,636 year-firm observations from 2008 to 2015, the results show that social media (Facebook, Google+, and LinkedIn) positively influence firm value, whereas bank relationships affect firm value differently: the high number of banks a firm borrows from reducing value, whereas the high bank debt a firm using creates value. The impacts of YouTube and Twitter on firm value are insignificant. Although social media has a similar function as banks in mitigating the information asymmetry between firms and outsiders, the information types vary. Banks create more soft and private information, while social media deliver more public and hard information. The accuracy of information is more than the quantity; hence, whether more information sharing via social media creates value is uncertain. We also find the substitution and complementary effects between various types of social media and banking relationships on firm value. Our results remain robust after conducting a difference-in-differences ( DID) analysis using the exogenous shock of the Facebook IPO in 2012.

Structural Estimation of Decision Making under Natural Hazard Risk
Turner, Dylan,Landry, Craig E.
SSRN
The use of structural models for decision-making under risk and uncertainty in applied economics is scarce compared to reduced form approaches. This is unfortunate, as structural models have clear connections to theory and permit direct tests of hypotheses and exploration of potential causal mechanisms. In this paper, we derive and estimate a structural model of decision making in the presence of natural hazard risk. Utilizing a unique data set that includes information on risk preferences, subjective likelihood of hurricane strike, and expectations of damage, we estimate several variants of a subjective expected utility model for coastal householdsâ€™ decisions to purchase flood insurance. We benchmark our modelâ€™s out of sample accuracy by comparing it against both reduced-form and machine learning estimates. Overall, we find our structural models perform about as well as a reduced-form probit model in predicting out of sample behavior, but offer increased external validity and insight into theory and mechanisms.

The Benefits of Going Public: Evidence of Increased Public Recognition
SSRN
We empirically examine the benefits of going public: increased public recognition. We develop a novel measure of public recognition by using page views of the companyâ€™s website that can calculate pre- and post-initial public offering (IPO) periods and capture a wider range of recognition than the existing measures. We find that 89% of firms that go public experience increased page views; the remaining 11%, however, experience decreased page views. When a firm is less known before the IPO and listed by reputable underwriters, the increased page views is larger.

The Pricing of Quanto Options: An empirical copula approach
Rafael Felipe Carmargo Prudencio,Christian D. Jäkel
arXiv

The quanto option is a cross-currency derivative in which the pay-off is given in foreign currency and then converted to domestic currency, through a constant exchange rate, used for the conversion and determined at contract inception. Hence, the dependence relation between the option underlying asset price and the exchange rate plays an important role in quanto option pricing.

In this work, we suggest to use empirical copulas to price quanto options. Numerical illustrations show that the flexibility provided by this approach, concerning the dependence relation of the two underlying stochastic processes, results in non-negligible pricing differences when contrasted to other models.

The socio-economic determinants of the coronavirus disease (COVID-19) pandemic
Viktor Stojkoski,Zoran Utkovski,Petar Jolakoski,Dragan Tevdovski,Ljupco Kocarev
arXiv

The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 29 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome. We show that the true mathematical model of the coronavirus outcome is constituted only of few determinants, but the extent to which each determinant is able to provide an adequate explanation varies between countries due to their heterogeneous socioeconomic structures. To understand the relationship between the socio-economic determinants in the specification of the true model we develop the coronavirus determinants Jointness space. In this space, the potential determinants are linked with each other by their ability to jointly explain the coronavirus outcome. As such the Jointness space can be efficiently implemented for developing socio-economic policies aimed at prevention of future epidemic crises.

Uncovering the hierarchical structure of the international FOREX market by using similarity metric between the fluctuation distributions of currencies
Abhijit Chakraborty,Soumya Easwaran,Sitabhra Sinha
arXiv

The decentralized international market of currency trading is a prototypical complex system having a highly heterogeneous composition. To understand the hierarchical structure relating the price movement of different currencies in the market, we have focused on quantifying the degree of similarity between the distributions of exchange rate fluctuations. For this purpose we use a metric constructed using the Jensen-Shannon divergence between the normalized logarithmic return distributions of the different currencies. This provides a novel method for revealing associations between currencies in terms of the statistical nature of their rate fluctuations, which is distinct from the conventional correlation-based methods. The resulting clusters are consistent with the nature of the underlying economies but also show striking divergences during periods of major international crises.

What Does Japanese Corporate Governance Reform Mean?
Omura, Hiroyasu
SSRN
In Japan, since 2013, Japanese corporate governance reform has been developed by Japanese Government initiatives. This paper provides a theoretical framework for understanding what Japanese corporate governance reform means for Japanese companies by an application of agency theory. Corporate governance is a structure which determines how shareholders delegate corporate control to managers and monitor managersâ€™ business executions. Debates on corporate governance finally end in how we should resolve agency problems which decide what is the best measures to maximize corporate value by reducing agency costs deriving from an agency relationship between shareholders and managers. New Japanese reform further develops previously introduced measures for improving corporate governance in order to reduce agency costs. Now some changes in corporate financial results are recognized in stewardship reports by institutional investors. This reform is facilitated by an introduction of Japanese Stewardship Code and leveraged by a collaboration between Corporate Governance Code and Japanese Stewardship Code to seek a long-term corporate growth. Key to success to a corporate governance reform is a synchronized collaboration between these two codes, which puts burdens of execution on institutional investors who take stewardship activities effectuating a governance reform. Agency theory focused on principal costs provides us with interpretation of the reform and implication of possible changes in corporate governance and their solutions. One possible hint for solutions is disclosure of stewardship activities including engagement activities by institutional investors.

Your Gender Identity Is Who You Are: Female CEOs and Corporate Debt Structure
Zhu, Qi,Huang, Yuxuan,Yan, Cheng,Zeng, Yeqin
SSRN
We examine the implications of CEO gender for corporate debt structure. After controlling for endogeneity, firms with female CEOs issue less debt than firms with male CEOs. Although both risk aversion and overconfidence may serve as the channel of our main finding, we show that female CEOs being more risk averse is the underlying mechanism. Further, we find that the CEO gender effect is more pronounced for firms with younger CEOs, higher litigation risk, and more market competition. We also find that firms with female CEOs are more likely to keep positive debt capacity and have longer debt maturities.