# Research articles for the 2021-07-21

Bank Earnings Management Using Loan Loss Provisions: Comparing the UK, France, South Africa and Egypt
Ozili, Peterson K
SSRN
This paper investigates bank earnings management using loan loss provision (LLP). The paper examines income smoothing which is a type of earnings management. It compares the income smoothing behaviour of banks in the UK, France, South Africa and Egypt. The findings show that bank income smoothing is present in the UK and Egypt, and absent in France and South Africa during the period examined. Banks in Egypt used LLPs to smooth income before the global financial crisis. Meanwhile, bank income smoothing is pronounced in France during and after the financial crisis but is absent in the pre-crisis period. Also, bank income smoothing is reduced in countries that (i) have strict banking supervision, (ii) adopt common law such as the United Kingdom, and (iii) adopt civil law such as France and Egypt. Bank earnings management is also greater in countries that adopt a mixed legal system such as South Africa, and in countries that adopt IFRS accounting standards.

Calibration of Local-Stochastic Volatility Models by Optimal Transport
Ivan Guo,Gregoire Loeper,Shiyi Wang
arXiv

In this paper, we study a semi-martingale optimal transport problem and its application to the calibration of Local-Stochastic Volatility (LSV) models. Rather than considering the classical constraints on marginal distributions at initial and final time, we optimise our cost function given the prices of a finite number of European options. We formulate the problem as a convex optimisation problem, for which we provide a PDE formulation along with its dual counterpart. Then we solve numerically the dual problem, which involves a fully non-linear Hamilton-Jacobi-Bellman equation. The method is tested by calibrating a Heston-like LSV model with simulated data and foreign exchange market data.

Capital Reallocation and Firm-Level Productivity Under Political Uncertainty
Tut, Daniel,Cao, Melanie
SSRN
Does policy uncertainty affect productivity? Policy uncertainty creates delays as firms await new information about prices, costs and other market conditions before committing resources. Such delays can have real consequences on firmsâ€™ productivity and corporate decisions. First, we find that economic policy uncertainty has a negative impact on firm-level productivity. Second, debt magnifies the adverse effects of policy uncertainty on productivity, but access to external financing during periods of significant policy uncertainty shocks has a positive impact on firm-level productivity. Third, Policy uncertainty is positively related to cash holdings but this effect is mostly driven by highly productive firms and by firms with higher levels of irreversible investments since these firms face higher opportunity costs in future states. The three findings are robust to various specifications and provide an affirmative answer to the opening question.

Currency Network Risk
Mykola Babiak,Jozef Barunik
arXiv

This paper identifies new currency risk stemming from a network of idiosyncratic option-based currency volatilities and shows how such network risk is priced in the cross-section of currency returns. A portfolio that buys net-receivers and sells net-transmitters of short-term linkages between currency volatilities generates a significant Sharpe ratio. The network strategy formed on causal connections is uncorrelated with popular benchmarks and generates a significant alpha, while network returns formed on aggregate connections, which are driven by a strong correlation component, are partially subsumed by standard factors. Long-term linkages are priced less, indicating a downward-sloping term structure of network risk.

Customer Liquidity Provision in Corporate Bond Markets: Electronic Trading versus Dealer Intermediation
Mattmann, Brian
SSRN
We investigate electronic trading among customers under normal market conditions and during the Covid-19 crisis using a unique data sample of U.S. corporate bond transactions from UBS Bond Port. We show that electronic customer-to-customer (C-to-C) trading is beneficial in terms of costs for orders up to $1 million. The advantage of electronic C-to-C trading primarily benefits liquidity-consuming customers, as dealers penalize liquidity takers more than the electronic trading channel. Contrary to expectations, at the onset of the Covid-19 crisis the costs for liquidity takers selling bonds electronically inverted, resulting in negative aggressor markups. We argue that this effect is allocated to the trading protocol of a firm and transparent order book. Volumes in electronic C-to-C trading are more driven by orders wherein the liquidity-consuming party is selling; this effect is amplified in stressed markets. Whereas electronic liquidity provision by dealers is primarily concentrated to normal market conditions, electronic C-to-C trading becomes more important in stressed markets. Literature underestimates the effect of inverting markups during the Covid-19 crisis and thus undervalues electronic C-to-C trading as a viable liquidity pool in stressed markets. Deep Learning, Predictability, and Optimal Portfolio Returns Mykola Babiak,Jozef Barunik arXiv We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. We demonstrate that a long-short-term-memory recurrent neural network, which excels in learning complex time-series dependencies, generates a superior performance among a variety of networks considered. Return predictability via deep learning generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints. Default Distances Based on the KMV-CEV Model Wen Su arXiv This paper presents a new method to assess default risk based on applying non constant volatility to the KMV model, taking the CEV model as an instance. We find the evidence that the classical KMV model could not distinguish ST companies in China stock market. Aiming at improve the accuracy of the KMV model, we assume the firm's asset value dynamics are given by the CEV process$\frac{dV_A}{V_A} = \mu_A dt + \delta V_A^{\beta-1}dB$and use fixed effects model and equivalent volatility method to estimate parameters. The estimation results show the$\beta>1$for non ST companies while$\beta<1\$ for ST companies and the equivalent volatility method estimate the parameters much more precisely. Compared with the classical KMV model, our CEV-KMV model fits the market better in forecasting the default probability. We also provide an insight that other volatility model can be applied, too.

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

In this paper we introduce a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities, including a dynamic examination of default risk. Such a system allows us to distinguish between defaults resulting from either insolvency or illiquidity, and to analyze the resulting effects on the rest of the network. As a special case, we are also able to recover the solution to the Eisenberg-Noe system under certain model choices within the dynamic framework.

Forecasting performance of workforce reskilling programmes
Evan Hurwitz,George Cevora
arXiv

Estimating success rates for programmes aiming to reintegrate theunemployed into the workforce is essential for good stewardship of publicfinances. At the current moment, the methods used for this task arebased on the historical performance of comparable programmes. In lightof Brexit and Covid-19 simultaneously causing a shock to the labourmarket in the UK we developed an estimation method that is basedon fundamental factors involved - workforce demand and supply - asopposed to the historical values which are quickly becoming irrelevant.With an average error of 3.9% of the re-integration success rate, ourmodel outperforms the best benchmark known to us by 53%

Funk Brothers: An Exercise in Obviousness
Tu, S. Sean
SSRN
This article departs from the dogma that Funk Brothers delineates the limits of patentable subject matter and gives several rationales for why Funk Brothers is, in actuality, a case that outlines an obviousness standard. As an initial matter, one only needs to look to the historical background in which Funk Brothers sits to understand this maxim. Funk Brothers was decided before the codification of the 1952 Patent Act and, in fact, simply defines the current obviousness standard later codified in 35 U.S.C. Â§ 103(a). Accordingly, Funk Brothers should not be cited as a case against the patentability of genes under non-patentable subject matter (35 U.S.C. Â§101). Interestingly, the analysis that the Funk Brothers Court uses is, at its core, an obviousness analysis. This article suggests that the breadth of subject matter patentability should be kept broad. Furthermore, this article suggests that the novelty and obviousness standards are better tools that can limit and define the boundaries of patentability for gene patents.

Order Book Queue Hawkes-Markovian Modeling
Philip Protter,Qianfan Wu,Shihao Yang
arXiv

This article presents a Hawkes process model with Markovian baseline intensities for high-frequency order book data modeling. We classify intraday order book trading events into a range of categories based on their order types and the price changes after their arrivals. To capture the stimulating effects between multiple types of order book events, we use the multivariate Hawkes process to model the self- and mutually-exciting event arrivals. We also integrate a Markovian baseline intensity into the event arrival dynamic, by including the impacts of order book liquidity state and time factor to the baseline intensity. A regression-based non-parametric estimation procedure is adopted to estimate the model parameters in our Hawkes+Markovian model. To eliminate redundant model parameters, LASSO regularization is incorporated in the estimation procedure. Besides, model selection method based on Akaike Information Criteria is applied to evaluate the effect of each part of the proposed model. An implementation example based on real LOB data is provided. Through the example, we study the empirical shapes of Hawkes excitement functions, the effects of liquidity state as well as time factors, the LASSO variable selection, and the explanatory power of Hawkes and Markovian elements to the dynamics of the order book.

Pricing Exchange Option Based on Copulas by MCMC Algorithm
Wen Su
arXiv

This paper focus on pricing exchange option based on copulas by MCMC algorithm. Initially, we introduce the methodologies concerned about risk-netural pricing, copulas and MCMC algorithm. After the basic knowledge, we compare the option prices given by different models, the results show except Gumbel copula, the other model provide similar estimation.

The Adoption of Blockchain-based Decentralized Exchanges
Agostino Capponi,Ruizhe Jia
arXiv

We investigate the market microstructure of Automated Market Makers (AMMs), the most prominent type of blockchain-based decentralized exchanges. We show that the order execution mechanism yields token value loss for liquidity providers if token exchange rates are volatile. AMMs are adopted only if their token pairs are of high personal use for investors, or the token price movements of the pair are highly correlated. A pricing curve with higher curvature reduces the arbitrage problem but also investors' surplus. Pooling multiple tokens exacerbates the arbitrage problem. We provide statistical support for our main model implications using transaction-level data of AMMs.

What Explains Gender Gap in Unpaid Household and Care Work in India?
Athary Janiso,Prakash Kumar Shukla,Bheemeshwar Reddy A
arXiv

Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been undertaken in the context of India. The present paper, using the recent Time Use Survey (2019) data, examines the socioeconomic and demographic factors associated with variation in time spent on unpaid household and care work among men and women. It analyses how much of the gender gap in the time allocated to unpaid work can be explained by differences in these factors. The findings show that women spend much higher time compared to men in unpaid household and care work. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results indicate that unobserved gender norms and practices most crucially govern the allocation of unpaid work within Indian households.