# Research articles for the 2019-03-20

SSRN

This paper provides a systematic literature review on the literature on corporate governance in banks. The review is conducted over academic papers published in the period 1980-2015, identifying 35 years of evolution in the core aspects of banking corporate governance: risk management, ownership structure and executive compensation of banks. Best practices for increasing performance and reducing risk in banks are commented, when identified. Gaps in the literature and lack of univocal consensus on the different implementation of corporate governance in the selected topic are also identified.

arXiv

We treat a fairly broad class of financial models which includes markets with proportional transaction costs. We consider an investor with cumulative prospect theory preferences and a non-negativity constraint on portfolio wealth. The existence of an optimal strategy is shown in this context in a class of generalized strategies.

arXiv

In this paper, we present own point of view how the unexpected fluctuations of the long-term real interest rate can be explained. We describe a macroeconomic environment by the modification of the fundamental macroeconomic equilibrium model called the IS-LM model. Last but not least, we suggest a possible cooperation between the fiscal and monetary policy to reduce these fluctuations. Our modelling is demonstrated on an illustrative example.

arXiv

Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recently, several systemic risk measures are proposed in the literature that are used to determine capital requirements for the members subject to joint risk considerations. We address the problem of computing systemic risk measures for systems with sophisticated clearing mechanisms. In particular, we consider the Eisenberg-Noe network model and the Rogers-Veraart network model, where the former one is extended to the case where operating cash flows in the system are unrestricted in sign. We propose novel mixed-integer linear programming problems that can be used to compute clearing vectors for these models. Due to the binary variables in these problems, the corresponding (set-valued) systemic risk measures fail to have convex values in general. We associate nonconvex vector optimization problems to these systemic risk measures and solve them by a recent nonconvex variant of Benson's algorithm which requires solving two types of scalar optimization problems. We provide a detailed analysis of the theoretical features of these problems for the extended Eisenberg-Noe and Rogers-Veraart models. We test the proposed formulations on computational examples and perform sensitivity analyses with respect to some model-specific and structural parameters.

SSRN

This paper provides a two steps investigation of the literature on banking corporate governance. We firstly perform a systematic literature review on the academics papers focused on risk management, compensation and ownership structure of banks. Then we run a meta-analysis investigation over more than 2,500 observations to clarify the understanding of the relationship with performance and risk in banks. The sub-group analysis related with bank performance shows a clear and significant finding: Board ownership, CEO ownership and Controlling shareholder enhance the performance of banks. Conversely, State ownership is negatively associated with bank performance. Results of the whole investigation and directions for scholars are also discussed.

arXiv

We view a conic optimization problem that has a unique solution as a map from its data to its solution. If sufficient regularity conditions hold at a solution point, namely that the implicit function theorem applies to the normalized residual function of [Busseti et al., 2018], the problem solution map is differentiable. We obtain the derivative, in the form of an abstract linear operator. This applies to any convex optimization problem in conic form, while a previous result [Amos et al., 2016] studied strictly convex quadratic programs. Such differentiable problems can be used, for example, in machine learning, control, and related areas, as a layer in an end-to-end learning and control procedure, for backpropagation. We accompany this note with a lightweight Python implementation which can handle problems with the cone constraints commonly used in practice.

arXiv

Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the global scale are rare. In this work, we use LinkedIn's employment histories of more than 500 million users over 25 years to construct a labor flow network of over 4 million firms across the world and apply a recursive network community detection algorithm to reveal the hierarchical structure of geo-industrial clusters. We show that the resulting geo-industrial clusters exhibit a stronger association between the influx of educated-workers and financial performance, compared to existing aggregation units. Furthermore, our additional analysis of the skill sets of educated-workers supplements the relationship between the labor flow of educated-workers and productivity growth. We argue that geo-industrial clusters defined by labor flow provide better insights into the growth and the decline of the economy than other common economic units.

SSRN

This article develops on the premise that - as the emergence of low-carbon and transitional energy markets gain traction globally, the viability and security of gas supply markets are becoming as essential as the need to decarbonise and curb unsustainable practices such as flaring and methane leakages along the value chain. In the Sub-Saharan African context, it is necessary to ensure the reliability of supplies from upstream petroleum production sources as well as promote commercially secure institutional and investment climate of the domestic downstream energy sectors. The paper notes that energy demand in Africa is projected to grow at 3.5% per annum (p.a.) over the next couple of decades, while gas production is expected to increase by 110%. By examining relevant developments in Nigeria and Ghana such as the Offshore Cape Three Points (OCTP) area projects, the Nigerian LNG Project and the West African Gas Pipeline (WAGP) project, this paper discusses the spectrum of international petroleum transactions, legal, policy, and risk assessment issues that underpin gas utilisation and commercialisation in sub-region. It contends that projects designed to support production and supplies must be bankable, thus requiring applicable legal, regulatory and contractual frameworks which are designed to enhance timely investment decisions and effectively mitigate post and pre-completion risks.

SSRN

Are press releases on Corporate Governance price sensitive? What is the impact of Corporate Governance information on stock prices of banks? This paper addresses these questions by applying an event study methodology on 70 press releases published by the Euro area banks listed on the Eurostoxx banks Index, from 2007 to 2016. Systemic shocks are explored as well idiosyncratic ones. Our results show that investment decisions are significantly but negatively influenced by the disclosure of a press release on corporate governance as if this kind of news leads investors to perceive the banks' prospects negatively. The best of our knowledge this is the first paper that investigates European banks press releases on corporate governance. Findings are relevant for banks' management and their disclosure policy. Nonetheless, further research is needed to investigate differences and similarities between an area of governance disclosure and another.

SSRN

We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous assets. We assemble a database of 1.1 million paintings that were auctioned between 2008 and 2015. We use a popular machine-learning techniqueâ€"neural networksâ€"to develop a price prediction algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneersâ€™ pre-sale estimates. Machine learning is particularly helpful for assets that are associated with higher levels of ex-ante price uncertainty. Finally, we show that it can help overcome expertsâ€™ systematic biases in expectations formation.

arXiv

The application of Markov chains to modelling refugee crises is explored, focusing on local migration of individuals at the level of cities and days. As an explicit example we apply the Markov chains migration model developed here to UNHCR data on the Burundi refugee crisis. We compare our method to a state-of-the-art `agent-based' model of Burundi refugee movements, and highlight that Markov chain approaches presented here can improve the match to data while simultaneously being more algorithmically efficient.

SSRN

In this work, behavior of money multipliers for a developing country, Turkey is examined and is calculated by estimating the values of the model constructed for the purpose for the period of 1952-1972. However, model lends itself for applications covering any other period.Study includes three sections. First, basic money multipliers model is presented. Then, model is modified and developed considering Turkeyâ€™s special conditions. In the final section, the improved model is tested against actual data.Interestingly, although tentative, the results are different from the limited number of studies about money multipliers in Turkey. Nevertheless, the necessity for a more detailed model to test actual data is imperative.

arXiv

This paper presents several models addressing optimal portfolio choice, optimal portfolio liquidation, and optimal portfolio transition issues, in which the expected returns of risky assets are unknown. Our approach is based on a coupling between Bayesian learning and dynamic programming techniques that leads to partial differential equations. It enables to recover the well-known results of Karatzas and Zhao in a framework \`a la Merton, but also to deal with cases where martingale methods are no longer available. In particular, we address optimal portfolio choice, portfolio liquidation, and portfolio transition problems in a framework \`a la Almgren-Chriss, and we build therefore a model in which the agent takes into account in his decision process both the liquidity of assets and the uncertainty with respect to their expected return.

SSRN

Sukuk restructuring primarily aims at offering a debtor more latitude, in form and time, to settle his obligations. To meet Shariâ€™ah requirements of transferring assets to Sukuk holders in asset-based Sukuk, the originator usually transfers the beneficial ownership to the issuer special purpose vehicles (SPV). However, in asset-backed Sukuk, the originator sells the underlying asset to an SPV and Sukuk holders do not have recourse to the originator in the event of defaults. Among some key unresolved Shariâ€™ah issues in this regard is whether a change of contract necessitates entering a new contract. Other related issues that conflict with the tenets of Shariâ€™ah are: (1) Sukuk structuring on tangible assets and debts; (2) receiving the full title by the Sukuk holders to the underlying assets in the event of default in case of securities that are publicized as asset backed; (3) Sukukâ€™s similarity with interest bearing conventional bonds: (a) capital guarantee by the originator or third party, (b) the originatorsâ€™ promise to repurchase Sukuk at face value upon their redemption, and (c) providing internal and external credit enhancement. The Shariâ€™ah-compliance of the above-mentioned clauses and structures of Sukuk remain debated among the Shariâ€™ah scholars. Based on some specific cases, this study examines the Shariâ€™ah viewpoint on sukuk restructuring and potential solutions to these unresolved Shariâ€™ah issues in light of the past and recent declaration of some Sukuk defaults as non-Shariâ€™ah complaints. Undoubtedly, resolution of these and other unresolved issues pertaining to Sukuk defaults can help strengthen the confidence of investors in Islamic capital market structures.

SSRN

This paper examines the ability of bond and stock markets to predict subsequent GDP growth over a range of horizons for twelve international countries. The results, using linear, probit, time- and regime-varying in-sample regressions and out-of-sample forecasting, confirm the view that both financial markets exhibit predictive power for future output growth. moreover, there is notable variation within the strength of the predictive relation, for example, predictive power increases during the financial crisis period. Results suggest that while the term structure arguably exhibits stronger predictive power, both series contain distinct predictive information. Notably, predictive power emanating from the stock return series appears stronger over shorter (up to four-quarter) time horizons, while the term structure series exhibits more consistent predictive power over a range of horizons. Considering different regimes, we observe that the bond market exhibits greater predictive power for a flatter yield curve and lower stock prices relative to fundamentals, while the stock market exhibits greater predictive power for a steeper yield curve and higher relative stock prices. This suggests that the two financial markets exhibit different information content for future output growth. This view is further supported by forecast results whereby a model that includes both financial series outperforms a model that only includes one. Forecast encompassing tests further support the view that stock returns contain additional information over that presented by the term structure alone.

SSRN

We demonstrate that a limited adoption problem arises endogenously in a Proof-of-Work (PoW) payments blockchain. Increased transaction demand increases fees because PoW imposes an artificial supply constraint. The increased fees in turn induce validators to enter the network because of PoWâ€™s permissionless nature. The increased network size protracts the consensus process and thereby delays payment confirmation. Given access to traditional payment systems, users prefer to transact via the blockchain only if they possess extreme insensitivity to delays. A PoW payments blockchain therefore cannot obtain widespread adoption. A permissioned blockchain offers an alternative to overcome this problem because such a blockchain neither imposes PoWâ€™s artificial supply constraint nor admits free entry among validators. Nonetheless, validators may collude on such a blockchain if implemented with a standard consensus protocol. We offer an alternative consensus protocol that overcomes such collusion. Our protocol employs the blockchainâ€™s native cryptocurrency to induce the desired behavior and thereby provides both expedient transaction processing and blockchain security.

arXiv

We consider the tail probabilities of stock returns for a general class of stochastic volatility models. In these models, the stochastic differential equation for volatility is autonomous, time-homogeneous and dependent on only a finite number of dimensional parameters. Three bounds on the high-volatility limits of the drift and diffusion coefficients of volatility ensure that volatility is mean-reverting, has long memory and is as volatile as the stock price. Dimensional analysis then provides leading-order approximations to the drift and diffusion coefficients of volatility for the high-volatility limit. Thereby, using the Kolmogorov forward equation for the transition probability of volatility, we find that the tail probability for short-term returns falls off like an inverse cubic. Our analysis then provides a possible explanation for the inverse cubic fall off that Gopikrishnan et al. (1998) report for returns over 5-120 minutes intervals. We find, moreover, that the tail probability scales like the length of the interval, over which the return is measured, to the power 3/2. There do not seem to be any empirical results in the literature with which to compare this last prediction.

arXiv

The purpose of this study is to find a relation between sex education and abortion in the United States. Accordingly, multivariate logistic regression is employed to study the relation between abortion and frequency of sex, pre-marriage sex, and pregnancy by rape. The finding shows the odds of abortion among those who have had premarital sex, more frequent sex before marriage, and been the victim of rape is higher than those who have not experienced any of these incidents. The output identified with one unit increase in pre-marriage sex the log-odds of abortion increases by 0.47. Similarly, it shows by one unit increase in the frequency of sex, the log-odds of abortion increases by 0.39. Also, for every additional pregnancy by rape, there is an expectation of a 3.17 increase in the log-odds of abortion. The findings of this study also suggests abortion is associated with sex education. Despite previous findings, this study shows the factors of age, having children, and social standing is not considered a burden to parents and thereby do not have a causal relation to abortion.

arXiv

Is urban economic performance driven by a few factors? We study a simple model for the probability that an individual in a city is employed in a given urban activity. The theory posits that three quantities drive this probability: the activity-specific complexity, individual-specific knowhow, and the city-specific collective knowhow. We use data on employment across industries and metropolitan statistical areas in the US, from 1990 to 2016, to show that these drivers can be measured and have measurable consequences over measures of urban economic performance. First, we analyze the functional form of the probability function proposed by the theory, and show its superiority when compared to competing alternatives. Second, we show that individual and collective knowhow correlate with measures of urban economic performance, suggesting the theory can provide testable implications for why some cities are more prosperous than others.

SSRN

We assess how a major, unconventional central bank intervention, Draghiâ€™s â€œwhatever it takesâ€ speech, affected lending conditions. Similar to other large interventions, it responded to adverse financial and macroeconomic developments that also influenced the supply and demand for credit. We avoid such endogeneity concerns by focusing on a third country and comparing lending conditions by euro area and other banks to the same borrower. We show that the intervention reversed prior risk-takingâ€"â€"in volume, price, and loan credit ratingsâ€"â€"by subsidiaries of euro area banks relative to local and other foreign banks. Our results document a new effect of large central banksâ€™ interventions and are robust along many dimensions.