Research articles for the 2019-06-11

A Dual Method For Backward Stochastic Differential Equations with Application to Risk Valuation
Andrzej Ruszczynski,Jianing Yao

We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we show that a piecewise-constant dual control provides a good approximation on a short interval. A dynamic programming algorithm extends the approximation to a finite time horizon. Finally, we illustrate the application of the procedure to financial risk management in conjunction with nested simulation and on an multidimensional portfolio valuation problem.

A Perspective on Financial Literacy and Inclusion in India
Kapadia, Sunil
The financial inclusion is a crucial element in the fight against poverty. The ability to take effective decisions regarding the use and management of money with required informed judgments is financial literacy. It enables a person to understand the importance of savings and thus regarded as an important requirement for functioning effectively in modern society. Large sections of the rural population borrow from moneylenders (their sole resource) at a very high and unreasonable cost causing slavery and have no access to any financial services. The article highlights that considering the immensely large population of the country, the progress on financial inclusion is not enough and financial institutions and banks need to manage their efforts towards financial inclusion since the development of the economy is impacted and linked with the extent of financial inclusion in the country. Access to finance by the underprivileged group, poor, and disadvantaged is essential to remove poverty on one hand and the economic growth on the other.

Asian Options Pricing in Hawkes-Type Jump-Diffusion Models
Brignone, Riccardo,Sgarra, Carlo
In this paper we propose a method for pricing Asian options in market models with the risky asset dynamics driven by a Hawkes process with exponential kernel. For these processes the couple (λ(t), X(t)) is affine, this property allows to extend the general methodology introduced by Hubalek, Keller-Ressel and Sgarra for Geometric Asian option pricing to jump-diffusion models with stochastic jump intensity. Although the system of ordinary differential equations providing the characteristic function of the related affine process cannot be solved in closed form, a COS-type algorithm allows to obtain the relevant quantities needed for options valuation. We describe, by means of graphical illustrations, the dependence of Asian options prices by the main parameters of the driving Hawkes process. Finally, by using Geometric Asian options values as control variates, we show that Arithmetic Asian options prices can be computed in a fast and efficient way by a standard Monte Carlo method.

Bayesian Estimation of Economic Simulation Models using Neural Networks
Donovan Platt

Recent advances in computing power and the potential to make more realistic assumptions due to increased flexibility have led to the increased prevalence of simulation models in economics. While models of this class, and particularly agent-based models, are able to replicate a number of empirically-observed stylised facts not easily recovered by more traditional alternatives, such models remain notoriously difficult to estimate due to their lack of tractable likelihood functions. While the estimation literature continues to grow, existing attempts have approached the problem primarily from a frequentist perspective, with the Bayesian estimation literature remaining comparatively less developed. For this reason, we introduce a Bayesian estimation protocol that makes use of deep neural networks to construct an approximation to the likelihood, which we then benchmark against a prominent alternative from the existing literature. Overall, we find that our proposed methodology consistently results in more accurate estimates in a variety of settings, including the estimation of financial heterogeneous agent models and the identification of changes in dynamics occurring in models incorporating structural breaks.

Blockchain Hysteria: Adding ‘‘Blockchain’’ to Company’s Name
Jain, Archana,Jain, Chinmay
Using a list of companies that changed their names to add ‘‘blockchain’’ or ‘‘bitcoin’’ to their names, we find that after changing the names, these firms have a significant abnormal positive return that lasts for 2 months. The abnormal return turns negative 5 months after the change. This suggests that these firms changed their names to take advantage of the hysteria surrounding the price rise of bitcoin.

Corporate Governance Through Ownership Structure: Evidence from KSE-100 Index
Shabbir, Aqsa,Tahir, Dr. Safdar Husain,Aziz, Bilal
This paper examined the association between ownership structure, firm performance and dividend policy with respect to Governance perspective of companies present in Karachi Stock Exchange (KSE). A sample of 45 Nonfinancial KSE-100 Index listed firms for a period of 2010 to 2013 has been taken for analysis of this study. Multiple Regression Models are applied on the panel data to measure the relationship between ownership structure, firm performance and dividend policy. The empirical results provide evidence that ownership structure significantly affects the dividend policy and firm performance in connection with family ownership concentration. The empirical results also exhibit a significant negative relation between Dividend payments and ownership concentration and thus support the Entrenchment Theory; it means that the major shareholders protect their own benefit at the cost of minor stakeholders. Furthermore, the results also showed a positive relation of firm performance with foreign holdings, which means better performance is an attractive sign for the foreign investment and financial health of the economy.

Corporate Risk Management: New Empirical Evidence from Foreign Exchange and Interest Rate Risk
Hecht, Andreas
Contemporary corporate risk management with its diverse facets and categories commonly involves the usage of derivative instruments. Most of the relevant empirical literature originates from commodity risk management, even though the most important risk categories in terms of derivative usage are foreign exchange (FX) and interest rate (IR) risk. Empirical evidence in these areas is rare and often relies on alternative indicators of derivative usage due to a limited availability of adequate data. We close this gap in the literature and introduce two innovative and hand-collected datasets â€" one for FX and one for IR risk â€" from the unexplored regulatory environment in France. Based on an unprecedented data granularity with advanced exposure and derivative usage information, we examine the preeminent topics on the relevance and the determinants (together with the identification) of speculative activities in corporate FX and IR risk management.

Covariance Prediction in Large Portfolio Allocation: Supplementary Material
Trucíos, Carlos,Zevallos, Mauricio,Hotta, Luiz Koodi,Santos, André
In this supplementary material we discuss the results corresponding to the case without short-selling constraints of the empirical application in the paper of Trucíos et al. (2019). These results are given in Tables 9-16.

Crowdsourcing and Crowdfunding in the Manufacturing and Services Sectors
Allon, Gad,Babich, Volodymyr
In the last few years, we have seen the emergence of two new ways in which firms interact with outside stakeholders, namely crowdsourcing and crowdfunding service providers. In this article we define crowdsourcing and crowdfunding terms, compare new business models with the traditional ones, review the OM research community's contribution so far, point out useful frameworks for understanding the phenomena, illuminate promising research paths, and highlight open research questions. We also discuss the parallels between these concepts, as well the main differences.

DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
Zihao Zhang,Stefan Zohren,Stephen Roberts

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.

Disclosure and Competition for Capital
Cheng, Stephanie F.,Cuny, Christine,Xue, Hao
To study the relation between disclosure and competition for capital, we use Moody's 2010 recalibration of the municipal rating scale. On a relative basis, the recalibration advantaged heavily upgraded issuers and disadvantaged lightly upgraded issuers. To develop our hypotheses, we set up a simple model in which bond issuers compete for capital from investors. The disadvantaged issuers respond to the recalibration by offering investors better financing terms to compete for investors' capital with the advantaged issuers. These financing terms heighten financing costs that can induce and exacerbate a potential conflict between social welfare and government officials' personal preferences. The conflict of interest calls for higher quality disclosure to assure investors that the government officials will not pursue their personal preference over social welfare. Empirically, we find that the disadvantaged issuers provide timelier financial statements after the recalibration, particularly when those issuers face relatively intense competition for capital. This evidence supports the idea that competition for capital can motivate issuers to provide higher quality disclosure.

Do 'Speed Bumps' Prevent Accidents in Financial Markets?
Goncalves, Jorge,Kräussl, Roman,Levin, Vladimir
Is it true that speed bumps level the playing field, make financial markets more stable and reduce negative externalities of high frequency trading (HFT) firms? We examine how the implementation of a particular speed bump - Midpoint Extended Life order (M-ELO) on Nasdaq impacted financial markets stability in terms of occurrences of mini-flash crashes in individual securities. We use high frequency order book message data around the implementation date and apply difference-in-differences analysis to estimate the average treatment effect of the speed bump on market stability and liquidity provision. The results suggest that the introduction of the M-ELO decreases the average number of crashes on Nasdaq compared to other exchanges by 2.7 per a hundred stocks. Liquidity provision by HFT firms also improves. These findings imply that technology-based solutions by exchanges are feasible alternatives to regulatory intervention towards safer markets.

Ergodicity-breaking reveals time optimal economic behavior in humans
David Meder,Finn Rabe,Tobias Morville,Kristoffer H. Madsen,Magnus T. Koudahl,Ray J. Dolan,Hartwig R. Siebner,Oliver J. Hulme

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theory reveals how individuals should tolerate risk in different environments. To optimise wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing economic theory. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximise the time average growth of wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.

Extending Deep Learning Models for Limit Order Books to Quantile Regression
Zihao Zhang,Stefan Zohren,Stephen Roberts

We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-series. We develop a deep learning architecture that simultaneously models the return quantiles for both buy and sell positions. We test our model over millions of LOB updates across multiple different instruments on the London Stock Exchange. Our results suggest that the proposed network not only delivers excellent performance but also provides improved prediction robustness by combining quantile estimates.

Financial Crises and Liberalisation: Progress or Reversals?
Saka, Orkun,Campos, Nauro F.,De Grauwe, Paul,Ji, Yuemei,Martelli, Angelo
Financial crisis can trigger policy reversals, i.e. they can lead to a process of re- regulation of financial markets. Using a recent comprehensive dataset on financial liberalization across 94 countries for the period between 1973 and 2015, we formally test the validity of this prediction for the member states of the European Union as well as for a global sample. We contribute by (a) using a new up-to date dataset of reforms and crises and (b) subjecting it to a combination of difference-in-differeeces and local projection estimations. In the global sample, our findings consistently confirm that crises lead to a reversal of liberal reforms, suggesting that governments react to crises by re-regulating financial markets. However, in a dynamic setting with impulse-responses, we also find that these new regulations are only temporary and a liberalization process restarts a few years after a financial crisis. One decade later, financial markets have returned to their pre-crisis level of liberalization. In the EU sample, however, we do not find sufficient evidence to support these observations.

Forecasting the Realized Variance in the Presence of Intraday Periodicity
Dumitru, Ana-Maria H.,Hizmeri, Rodrigo,Izzeldin, Marwan
This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted model, HARP, where predictors are built from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000--2016) and via Monte Carlo simulations that the HARP models produce significantly better forecasts, especially at the 1-day and 5-days ahead horizons.

From Implicit to Explicit: The Consequences of Fee Disclosure
Cuny, Christine,Even-Tov, Omri,Watts, Edward M.
We investigate whether more salient fee disclosure mitigates bond market professionals' ability to charge retail investors high fees. We explore changes in fees around FINRA's 2018 amendment of the customer confirmation rule, requiring corporate bond market professionals to explicitly disclose the fee (markup) on some retail trades. Investors could have inferred the fee before the rule change using historical transaction prices. Nonetheless, we find that fees associated with trades subject to explicit fee disclosure decline after the rule change, relative to trades that are not subject to explicit fee disclosure. Our findings are pronounced among bonds for which fees were highest before the rule change. In sum, our evidence shows that the nature of fee disclosure (i.e., explicit or implicit) has real effects on corporate bond market professionals' ability to charge high fees for their services.

Front-Running and Collusion in Forex Trading
Evans, Martin D.D.
Abstract This paper examines the market-wide effects of front-running and information-sharing by dealers in a quantitative microstructure model of Forex trading. Recent investigations by government regulators and court proceedings reveal that there has been widespread sharing of information among Forex dealers working at major banks, as well as the regular front-running of large customer orders. I use the model to study the effects of unilateral front-running, where individual dealers trade ahead of their own customer orders; and collusive front-running where individual dealers trade ahead of another dealer's customer order based on information that was shared among a group of dealers. I find that both forms of front-running create an information externality that significantly affects order flows and Forex prices by slowing down the process through which inter-dealer trading aggregates information from across the market. Font-running reduces dealers' liquidity provision costs by raising the price customers pay to purchase Forex, and lowering the price they receive when selling Forex. These cost reductions are substantial; they lower costs by more than 90 percent. Front-running also affects other market participants that are not directly involved in front-running trades. The information externality makes these participants less willing to speculate on their private information when trading with dealers. This indirect effect of front-running can reduce participants' expected returns by as much as 10 percent. My analysis also shows that collusive front-running has larger effects on order flows than unilateral front-running because information-sharing reduces the risks dealers face when trading ahead of customer orders. However, in other respects, the effects of collusive and unilateral front-running are quite similar. Greater collusion lowers the costs of providing liquidity and it reduces other participants' expected returns, but the effects are small.

Good Dispersion, Bad Dispersion
Kehrig, Matthias,Vincent, Nicolas
Dispersion in marginal revenue products of inputs across plants is commonly thought to reflect misallocation, i.e., dispersion is "bad." We document that most dispersion occurs across plants within rather than between firms. In a model of multi-plant firms, we then show that dispersion can be "good": Eliminating frictions increases productivity dispersion and raises overall output. Based on this framework, we argue that in U.S. manufacturing, one-quarter of the total variance of revenue products reflects good dispersion. In contrast, we find that in emerging economies, almost all dispersion is bad and the gains from eliminating distortions are larger than previously thought.

Higher Capital and Liquidity Regulations of Basel Standards Have Made Banks and Banking Systems Become More Prone to Financial and Economic Crises
Taskinsoy, John
Basel II and III standards are a regulatory consequence following two major crises in systemic nature, the homegrown Asian crisis of 1997-98 and the global financial crisis of 2007-08. Basel I, despite high expectations and claims by the Basel Committee, failed to prevent the following financial crises from occurring in the 1990s; Finnish and Swedish banking crises (early 1990s), Indian economic crisis (1991), Mexican peso crisis (1994), Turkish economic crisis (1994), Asian crisis (1997-98), Russian financial crisis (1998), Argentine economic crisis (1999-2002), and Brazil crisis (1999). The Asian financial crisis in systemic nature cost global investors a jaw dropping close to one trillion dollars. Replacing Basel I with a Revised Framework did not stop the recurrence of financial crises in the new millennium which have been ever more costly, longer-lasting, and unbearably damaging. Basel II, just like Basel I, failed to avoid the following crises either originated in the U.S. or caused by contagion; the bust of the bubble (2001-02), mortgage debacle (2006), global financial crisis (2008), and sovereign debt crisis in eurozone (2010-12). Just these four crises cost the world’s economies as much as thirty trillion dollars. The probability of a high-magnitude financial crisis to occur is between 4% and 5%, which means that by 2030 Basel III may have a chance to prove its ability to withstand shocks; in the event of a failure, the extent of financial loses may be the largest ever ($50 trillion?).

Inequality and the Economic Participation of Women in Sub-Saharan Africa: An Empirical Investigation
Asongu, Simplice,Odhiambo, Nicholas
This study investigates the effect of inequality on female employment in 42 countries in sub-Saharan Africa for the period 2004-2014. Three inequality indicators are used, namely, the: Gini coefficient, Atkinson index and Palma ratio. Two indicators of gender inclusion are also employed, namely: female employment and female unemployment rates. The empirical analysis is based on the Generalised Method of Moments (GMM).The following main findings are established. First, inequality increases female unemployment in regressions based on the Palma ratio. Second, from the robustness checks, inequality reduces female employment within the frameworks of the Gini coefficient and Palma ratio.

Is there a Zero Lower Bound? The Effects of Negative Policy Rates on Banks and Firms
Altavilla, Carlo,Burlon, Lorenzo,Giannetti, Mariassunta,Holton, Sarah
Exploiting confidential data from the euro area, we show that sound banks can pass negative rates on to their corporate depositors without experiencing a contraction in funding. These pass-through effects become stronger as policy rates move deeper into negative territory. Banks offering negative rates provide more credit than other banks suggesting that the transmission mechanism of monetary policy is not hampered. The negative interest rate policy (NIRP) provides further stimulus to the economy through firms’ asset rebalancing. Firms with high current assets linked to banks offering negative rates appear to increase their investment in tangible and intangible assets and to decrease their cash holdings to avoid the costs associated with negative rates. Overall, our results challenge the commonly held view that conventional monetary policy becomes ineffective when policy rates reach the zero lower bound.

Likelihood Evaluation of Jump-Diffusion Models Using Deterministic Nonlinear Filters
Jean-François Bégin,Mathieu Boudreault

In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also stochastic. We show numerically that the deterministic filtering method is precise and much faster than the particle filter, in addition to yielding a smooth function over the parameter space. We then find the maximum likelihood estimates of various models that include stochastic volatility, jumps in the returns and variance, and also stochastic jump arrival intensity with the S&P 500 daily returns. During the Great Recession, the jump arrival intensity increases significantly and contributes to the clustering of volatility and negative returns.

Measuring Euro Area Monetary Policy
Altavilla, Carlo,Brugnolini, Luca,Gurkaynak, Refet S.,Motto, Roberto,Ragusa, Giuseppe
We study the information flow from the ECB on policy dates since its inception, using tick data. We show that three factors capture about all of the variation in the yield curve but that these are different factors with different variance shares in the window that contains the policy decision announcement and the window that contains the press conference. We also show that the QE-related policy factor has been dominant in the recent period and that Forward Guidance and QE effects have been very persistent on the longer-end of the yield curve. We further show that broad and banking stock indices' responses to monetary policy surprises depended on the perceived nature of the surprises. We find no evidence of asymmetric responses of financial markets to positive and negative surprises, in contrast to the literature on asymmetric real effects of monetary policy. Lastly, we show how to implement our methodology for any policy-related news release, such as policymaker speeches. To carry out the analysis, we construct the Euro Area Monetary Policy Event-Study Database (EA-MPD). This database, which contains intraday asset price changes around the policy decision announcement as well as around the press conference, is a contribution on its own right and we expect it to be the standard in monetary policy research for the euro area.

On the Optimal Strategy for the Hedge Fund Manager: An Experimental Investigation
Permana, Yudistira Hendra
This paper examines the empirical validity of Nicolosi’s model (2018) which investigates the optimal strategy for a hedge fund manager under a specific payment contract. The contract specifies that the manager’s payment consists of a fixed payment and a variable payment, which is based on the over-performance with respect to a pre-specified benchmark. The model assumes that the manager is an Expected Utility agent who maximises his or her expected utility by buying and selling the asset at appropriate moments. Nicolosi derives the optimal strategy for the manager. To find this, Nicolosi assumes a Black-Scholes setting where the manager can invest either in an asset or in a money account. The asset price follows geometric Brownian motion and the money account has a constant interest rate. I experimentally test Nicolosi’s model. To meet the aim of this paper, I compare the empirical support of Nicolosi’s story with other possible strategies. The results show that Nicolosi’s model receives strong empirical support for explaining the subjects’ behaviour, though not all of the subjects follow Nicolosi’s model. Having said this, it seems that the subjects somehow follow the intuitive prediction of Nicolosi’s model in which the decision-maker responds to the difference between the managed portfolio and the benchmark to determine the portfolio allocation.

Optimal hedging under fast-varying stochastic volatility
Josselin Garnier,Knut Solna

In a market with a rough or Markovian mean-reverting stochastic volatility there is no perfect hedge. Here it is shown how various delta-type hedging strategies perform and can be evaluated in such markets in the case of European options. A precise characterization of the hedging cost, the replication cost caused by the volatility fluctuations, is presented in an asymptotic regime of rapid mean reversion for the volatility fluctuations. The optimal dynamic asset based hedging strategy in the considered regime is identified as the so-called `practitioners' delta hedging scheme. It is moreover shown that the performances of the delta-type hedging schemes are essentially independent of the regularity of the volatility paths in the considered regime and that the hedging costs are related to a vega risk martingale whose magnitude is proportional to a new market risk parameter. It is also shown via numerical simulations that the proposed hedging schemes which derive from option price approximations in the regime of rapid mean reversion, are robust: the `practitioners' delta hedging scheme that is identified as being optimal by our asymptotic analysis when the mean reversion time is small seems to be optimal with arbitrary mean reversion times.

ProPublica's COMPAS Data Revisited
Matias Barenstein

In this paper I re-examine the COMPAS recidivism score and criminal history data collected by ProPublica in 2016, which has fueled intense debate and research in the nascent field of `algorithmic fairness' or `fair machine learning' over the past three years. ProPublica's COMPAS data is used in an ever-increasing number of studies to test various definitions and methodologies of algorithmic fairness. This paper takes a closer look at the actual datasets put together by ProPublica. By doing so, I find that ProPublica made an important data processing mistake when it created some of the key datasets most often used by other researchers. In particular, the datasets built to study the likelihood of recidivism within two years of the original COMPAS screening date. As I show in this paper, ProPublica made a mistake implementing the two-year sample cutoff rule for recidivists in such datasets (whereas it implemented an appropriate two-year sample cutoff rule for non-recidivists). As a result, ProPublica incorrectly kept a disproportionate share of recidivists. This data processing mistake leads to biased two-year recidivism datasets, with artificially high recidivism rates. This also affects the positive and negative predictive values. On the other hand, this data processing mistake does not impact some of the key statistical measures highlighted by ProPublica and other researchers, such as the false positive and false negative rates, nor the overall accuracy.

Risk Management in Financial Institutions
Rampini, Adriano A.,Viswanathan, S.,Vuillemey, Guillaume
We study risk management in financial institutions using data on hedging of interest rate and foreign exchange risk. We find strong evidence that institutions with higher net worth hedge more, controlling for risk exposures, both across institutions and within institutions over time. For identification, we exploit net worth shocks resulting from loan losses due to drops in house prices. Institutions that sustain such shocks reduce hedging significantly relative to otherwise similar institutions. The reduction in hedging is differentially larger among institutions with high real estate exposure. The evidence is consistent with the theory that financial constraints impede both financing and hedging.

State Controlling Shareholder and Expropriation
Lin, Chen,Liu, Hang,Ni, Chenkai,Zhang, Bohui
We examine how state objectives affect state-owned enterprises (SOEs)’ payout policies. Our identification strategy relies on a series of reforms that mandate parent central state-owned enterprises (CSOEs) in China to contribute a proportion of their consolidated net income to the state for the formation of a fiscal fund initiated in 2007. We show that, upon the inception of these reforms, the listed CSOEs controlled by parent CSOEs experience a significant reduction in dividend payouts. Moreover, these dividend reductions are concurrent with an increase in both within-group borrowings and related party transactions, i.e., two primary channels of reallocating resources within the group. We conclude by showing that the listed CSOEs’ dividend policies tend to be explained by their group managers’ career concerns and that such policies eventually hurt the minority shareholders’ interests, manifested in a postreform reduction in firm valuation.

Tensor Processing Units for Financial Monte Carlo
Francois Belletti,Davis King,Kun Yang,Roland Nelet,Yusef Shafi,Yi-Fan Chen,John Anderson

Monte Carlo methods are core to many routines in quantitative finance such as derivatives pricing, hedging and risk metrics. Unfortunately, Monte Carlo methods are very computationally expensive when it comes to running simulations in high-dimensional state spaces where they are still a method of choice in the financial industry. Recently, Tensor Processing Units (TPUs) have provided considerable speedups and decreased the cost of running Stochastic Gradient Descent (SGD) in Deep Learning. After having highlighted computational similarities between training neural networks with SGD and stochastic process simulation, we ask in the present paper whether TPUs are accurate, fast and simple enough to use for financial Monte Carlo. Through a theoretical reminder of the key properties of such methods and thorough empirical experiments we examine the fitness of TPUs for option pricing, hedging and risk metrics computation. We show in the following that Tensor Processing Units (TPUs) in the cloud help accelerate Monte Carlo routines compared to Graphics Processing Units (GPUs) which in turn decreases the cost associated with running such simulations while leveraging the flexibility of the cloud. In particular we demonstrate that, in spite of the use of mixed precision, TPUs still provide accurate estimators which are fast to compute. We also show that the Tensorflow programming model for TPUs is elegant, expressive and simplifies automated differentiation.

The Capital Matthew Effect
Su, Dan
This study shows that technological specialization is the primary driver of long-term international capital flows. The underlying mechanism comprises the two faces of capital scarcity: although a shortage of capital currently generates a higher marginal product, it also makes a country incline toward capital-saving technology. Therefore, in equilibrium, the rate of capital returns is jointly determined by a convergence effect from diminishing return and an opposing divergence effect from directed technological change. If the elasticity of substitution between labor and capital is sufficiently high, then initially capital-rich countries will favor a capital-biased technology and continuously import capital from capital-poor countries that develop a labor-biased technology. I name this anti-convergence force on capital allocation as the capital Matthew effect. With this perspective, we can rationalize many related international finance puzzles, such as the Lucas paradox, global imbalance, and the allocation puzzle.

The Impact of Accounting Standards on the Hedging Behaviour in Europe and the USA (Einfluss der Rechnungslegungsstandards auf das Hedging-Verhalten in Europa und den USA)
Eigenthaler, Carolin,Hecht, Andreas,Hachmeister, Dirk
English Abstract: The internationalization of entrepreneurial activity and the dynamics of global markets continue to increase the complexity of firms' businesses and thus the importance of risk management. In order to hedge against financial risks, it is particularly important for companies to enter into hedging relationships. This is done within the framework of the bookkeeping practice of hedge accounting, whereby the different accounting standards are of particular importance. Do Europeans hedge their risk differently from Americans and what role do the respective accounting rules play? This paper examines the impact of accounting standards on the hedging behavior of non-financial firms in Europe and the US, based on existing empirical research on the application of hedge accounting in business practice.German Abstract: Die Internationalisierung unternehmerischer Tätigkeit und die Dynamik globaler Märkte lassen die Komplexität der Geschäfte von Unternehmen und damit die Bedeutung des Risikomanagements weiter wachsen.Um sich gegen finanzielle Risiken abzusichern, ist bei Unternehmen vor allem das Eingehen von Sicherungsbeziehungen zu beobachten. Für diese gelten im Allgemeinen besondere Vorschriften zum Hedge Accounting , wodurch unterschiedlichen Rechnungslegungsstandards eine besondere Bedeutung zukommt. Hedgen Europäer ihr Risiko auf andere Art und Weise als Amerikaner und welche Rolle spielen dabei die jeweiligen Bilanzierungsvorschriften? Der Beitrag untersucht den Einfluss der Rechnungslegungsstandards auf das Hedging-Verhalten von Nichtbanken in Europa und den USA auf Grundlage bislang durchgeführter empirischer Studien zur Untersuchung der Anwendung des Hedge-Accounting in der Unternehmenspraxis.

The Impact of the Global Financial Crisis on the Efficiency and Performance of Latin American Stock Markets
Zhu, Zhenzhen,Bai, Zhidong,Vieito, João Paulo,Wong, Wing‐Keung
We analyze the impact of the most recent global financial crisis (GFC) on the seven most important Latin American stock markets. Our mean-variance analysis shows that the markets are significantly less volatile and, in general, investors prefer to invest in the post-GFC period. Our results from the Hurst exponent and runs and variance-ratio tests show that the randomness and efficiency have been improved after the GFC. The stochastic dominance test shows that the markets are efficient, there is no arbitrage opportunity due to the GFC in our studying period, and, in general, investors prefer investing in the post-GFC period. The results confirm that the 2008 global financial crisis does have some positive impacts on Latin American stock markets. Our findings provide impor-tant information for investors and market regulators in their decision making in investment and setting regulations.

The Social Psychology of Financial Regulatory Governance
Castellano, Giuliano G.,Helleringer, Genevieve
This paper contributes to addressing a fundamental question: how do institutions, in general, and financial regulators, in particular, “think”? To this end, the analytical tools of social psychology are applied to the regulatory framework for financial services in the European Union. The paper reveals a relationship between the constitutional status of EU regulators and the dominant group dynamics typified in the literature of social psychology. Such a relationship indicates that institutional structures might favour the emergence of specific behavioural patterns and modus operandi within regulatory bodies. Furthermore, the identification of dominant group dynamics paves the way to a more profound understanding of conflictual dynamics within groups of decision-makers. Such a novel analytical map is, then, applied to the context of the ongoing debate as to whether, following Brexit, the decision-making process of EU regulators is poised to be marked by a divide separating eurozone and non-eurozone Member States.

The Stock Market; An Imperative for Economic Growth Case Study of Nigeria Stock Exchange
oriaregbete, Solomon
This study examined the role of the stock market in Nigeria's economic growth. It aimed at finding out the relationship between stock market activities proxied by market capitalization (MCP), all share index (ASI) and value of new shares (VNS) on Nigeria’s economic growth proxied by GDP. Data were collected from CBN Statistical Bulletin from 1985-2016. The regression analysis, ADF test, Cointegration Error correction model and granger causality were used to analysed the data. The regression analysis shows that ASI has positive but insignificant relationship with GDP. MCP has positive and significant relationship with GDP. VNS has negative and significant relationship with GDP. The ADF result shows that all the variables are stationary in order one. The co-integration result suggests that there were two cointegrating equations while the error correction model indicates a negative sign with F-cal having prob.value of 0.0006 hence there is long run relationship between capital market activities and Nigeria’s GDP. The granger causality test shows that there is no granger causality relationship between GDP and ASI while VNS and MCP have unidirectional causality relationship with GDP. It concluded that the establishment of the stock market is one the best thing that has happened to the Nigerian economy. From the findings, it recommends the need for more reform to be carried out at the market to enhance its activities and financial intermediation functions. Furthermore, information on new shares should be promoted through the mass media to encourage buying by the public.

Theories of Executive Remuneration
Nedelchev, Miroslav
The aim of the article is to present the theories of remuneration. Both classical and modern theories are presented within their time and economic environment. The anchor of article is the model principal-agent and reducing asymmetric information through remuneration. The conclusions of the article define a wide range of theories. All theories aim to solve the principal-agent problem through a new tool - the remuneration. The nuances of individual theories can be determined from the different periods of their occurrence and from the dominant economic environment for the essence of the remuneration.

Volatility Spillover Among Industries in the Capital Market in Iran
Botshekan, Mohamad Hashem,Mohseni, Hossein
Measuring the dynamic relationship between banking and industries with systemic importance has attracted much attention after the recent financial crisis. This paper examines the dynamic conditional correlations and volatility spillover using three popular multivariate GARCH models in the twelve-year period (from the beginning of 2005 to the beginning of 2016) among the fourteen systemically important industries in Iran’s capital market. The purpose of this study is to understand and identify the volatility spillover between industries to predict financial fluctuations, as well as policy decisions and risk management. The results of this study confirm the spillover between “Banking” and the five industries of "Basic Metals", "Industrial Multidisciplinary", "Investments", "Computers", and "Transportation & Warehousing". There is also an asymmetric spillover between “Banking” index and the "Chemical Industry", the "Extraction of Metal Ores", "Pharmaceuticals" and "Communications Devices". The results are used for mapping fundamental analysis and risk programming.