Research articles for the 2020-02-02

An Interacting Agent Model of Economic Crisis
Yuichi Ikeda

Most national economies are linked by international trade. Consequently, economic globalization forms a massive and complex economic network with strong links, that is, interactions arising from increasing trade. Various interesting collective motions are expected to emerge from strong economic interactions in a global economy under trade liberalization. Among the various economic collective motions, economic crises are our most intriguing problem. In our previous studies, we have revealed that the Kuramoto's coupled limit-cycle oscillator model and the Ising-like spin model on networks are invaluable tools for characterizing the economic crises. In this study, we develop a mathematical theory to describe an interacting agent model that derives the Kuramoto model and the Ising-like spin model by using appropriate approximations. Our interacting agent model suggests phase synchronization and spin ordering during economic crises. We confirm the emergence of the phase synchronization and spin ordering during economic crises by analyzing various economic time series data. We also develop a network reconstruction model based on entropy maximization that considers the sparsity of the network. Here network reconstruction means estimating a network's adjacency matrix from a node's local information. The interbank network is reconstructed using the developed model, and a comparison is made of the reconstructed network with the actual data. We successfully reproduce the interbank network and the known stylized facts. In addition, the exogenous shock acting on an industry community in a supply chain network and financial sector are estimated. Estimation of exogenous shocks acting on communities of in the real economy in the supply chain network provide evidence of the channels of distress propagating from the financial sector to the real economy through the supply chain network.

Comovement and Instability in Cryptocurrency Markets
De Pace, Pierangelo,Rao, Jayant
We analyze the correlations of daily price returns for nine major cryptocurrencies between April 2013 and November 2018 and estimate their evolution using bivariate and multivariate modelling approaches. We detect pronounced time variation and find these correlations to be generally increasing between early 2017 and late 2018. We then adopt a right-tail variation of the Augmented Dickey-Fuller unit root test to identify and date-stamp periods of mildly explosive behavior (statistical instability) in the time series of the Network Value to Transactions (NVT) ratio (a measure of the dollar value of cryptocurrency transaction activity relative to its network value) of six cryptocurrencies. We show statistically significant evidence of mild explosiveness in all of them. At the end of 2017 and in 2018, several major cryptocurrencies experience significant (often simultaneous) instability associated with rising NVT ratios. Instability is a steady feature of cryptocurrency markets.

Corporate Disclosure and Capital Markets
Abdullazade, Zaur
Corporate disclosures are critical for functioning of capital markets demanding financial information. Firms provide disclosures through structured and regulated reports, including financial statements, disclosure notes, management recommendations, other compliance filings. In addition, firms voluntarily communicate their proforma financial statements and business data arising from forecasts or corporate reports prepared for investors. Lastly, there are certain disclosures demanded by financial and market intermediaries, industry experts and media.This paper introduces questions and empirical methods that are pertinent to capital markets research in accounting (hereinafter, CMRA). In section II, the literature review is discussed. Forces giving rise to demand for disclosures in modern capital-market economy, and the institutions increasing the credibility of disclosures are addressed. In the same section, I inform about the main research topics used for studying of financial reporting and disclosures associated with capital markets. I use the disclosure framework to identify research questions within each topic. Empirical evidence and research methodology related with CMRA are reviewed in section III. Section IV discusses reflections on constraints of studying financial reporting issues and their impact on capital markets. Previous studies in CMRA documented disclosures associated with stock performance, bid-ask spreads, transactions, cost of capital, analyst coverage, executive compensation and institutional ownership (Healy, P. et al. 2001). Also, section IV evaluates the previous studies falling short due to endogeneity and measurement error problems and complicating the interpretation of research findings. This paper will aid potential researchers in identification of many open, i.e. unanswered, questions in the CMRA field. Section V summarizes the main ideas, methodology and desired results for the research. Finally, section VI lists bibliography used for the references.

Do Foreign Direct Investments (FDI) Crowd In or Crowd Out Domestic Investment in Cote D’ivoire?
Oualy, Jean Michel Roy
The study is conducted to analyse the impact of foreign direct investments on domestic investment in Cote d’Ivoire. To this end, annual time series data for the period of 1975-2018 is used. The model is based on the theoretical model of Agosin and Mayer (2001).The study employs the differenced error correction model of Hendry (2006). The results reveal that the speed of adjustment of the variables towards their long-run equilibrium path is 16%. Foreign direct investments crowd out domestic investment in the long run and in the short run economic growth increase gross fixed capital formation. Human capital is significantly positive in the short and long run. Human capital increases investments in Cote d’Ivoire. Policy recommendation emanating from the study is that foreign direct investments is important when Cote d’Ivoire has already a national sector dynamic and well developed domestic companies.

Econophysics deserves a revamping
Paolo Magrassi

The paper argues that attracting more economists and adopting a more-precise definition of dynamic complexity might help econophysics acquire more attention in the economics community and bring new lymph to economic research. It may be necessary to concentrate less on the applications than on the basics of economic complexity, beginning with expansion and deepening of the study of small systems with few interacting components, while until thus far complexity has been assumed to be a prerogative of complicated systems only. It is possible that without a thorough analysis at that level, the understanding of systems that are at the same time complex and complicated will continue to elude economics and econophysics research altogether. To that purpose, the paper initiates and frames a definition of dynamic complexity grounded on the concept of non-linear dynamical system.

Effect of Increased Disclosure on Firm Financing: An Assessment of the Impact of Clause 49 in India
Pal, Sarmistha,Saher, Zoya
Using the 2000 introduction of the Clause 49 regulations in India as a natural experiment, we examine the impact of increased disclosure on firm financing choices. Clause 49 introduced transparency and disclosure rules, among others, and listed Indian firms were required to adopt it by 2006. Difference-in-difference estimates using firm-level panel data over 1996-2014 suggest that increased disclosure after 2000 has led to a greater (lower) reliance on equity (debt) among treated domestic listed (relative to cross-listed) sample Indian firms; but the effect was more pronounced after 2006 when most firms adopted the reform, even if partly. Cross-listed firms, however, remained largely unaffected by the reform, thus acted as our control group. We show that the reform improved earnings quality, reduced the information asymmetry and therefore lowered the cost of capital among most sample firms, except those affiliated to various business groups. These firms are more likely to be politically connected and continue to be opaque with greater reliance on debt, thus challenging the effective implementation of Clause49 regulations in a country with weak institutions.

Executive Compensation and Corporate Social Responsibility: Evidence from Chinese-listed SOEs
Li, Jiaxing,Shen , Jim Huangnan,Lee, Chien-Chiang
Based on annual data for Chinese-listed state-owned enterprises (SOEs) over the period 2013-2018, this research explores the effect of executive compensation on corporate social responsibility (CSR) performance. Our empirical results show that the executive compensation of SOEs has an inverted U-shaped effect on CSR fulfillment, and that the inverted U-shaped effect is robust to endogeneity corrections. Subsample analysis suggests that marginal contributions of executive compensation on CSR performance are different in the secondary industry and service industry. The ERG hierarchy of needs and the upper echelon theory help explain the non-linear effects. Based on the empirical results, this paper provides a new channel of unlocking the dynamics of CSR fulfillment.

Gated neural networks for implied volatility surfaces
Yu Zheng,Yongxin Yang,Bowei Chen

This paper presents a framework of developing neural networks to predict implied volatility surfaces. It can incorporate the related properties from existing mathematical models and empirical findings, including no static arbitrage, limiting boundaries, asymptotic slope and volatility smile. These properties are also satisfied empirically in our experiments with the option data on the S&P 500 index over 20 years. The developed neural network model outperforms the widely used surface stochastic volatility inspired (SSVI) model and other benchmarked neural network models on the mean average percentage error in both in-sample and out-of-sample datasets. This study has two major contributions. First, it contributes to the recent use of machine learning in finance, and an accurate deep learning implied volatility surface prediction model is obtained. Second, it provides the methodological guidance on how to seamlessly combine data-driven models with domain knowledge in the development of machine learning applications.

Hedging Home Equity Risk: Examination of a Nobel Idea
Sommervoll, Dag Einar,Swidler, Steve
Robert Shiller has long advocated the use of derivative real estate instruments to manage home equity risk. While his Nobel idea discusses the benefits to homeowners, to date, practical issues of hedging have been largely unexplored. Starting with the 2006 listing of real estate futures contracts on the Chicago Mercantile Exchange (CME), we examine transaction data from Las Vegas and consider several futures and index hedging strategies. The results indicate that idiosyncratic risk is large and renders hedging strategies ineffective for many homeowners that lost money on the sale of their house during the financial crisis. The set of results include certain holding periods where hedge payouts are only a small fraction of their home equity losses and still other times when an individual would lose both on their home sale and on their derivatives position. Thus, while home equity risk management is a Nobel idea, in practice, it is difficult to do.

Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?
Geoff Boeing,Max Besbris,Ariela Schachter,John Kuk

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.

Judicial Favoritism of Politicians: Evidence from Small Claims Court
Andre Assumpcao,Julio Trecenti

Multiple studies have documented racial, gender, political ideology, or ethnical biases in comparative judicial systems. Supplementing this literature, we investigate whether judges rule cases differently when one of the litigants is a politician. We suggest a theory of power collusion, according to which judges might use rulings to buy cooperation or threaten members of the other branches of government. We test this theory using a sample of small claims cases in the state of S\~ao Paulo, Brazil, where no collusion should exist. The results show a negative bias of 3.7 percentage points against litigant politicians, indicating that judges punish, rather than favor, politicians in court. This punishment in low-salience cases serves as a warning sign for politicians not to cross the judiciary when exercising checks and balances, suggesting yet another barrier to judicial independence in development settings.

On Calibration Neural Networks for extracting implied information from American options
Shuaiqiang Liu,Álvaro Leitao,Anastasia Borovykh,Cornelis W. Oosterlee

Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational costs needed to solve the corresponding mathematical problem many thousands of times. We will employ a data-driven machine learning approach to estimate the Black-Scholes implied volatility and the dividend yield for American options in a fast and robust way. To determine the implied volatility, the inverse function is approximated by an artificial neural network on the computational domain of interest, which decouples the offline (training) and online (prediction) phases and thus eliminates the need for an iterative process. For the implied dividend yield, we formulate the inverse problem as a calibration problem and determine simultaneously the implied volatility and dividend yield. For this, a generic and robust calibration framework, the Calibration Neural Network (CaNN), is introduced to estimate multiple parameters. It is shown that machine learning can be used as an efficient numerical technique to extract implied information from American options.

Optimal market making under partial information with general intensities
Diego Zabaljauregui,Luciano Campi

Starting from the Avellaneda-Stoikov framework, we consider a market maker who wants to optimally set bid/ask quotes over a finite time horizon, to maximize her expected utility. The intensities of the orders she receives depend not only on the spreads she quotes, but also on unobservable factors modelled by a hidden Markov chain. We tackle this stochastic control problem under partial information with a model that unifies and generalizes many existing ones under full information, combining several risk metrics and constraints, and using general decreasing intensity functionals. We use stochastic filtering, control and piecewise-deterministic Markov processes theory, to reduce the dimensionality of the problem and characterize the reduced value function as the unique continuous viscosity solution of its dynamic programming equation. We then solve the analogous full information problem and compare the results numerically through a concrete example. We show that the optimal full information spreads are biased when the exact market regime is unknown, and the market maker needs to adjust for additional regime uncertainty in terms of P&L sensitivity and observed order flow volatility. This effect becomes higher, the longer the waiting time in between orders.

Quasi-likelihood analysis for marked point processes and application to marked Hawkes processes
Simon Clinet

We develop a quasi-likelihood analysis procedure for a general class of multivariate marked point processes. As a by-product of the general method, we establish under stability and ergodicity conditions the local asymptotic normality of the quasi-log likelihood, along with the convergence of moments of quasi-likelihood and quasi-Bayesian estimators. To illustrate the general approach, we then turn our attention to a class of multivariate marked Hawkes processes with generalized exponential kernels, comprising among others the so-called Erlang kernels. We provide explicit conditions on the kernel functions and the mark dynamics under which a certain transformation of the original process is Markovian and $V$-geometrically ergodic. We finally prove that the latter result, which is of interest in its own right, constitutes the key ingredient to show that the generalized exponential Hawkes process falls under the scope of application of the quasi-likelihood analysis.

Resolving asset pricing puzzles with price impact
Xiao Chen,Jin Hyuk Choi,Kasper Larsen,Duane J. Seppi

We solve in closed-form an equilibrium model in which a finite number of exponential investors continuously consume and trade with price-impact. By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock-price volatility puzzle.

Revisiting integral functionals of geometric Brownian motion
Elena Boguslavskaya,Lioudmila Vostrikova

In this paper we revisit the integral functional of geometric Brownian motion $I_t= \int_0^t e^{-(\mu s +\sigma W_s)}ds$, where $\mu\in\mathbb{R}$, $\sigma > 0$, and $(W_s )_s>0$ is a standard Brownian motion. Specifically, we calculate the Laplace transform in $t$ of the cumulative distribution function and of the probability density function of this functional.

Structured climate financing: valuation of CDOs on inhomogeneous asset pools
N. Packham

Recently, a number of structured funds have emerged as public-private partnerships with the intent of promoting investment in renewable energy in emerging markets. These funds seek to attract institutional investors by tranching the asset pool and issuing senior notes with a high credit quality. Financing of renewable energy (RE) projects is achieved via two channels: small RE projects are financed indirectly through local banks that draw loans from the fund's assets, whereas large RE projects are directly financed from the fund. In a bottom-up Gaussian copula framework, we examine the diversification properties and RE exposure of the senior tranche. To this end, we introduce the LH++ model, which combines a homogeneous infinitely granular loan portfolio with a finite number of large loans. Using expected tranche percentage notional (which takes a similar role as the default probability of a loan), tranche prices and tranche sensitivities in RE loans, we analyse the risk profile of the senior tranche. We show how the mix of indirect and direct RE investments in the asset pool affects the sensitivity of the senior tranche to RE investments and how to balance a desired sensitivity with a target credit quality and target tranche size.

Supervised Machine Learning for Eliciting Individual Demand
John A. Clithero,Jae Joon Lee,Joshua Tasoff

Direct elicitation, guided by theory, is the standard method for eliciting individual-level latent variables. We present an alternative approach, supervised machine learning (SML), and apply it to measuring individual valuations for goods. We find that the approach is superior for predicting out-of-sample individual purchases relative to a canonical direct-elicitation approach, the Becker-DeGroot-Marschak (BDM) method. The BDM is imprecise and systematically biased by understating valuations. We characterize the performance of SML using a variety of estimation methods and data. The simulation results suggest that prices set by SML would increase revenue by 28% over the BDM, using the same data.