Research articles for the 2019-02-20

A Community Microgrid Architecture with an Internal Local Market
Bertrand Cornélusse,Iacopo Savelli,Simone Paoletti,Antonio Giannitrapani,Antonio Vicino
arXiv

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually.



Affine Stochastic Volatility Models: Supplementary Material
Dillschneider, Yannick
SSRN
Affine jump diffusion models in general and affine stochastic volatility models in particular are important modeling tools in finance. Their popularity resides in their exibility coupled with their analytical tractability, especially with respect to characteristic functions and polynomial moments. Within a generic affine jump diffusion model and a generic stochastic volatility model, nested in the former as a special case, this paper collects explicit expressions for various characteristic functions and polynomial moments.

Alchemy of Financial Innovation: Securitization, Liquidity and Optimal Monetary Policy
Yang, Jungu
SSRN
This paper provides a theoretical model to explain how securitization affects the overall liquidity and welfare of an economy, an under-discussed area in the literature. By applying an overlapping generations model with random-relocation shocks, the effects of securitization are analyzed in three different hypothetical situations: 1. only one region of the economy issues securities, 2. all regions issue securities with the same capital productivity, and 3. all regions issue securities, but capital productivity is disparate across regions. Asset securitization plays a role in supplying alternative liquid assets (fiat money). As the economy can invest its resources more efficiently in high-yielding illiquid assets (capital) due to securitization, both consumption and welfare increase overall. Optimal monetary policy follows the Friedman rule in cases 1. and 2. However, the rule does not apply in case 3.

An Exact Test of the Improvement of the Minimum Variance Portfolio
Glabadanidis, Paskalis
SSRN
I propose an exact finite sample test of the risk reduction of the global minimum variance (GMV) portfolio. The GMV test statistic has a straightforward geometric and portfolio interpretation and complements the celebrated GRS test in Gibbons, Ross and Shanken (1989). In practical applications, the GMV test leads to a rejection of the null hypothesis of no improvement in the GMV portfolio more often than the GRS test rejects the null hypothesis of no improvement in the risk-return profile of the tangent portfolio. The power of the GMV test increases with the variance reduction of the global minimum variance portfolio. Using test asset returns scaled by pre-determined instrumental variables is equivalent to increasing the overall number of test assets and leads to substantial power gains.

Asymmetric Learning from Prices and Post-Earnings-Announcement Drift
Choi, Jaewon,Le, Linh,Williams, Jared
SSRN
Motivated by research in psychology and experimental economics, we assume that investors update their beliefs about an asset's value upon observing the price, but only when the price clearly reveals that others obtained private information that differs from their own private information. Specifically, we assume that investors learn from the price of an asset in an asymmetric manner -- they learn from the price if they observe good (bad) private information and the price is worse (better) than what is justified based on public information alone. We show that asymmetric learning from an asset's price leads to post-earnings-announcement drift (PEAD), and that it generates arbitrage opportunities that are less attractive than alternative explanations of PEAD. In addition, our model predicts that PEAD will be concentrated in earnings surprises that are not dominated by accruals, and it also predicts that earnings response coefficients will decline in the magnitude of the earnings surprises.

Divestment may burst the carbon bubble if investors' beliefs tip to anticipating strong future climate policy
Birte Ewers,Jonathan F. Donges,Jobst Heitzig,Sonja Peterson
arXiv

To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestment from fossil fuels could play an important role in putting pressure on national governments on the road to decarbonization. Using a stochastic agent-based model of co-evolving financial market and investors' beliefs about future climate policy on an adaptive social network, here we find that the dynamics of divestment from fossil fuels shows potential for social tipping away from a fossil-fuel based economy. Our results further suggest that socially responsible investors have leverage: a small share of 10--20\,\% of such moral investors is sufficient to initiate the burst of the carbon bubble, consistent with the Pareto Principle. These findings demonstrate that divestment has potential for contributing to decarbonization alongside other social movements and policy instruments, particularly given the credible imminence of strong international climate policy. Our analysis also indicates the possible existence of a carbon bubble with potentially destabilizing effects to the economy.



Do Relationship Lenders Manage Loans Differently?
Keil, Jan
SSRN
Relationship banks manage loans differently than transaction lenders. This constitutes a new channel through which lending relationships affect borrowers. Analyzing 1.25 million observations with borrower-quarter and lender-quarter fixed effects, I show that loan amendments are 20% more likely for contracts signed by relationship lenders. Such formalized contract renegotiations are mostly beneficial for borrowers. To also investigate differences in informal administration policies, I analyze situations in which covenant violations reallocate control rights to creditors. Utilizing a regression discontinuity design and instrumenting lending relationships with geographical lender-borrower distances, I find reductions in investment and increases in firm exits only when lender-borrower relationships are absent.

Estimating Effects of Wind Loss Mitigation on Home Value
Awondo, Sebastain,Hollans, Harris,Powell, Lawrence,Wade, Chip
SSRN
We employ a novel spatial regression model to estimate effects of windstorm loss mitigation features on the value of coastal homes. Specifically, we consider joint effects of the Insurance Institute for Business and Home Safety’s (IBHS) FORTIFIED HOMETM . designation (henceforth Fortified) and distance from the coast on the prices paid for houses. We find that on average homebuyers pay a seven-percent premium for Fortified homes, which exceeds the typical cost of building or retrofitting homes to the Fortified standard. In addition, we find that Fortified construction and distance from the coast are substitutes. The premium is highest for homes nearest the coast.

Financial Crises and e-Commerce: How Are They Related
Thalassinos, Eleftherios Ioannis,Thalassinos, Yannis
SSRN
The recent financial crisis unveiled the major deficiencies and weaknesses of the Eurozone structure. However, almost 10 years after the beginning of the crisis, the Eurozone is still dealing with its effects. E-Commerce, as an innovative way to do business, has been affected negatively since it is based mainly on credit. The article discusses some of the reasons of the global crises since the 1980’s and focuses on the role of the Credit Rating Agencies (“CRAs”) during the recent financial crisis. It presents the methodologies that are used in order to assess country risk, the relevant variables used in their evaluations, the problems they face and suggests possible ways to improve the process at a European level. At the same time, it discusses how the low country grade given by the CRAs redirects business activities in countries with higher grades. The article is organized in six sections as follows: the concept of country risk, platforms for assessing country risk, the determinants of country risk, the reasons behind the recent financial crises, the effect of a low grade to e-commerce and the role of CRAs in the latest financial crisis.

Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Ziniu Hu,Weiqing Liu,Jiang Bian,Xuanzhe Liu,Tie-Yan Liu
arXiv

Stock trend prediction plays a critical role in seeking maximized profit from stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of stock market. Exploding information on Internet together with advancing development of natural language processing and text mining techniques have enable investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness and comprehensiveness of online content related to stock market varies drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our approach.



Market Impact: A Systematic Study of the High Frequency Options Market
Emilio Said,Ahmed Bel Hadj Ayed,Damien Thillou,Jean-Jacques Rabeyrin,Frédéric Abergel
arXiv

This paper deals with a fundamental subject that has seldom been addressed in recent years, that of market impact in the options market. Our analysis is based on a proprietary database of metaorders-large orders that are split into smaller pieces before being sent to the market on one of the main Asian markets. In line with our previous work on the equity market [Said et al., 2018], we propose an algorithmic approach to identify metaorders, based on some implied volatility parameters, the at the money forward volatility and at the money forward skew. In both cases, we obtain results similar to the now well understood equity market: Square-root law, Fair Pricing Condition and Market Impact Dynamics.



Matching Refugees to Host Country Locations Based on Preferences and Outcomes
Avidit Acharya,Kirk Bansak,Jens Hainmueller
arXiv

Facilitating the integration of refugees has become a major policy challenge in many host countries in the context of the global displacement crisis. One of the first policy decisions host countries make in the resettlement process is the assignment of refugees to locations within the country. We develop a mechanism to match refugees to locations in a way that takes into account their expected integration outcomes and their preferences over where to be settled. Our proposal is based on a priority mechanism that allows the government first to specify a threshold g for the minimum level of expected integration success that should be achieved. Refugees are then matched to locations based on their preferences subject to meeting the government's specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the government's threshold. We demonstrate our approach using simulations and a real-world application to refugee data from the United States.



Portfolio Optimization and Model Predictive Control: A Kinetic Approach
Torsten Trimborn,Lorenzo Pareschi,Martin Frank
arXiv

In this paper, we introduce a large system of interacting financial agents in which each agent is faced with the decision of how to allocate his capital between a risky stock or a risk-less bond. The investment decision of investors, derived through an optimization, drives the stock price. The model has been inspired by the econophysical Levy-Levy-Solomon model (Economics Letters, 45). The goal of this work is to gain insights into the stock price and wealth distribution. We especially want to discover the causes for the appearance of power-laws in financial data. We follow a kinetic approach similar to (D. Maldarella, L. Pareschi, Physica A, 391) and derive the mean field limit of our microscopic agent dynamics. The novelty in our approach is that the financial agents apply model predictive control (MPC) to approximate and solve the optimization of their utility function. Interestingly, the MPC approach gives a mathematical connection between the two opponent economic concepts of modeling financial agents to be rational or boundedly rational. Furthermore, this is to our knowledge the first kinetic portfolio model which considers a wealth and stock price distribution simultaneously. Due to our kinetic approach, we can study the wealth and price distribution on a mesoscopic level. The wealth distribution is characterized by a lognormal law. For the stock price distribution, we can either observe a lognormal behavior in the case of long-term investors or a power-law in the case of high-frequency trader. Furthermore, the stock return data exhibits a fat-tail, which is a well known characteristic of real financial data.



Prediction of Realized Volatility Based on Realized-GARCH-Kernel Model: The Comparison of CHINA and US
Wang, Jiazhen,Jiang, Yuexiang,Zhu, Yanjian,Yu, Jing
SSRN
We propose a Realized-GARCH-Kernel model to predict realized volatilities of 50 ETF in China and S&P500 index in U.S..The Kernel density fitting on disturbance term and semi-parametric method make our model perform well both statistically and economically. First, our model has the lowest in- and out-of-sample prediction errors among five comparable prediction models. The result is robust in eight measures of realized volatility. Second, in both China and U.S. markets, straddle option trading strategies with volatilities predicted with our model generate larger monthly profit and greater Sharpe ratio. Our model is useful in practical investment.

Remittances, Finance and Industrialisation in Africa
Efobi, Uchenna,Asongu, Simplice,Okafor, Chinelo,S. Tchamyou, Vanessa,Tanankem, Belmondo
SSRN
The paper assesses how remittances directly and indirectly affect industrialisation using a panel of 49 African countries for the period 1980-2014. The indirect impact is assessed through financial development channels. The empirical evidence is based on three interactive and non-interactive simultaneity-robust estimation techniques, namely: (i) Instrumental Fixed Effects (FE) to control for the unobserved heterogeneity; (ii) Generalised Method of Moments (GMM) to control for persistence in industrialisation and (iii) Instrumental Quantile Regressions (QR) to account for initial levels of industrialisation. The non-interactive specification elucidates direct effects of remittances on industrialisation whereas interactive specifications explain indirect impacts. The findings broadly show that for certain initial levels of industrialisation, remittances can drive industrialisation through the financial development mechanism. Policy implications are discussed.

Robust Asset Allocation for Robo-Advisors
Thibault Bourgeron,Edmond Lezmi,Thierry Roncalli
arXiv

In the last few years, the financial advisory industry has been impacted by the emergence of digitalization and robo-advisors. This phenomenon affects major financial services, including wealth management, employee savings plans, asset managers, etc. Since the robo-advisory model is in its early stages, we estimate that robo-advisors will help to manage around $1 trillion of assets in 2020 (OECD, 2017). And this trend is not going to stop with future generations, who will live in a technology-driven and social media-based world. In the investment industry, robo-advisors face different challenges: client profiling, customization, asset pooling, liability constraints, etc. In its primary sense, robo-advisory is a term for defining automated portfolio management. This includes automated trading and rebalancing, but also automated portfolio allocation. And this last issue is certainly the most important challenge for robo-advisory over the next five years. Today, in many robo-advisors, asset allocation is rather human-based and very far from being computer-based. The reason is that portfolio optimization is a very difficult task, and can lead to optimized mathematical solutions that are not optimal from a financial point of view (Michaud, 1989). The big challenge for robo-advisors is therefore to be able to optimize and rebalance hundreds of optimal portfolios without human intervention. In this paper, we show that the mean-variance optimization approach is mainly driven by arbitrage factors that are related to the concept of hedging portfolios. This is why regularization and sparsity are necessary to define robust asset allocation. However, this mathematical framework is more complex and requires understanding how norm penalties impacts portfolio optimization. From a numerical point of view, it also requires the implementation of non-traditional algorithms based on ADMM methods.



Tail Risk Management for Multi-Asset Multi-Factor Strategies
Chambers, David,Lohre, Harald,Rother, Carsten
SSRN
Multi-asset multi-factor portfolio allocation is typically centred around a risk-based allocation paradigm, often striving for maintaining equal volatility risk budgets. Given that the common factor ingredients can be highly skewed, we specifically incorporate the notion of tail risk management into the construction of multi-asset multi-factor portfolios. Indeed, we find that the minimum CVaR concentration approach of Boudt, Carl and Peterson (2013) effectively mitigates the dangers of tail risk concentrations. Yet, diversifying across multiple assets and style factors can be in and of itself a good means of tail risk management, irrespective of the risk-based allocation technique employed.

The Geography of FinTech
Choi, Hyun-Soo,Loh, Roger
SSRN
Banking services can now be delivered with technology (FinTech) and many banks are downsizing their physical operations. Bank customers are also relying less on physical locations because of FinTech-enabled banking. Is geography indeed now less important for banks? Using quasi-exogenous closures of ATMs in a densely populated city, we examine how changes in ATM access affects FinTech adoption. We find that after closures, affected customers' travel distance to ATMs increases. This induces them to increase their usage of the bank's digital platform. Further, using closures as an instrument for FinTech adoption, we find that adopters become less likely to incur minimum-balance penalties. Our results show that very slight frictions to geography can have a significant impact on the rate of FinTech adoption and financial inclusion.

Turkish Lira â€" A Fiat Currency that Resembles the Volatility of Cryptocurrencies: The Effects of Exchange Rate Volatility on Turkish Economy
Taskinsoy, John
SSRN
The dollar’s dominance as the world’s reserve currency was inaugurated at the 1944 Bretton Woods conference where the agreement was signed by the 44 wartime allies, but the dollar’s hegemony was solidified in 1971 when US President Nixon cut the dollar’s link to gold. True, the fixed exchange rate regime reminiscence of the Bretton Woods is no longer with us; and also true that the two watchdog institutions created in 1944 are still around permitting and encouraging but almost compelling world inflation. We are now faced with a dilemma that begs for a solution; the Bretton Woods system and its successor the dollar regime after 1971 created a system of economic slavery in which all countries are serving to keep the United States of America happy. The inevitable birth of Bitcoin is the upshot of this dilemma and the most recent devaluation of the Turkish lira is an unescapable consequence that unmistakably demonstrated exactly what happens when the U.S. demands are not satisfied. The U.S. dollar continues to rule both international trade and foreign exchange markets; today, circa 90% of daily forex trading volume is dollar denominated plus half of the world’s 185 currencies are in a tight trading range to the dollar. Since the late 1990s, the United States’ growing abuse of sanction power via the dollar tool has reemerged the interest in search for a viable alternative to the U.S. dollar.