nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒07‒24
28 papers chosen by

  1. Model-Free Market Risk Hedging Using Crowding Networks By Vadim Zlotnikov; Jiayu Liu; Igor Halperin; Fei He; Lisa Huang
  2. The Empirical Performance of Option Implied Volatility Surface-Driven Optimal Portfolios By Massimo Guidolin; Kai Wang
  3. Optimal Insurance: Dual Utility, Random Losses and Adverse Selection By Alex Gershkov; Benny Moldovanu; Philipp Strack; Mengxi Zhang
  4. Analisis Penerapan Manajemen Risiko Kredit Pada PT. Bank Nagari Cabang Solok By Isnaini, Ade; Afriyeni, Afriyeni
  5. Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification By Cassidy K. Buhler; Hande Y. Benson
  6. Loss Aversion, Risk Aversion, and the Shape of the Probability Weighting Function By Matthew D. Rablen
  7. The impact of COVID-19 induced panic on the return and volatility of precious metals By Zaghum Umar; Saqib Aziz; Dima Tawil
  8. Uninsured Deposits: Relevance and Evolutions Over Time By Van Roosebeke, Bert; Defina, Ryan; Wahyuni, Tri
  9. Risk Management Strategy of Food Safety: The Case of the US Fresh Produce Supply Chain By Yan, Minhao; Ge, Houtian; Gomez, Miguel I.
  10. Strong vs. Stable: The Impact of ESG Ratings Momentum and their Volatility on the Cost of Equity Capital By Ian Berk; Massimo Guidolin; Monia Magnani
  11. Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values By Falco J. Bargagli-Stoffi; Fabio Incerti; Massimo Riccaboni; Armando Rungi
  12. Using Deep Learning to Hedge Rainbow Options By Thibault Collin
  13. The emerging governance system for the Moroccan insurance sector By Chaymaa Reghay; Otheman Nouisser; Said Radi
  14. Internal Ratings, Non-Performing Loans, and Bank Opacity: Evidence from Analysts’ Forecasts By Brunella Bruno; Immacolata Marino; Giacomo Nocera
  15. Interactions Issues in Risk Analysis of Complex Systems By Ayeley Tchangani
  16. Aggregate Insider Trading and Stock Market Volatility in the UK By Guglielmo Maria Caporale; Kyriacos Kyriacou; Nicola Spagnolo
  17. Conclusions of the Third European Conference on Risk Perception, Behaviour, Management and Response – ECRP22 By Samuel Rufat; Karsten Uhing; Maike Vollmer; Alexander Fekete; Giacomo Bianchi; Christian Kuhlicke
  18. Bank Funding, SME lending and Risk Taking By Sander Lammers; Massimo Giuliodori; Robert Schmitz; Adam Elbourne
  19. Deep calibration with random grids By Fabio Baschetti; Giacomo Bormetti; Pietro Rossi
  20. A Game of Competition for Risk By Louis Abraham
  21. An experimental investigation of social risk preferences for health By Arthur E. Attema; Olivier L'Haridon; Gijs van de Kuilen
  22. The transmission of macroprudential policy in the tails: evidence from a narrative approach By Fernández-Gallardo, Álvaro; Lloyd, Simon; Manuel, Ed
  23. Moral Hazard in Agricultural Insurance – Evidence from A Non-Voluntary Sow Insurance Program in China By Rao, Xudong; Cai, Qingyin; Zhang, Yuehua
  24. How Heterogeneous Beliefs Trigger Financial Crises By Florian Schuster; Marco Wysietzki; Jonas Zdrzalek
  25. An Explained Extreme Gradient Boosting Approach for Identifying the Time-Varying Determinants of Sovereign Risk By Iader Giraldo; Carlos Giraldo; Jose E. Gomez-Gonzalez; Jorge M. Uribe
  26. High-frequency trading in the stock market and the costs of option market making By Nimalendran, Mahendrarajah; Rzayev, Khaladdin; Sagade, Satchit
  27. Exact Likelihood for Inverse Gamma Stochastic Volatility Models By Roberto Leon-Gonzalez; Blessings Majoni
  28. The Dominant Currency Financing Channel of External Adjustment By Camila Casas; Sergii Meleshchuk; Yannick Timmer

  1. By: Vadim Zlotnikov; Jiayu Liu; Igor Halperin; Fei He; Lisa Huang
    Abstract: Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this paper, we analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long-short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long-short portfolios provide protection for both small and large market price fluctuations, due to their negative correlation with the market and positive convexity as a function of market returns. By adding our long-short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, our method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio.
    Date: 2023–06
  2. By: Massimo Guidolin; Kai Wang
    Abstract: We apply a two-step strategy to forecast the dynamics of the volatility surface implicit in option prices to all American-style options written on the stocks that have entered the Dow Jones Industrial Average Index between 2004 and 2016. We explore whether the implied volatilities extracted through the two-step approach help improve the out of-sample performance of minimum-variance portfolios. We find that, by using option-implied volatilities in estimating the covariance matrix, the ex-post volatility of the minimum-variance portfolio is lower when compared with the equal-weighted portfolio and a minimum-variance portfolio simply derived from the historical, sample covariance matrix estimator. Moreover, over most of our 13-year sample, the realized Sharpe, Sortino and information ratios increase when the sample covariance matrix estimator is replaced with its implied counterpart. However, the benefits of using option implied information are countered by an increase in portfolio turnover that may imply higher (implicit) transaction costs. We also apply shrinkage methods to both the sample covariance estimator and the implied covariance estimator and note that they often lead to significant improvements in portfolio performance.
    Keywords: Equity options, Implied volatility surface, Predictability, optimal portfolios
    JEL: G11 G17 G13
    Date: 2022
  3. By: Alex Gershkov (Department of Economics and the Federmann Center for the Study of Rationality, The Hebrew University of Jerusalem, and School of Economics, University of Surrey); Benny Moldovanu (Department of Economics, University of Bonn); Philipp Strack (Department of Economics, Yale University, New Haven); Mengxi Zhang (Departmentof Economics, University of Bonn)
    Abstract: We study a generalization of the classical monopoly insurance problem under adverse selection (see Stiglitz [1977]) where we allow for a random distribution of losses, possibly correlated with the agent’s risk parameter that is private information. Our model explains patterns of observed customer behavior and predicts insurance contracts most often observed in practice: these consist of menus of several deductible-premium pairs, or menus of insurance with coverage limits-premium pairs. A main departure from the classical insurance literature is obtained here by endowing the agents with risk-averse preferences that can be represented by a dual utility functional (Yaari [1987]).
    Date: 2023–06
  4. By: Isnaini, Ade; Afriyeni, Afriyeni
    Abstract: The purpose of this research is to find out how the application of credit risk management at PT. Bank Nagari Cabang Solok. The method used in research is the method of analysis and data collection in research. In analyzing the data, it is done by examining the spaciousness data and library materials. The nature of the research used is descriptive research which explains in detail the Application of Credit Risk Management at PT. Bank Nagari Cabang Solok with a qualitative approach. The results of this study indicate that PT. Bank Nagari Cabang Solok has implemented the 5C principles, namely character, capacity, capital, collateral and condition of economy which is marked by a decrease in the level of bad loans from 2019 to 2022.
    Date: 2023–05–09
  5. By: Cassidy K. Buhler; Hande Y. Benson
    Abstract: The Markowitz mean-variance portfolio optimization model aims to balance expected return and risk when investing. However, there is a significant limitation when solving large portfolio optimization problems efficiently: the large and dense covariance matrix. Since portfolio performance can be potentially improved by considering a wider range of investments, it is imperative to be able to solve large portfolio optimization problems efficiently, typically in microseconds. We propose dimension reduction and increased sparsity as remedies for the covariance matrix. The size reduction is based on predictions from machine learning techniques and the solution to a linear programming problem. We find that using the efficient frontier from the linear formulation is much better at predicting the assets on the Markowitz efficient frontier, compared to the predictions from neural networks. Reducing the covariance matrix based on these predictions decreases both runtime and total iterations. We also present a technique to sparsify the covariance matrix such that it preserves positive semi-definiteness, which improves runtime per iteration. The methods we discuss all achieved similar portfolio expected risk and return as we would obtain from a full dense covariance matrix but with improved optimizer performance.
    Date: 2023–06
  6. By: Matthew D. Rablen
    Abstract: Loss aversion, risk aversion, and the probability weighting function (PWF) are three central concepts in explaining decisionmaking under risk. I examine interlinkages between these concepts in a model of decisionmaking that allows for loss averse/tolerant stochastic reference dependence and optimism/pessimism over probability distributions. I give a preference interpretation to commonly observed shapes of PWF and to risk aversion. In particular, I establish a connection between loss aversion and both risk aversion and the inverse-S PWF: loss aversion is a necessary condition to observe each of these phenomena. The results extend to distinct PWFs in the gain and loss domains, as under prospect theory.
    Keywords: probability weighting, rank dependent expected utility, loss aversion, risk aversion, reference dependence, optimism, pessimism, prospect theory
    JEL: D91 D81 D01
    Date: 2023
  7. By: Zaghum Umar (Zayed University); Saqib Aziz (ESC [Rennes] - ESC Rennes School of Business); Dima Tawil (ESC [Rennes] - ESC Rennes School of Business)
    Abstract: We use TVP-VAR approach to analyze the connectedness between the COVID-19 induced global panic index (GPI) and precious metals return and volatility. We find evidence of positive connectedness between the GPI and precious metals with GPI being a shock transmitter and precious metals, especially gold, being net receivers. While silver shows the highest resistance to shocks, platinum and palladium present a time varying transmission pattern. Our results refute the safe-haven property of precious metals during the COVID-19 outbreak, with the exception of silver.
    Keywords: COVID-19, Global panic index, Precious metals, TVP-VAR method
    Date: 2021–09
  8. By: Van Roosebeke, Bert; Defina, Ryan; Wahyuni, Tri
    Abstract: This global stock-taking of deposit insurance coverage ratios may offer a good first proxy for estimating the relevance of uninsured deposits. However, it is important to realise that the IADI Core Principles for Effective Deposit Insurance Systems (Core Principles) as global standards for deposit insurance focus on aggregated coverage ratios. Despite overall compliance with these standards by a jurisdiction, the share of deposits not covered by deposit insurance at a number of individual banks within this jurisdiction may be very high, which may give rise to increased bank-run risks. Deposit insurance standards currently do not explicitly account for this risk, which is implicitly assumed to be dealt with by prudential regulation and/or supervision. Ongoing research may offer further insights. Building on this stock-taking exercise, ongoing research will look into measures applied across the globe by deposit insurers that may help manage the risk of sudden and massive withdrawals by uninsured depositors. These will include the use of differentiated coverage levels, including temporary high balances, high and/or blanket coverage for payment and settlement accounts and voluntary top-up coverage levels.
    Keywords: deposit insurance; bank resolution
    JEL: G21 G33
    Date: 2023–06–12
  9. By: Yan, Minhao; Ge, Houtian; Gomez, Miguel I.
    Keywords: Food Consumption/Nutrition/Food Safety, Agribusiness, Agricultural and Food Policy
    Date: 2023
  10. By: Ian Berk; Massimo Guidolin; Monia Magnani
    Abstract: We test the performance of two ESG score-driven quantitative signals on a large, multi-national crosssection of European stock returns. In particular, we ask whether in the cross-section, the cost of equity capital is more strongly affected by the (upward) “slope” (identified as momentum over a period of time) of their ESG scores or by their “stability” (identified as the volatility of the scores over a period of time), measured around a given slope. We find that 1 month, short-term ESG momentum is priced in the cross-section of stock returns and that it lowers the ex-ante cost of capital (at the same time causing realised ex post average abnormal returns). Short-term ESG momentum may represent a novel, priced systematic risk factor. There is equally strong evidence that a ESG spread strategy that buys (sells) low (high) ESG score volatility stocks leads to a significant alpha and alters the ex-ante cost of capital. Both quantitative ESG signals lead to portfolio sorts and long-short strategies that increase the speed of improvement of the aggregate sustainability profile of the resulting portfolios with no costs in terms of average ESG scores or their stability
    Keywords: ESG ratings, ESG momentum, ESG score volatility, cross-sectional pricing, systematic risk factor.
    JEL: G11 G12 C59 G24
    Date: 2023
  11. By: Falco J. Bargagli-Stoffi; Fabio Incerti; Massimo Riccaboni; Armando Rungi
    Abstract: In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and non-random missing values of 304, 906 firms active in Italy from 2008 to 2017. Then, we spot the highest financial distress conditional on predictions that lies above a threshold for which a combination of false positive rate (false prediction of firm failure) and false negative rate (false prediction of active firms) is minimized. Therefore, we identify zombies as firms that persist in a state of financial distress, i.e., their forecasts fall into the risk category above the threshold for at least three consecutive years. For our purpose, we implement a gradient boosting algorithm (XGBoost) that exploits information about missing values. The inclusion of missing values in our predictive model is crucial because patterns of undisclosed accounts are correlated with firm failure. Finally, we show that our preferred machine learning algorithm outperforms (i) proxy models such as Z-scores and the Distance-to-Default, (ii) traditional econometric methods, and (iii) other widely used machine learning techniques. We provide evidence that zombies are on average less productive and smaller, and that they tend to increase in times of crisis. Finally, we argue that our application can help financial institutions and public authorities design evidence-based policies-e.g., optimal bankruptcy laws and information disclosure policies.
    Date: 2023–06
  12. By: Thibault Collin (Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres)
    Abstract: The general scope of this thesis will be to further study the application of artificial neural networks in the context of hedging rainbow options. Due to their inherently complex features, such as the correlated paths that the prices of their underlying assets take or their absence from traded markets, finding an optimal hedging strategy for rainbow options is difficult, and traders usually have to resort to models and methods they know are inaccurate. An alternative approach involving deep learning however recently surfaced in the context of hedging vanilla options [6], and researchers have started to see potential in the use of neural networks for options endowed with exotic features in [5], [12] and [22]. The key to a near-perfect hedge for contingent claims might be hidden behind the training of neural network algorithms [6], and the scope of this research will be to further investigate how those innovative hedging techniques can be extended to rainbow options [22], using recent research [21], and to compare our results with those proposed by the current models and techniques used by traders, such as running Monte-Carlo path simulations. In order to accomplish that, we will try to develop an algorithm capable of designing an innovative and optimal hedging strategy for rainbow options using some intuition developed to hedge vanilla options [21] and price exotics [5]. But although it was shown from past literature to be potentially efficient and cost-effective, the opaque nature of an artificial neural network will make it difficult for the deep learning algorithm to be fully trusted and used as a sole method for hedging purposes, but rather as an additional technique associated with other more reliable models.
    Keywords: Quantitative finance, deep hedging, deep learning, machine learning, rainbow options, call options, call worst-of options, black scholes, geometric brownian motion
    Date: 2023–06–04
  13. By: Chaymaa Reghay (laboratoire de recherche en sciences de gestion des organisations - ENCG Kenitra); Otheman Nouisser (laboratoire de recherche en sciences de gestion des organisations - ENCG Kenitra); Said Radi (Autorité de Contrôle des Assurances et de la Prévoyance Sociale)
    Abstract: Following the example of the European regulatory reform Solvency II, the Moroccan insurance sector has been engaged for a few years in a large-scale project called "Risk-Based Solvency". From now on, it takes a new turn since it becomes topical; for good reason: it represents a real challenge in particular in comparison with the current prudential framework, until then in force. The project, which was drawn up in 2017 and is due to be implemented in 2025, is preparing the sector for a major change in its regulation. For the time being, the implementation work is still in progress and the new circular is practically stabilized. Three sets of regulatory requirements will be introduced: quantitative, qualitative and financial reporting. In this paper, we will focus on the second set of requirements, which concerns the organization of insurance and reinsurance undertakings (EAR) and aims at the implementation of four key functions: risk management, audit, compliance and actuarial; in this case the internal risk and solvency assessment (ORSA). The implementation of these regulatory requirements should enable companies to ensure that they are well managed, sufficiently capitalized and able to justify their solvency at any time. This article is a systematic literature review. The objective is to provide a theoretical contribution to this subject in the form of a presentation of the new qualitative aspects of the directive, firstly by providing an overview of Pillar 2 and then by analyzing the interdependence of the key functions relating to risk management and compliance.
    Abstract: À l'instar de la réforme réglementaire européenne Solvabilité II, le secteur marocain des assurances a entamé depuis quelques années un projet d'envergure dit « Solvabilité Basée sur les Risques ». Désormais, il prend une nouvelle tournure puisqu'il devient d'actualité ; pour cause : il représente un réel défi notamment en comparaison avec le cadre prudentiel actuel, jusqu'alors en vigueur. Ce projet, élaboré en 2017 et dont la mise en œuvre effective est prévue pour 2025, prépare le secteur à un changement majeur au niveau de sa réglementation. Pour l'heure, les travaux de mise en place poursuivent leur cheminement et la nouvelle circulaire est pratiquement stabilisée. Trois séries d'exigences réglementaires seront introduites : quantitatives, qualitatives et communication financière. Dans ce papier, nous allons porter un regard sur la deuxième série d'exigences, qui touche à l'organisation même des entreprises d'assurances et de réassurance (EAR) et qui vise la mise en place de quatre fonctions clés : gestion des risques, audit, conformité et actuariat ; en l'occurrence l'évaluation interne des risques et de la solvabilité (ORSA). La mise en application de ces exigences réglementaires devra permettre aux entreprises de s'assurer qu'elles sont bien gérées, suffisamment capitalisées et en mesure de justifier à tout moment leur solvabilité. Cet article est une revue de littérature systématique. L'objectif est d'apporter une contribution théorique sur ce sujet sous la forme d'une présentation des nouveaux aspects qualitatifs de la directive en explicitant, dans un premier temps, une vue d'ensemble sur le Pilier 2, puis en analysant l'interdépendance des fonctions clés relatives à la gestion des risques et à la conformité.
    Keywords: Insurance, Risk-based solvency, Governance system, Key functions, Assurance, Solvabilité basée sur les risques, Système de gouvernance, Fonctions clés
    Date: 2023–06–10
  14. By: Brunella Bruno; Immacolata Marino; Giacomo Nocera
    Abstract: We use a panel data set of large listed European banks to evaluate the effect of the usage of internal ratings-based (IRB) models on bank opacity. We find that a more intensive implementation of these models is associated with lower absolute forecast error and disagreement among analysts about bank earnings per share. The results are stronger in banks adopting the advanced version of IRB models. In these banks the negative effect of non-performing loans on bank transparency is mitigated. We deal with concerns regarding omitted variables and reverse causality using an instrumental variables approach. Our results are driven by the more in-depth disclosure of the credit risk exposures that follows the adoption of IRB models.
    Keywords: Bank regulation, Basel II, risk-weighted assets, transparency
    JEL: G20 G21 G28
    Date: 2023
  15. By: Ayeley Tchangani (LGP - Laboratoire Génie de Production - ENIT - Ecole Nationale d'Ingénieurs de Tarbes - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse)
    Keywords: Risk analysis, Complex systems, Interactions
    Date: 2023–06–07
  16. By: Guglielmo Maria Caporale; Kyriacos Kyriacou; Nicola Spagnolo
    Abstract: This paper examines the relationship between aggregate insider trading (AIT) and stock market volatility using monthly data on insider transactions by UK executives in public limited companies for the period January 2002 - December 2020. More specifically, a Vector Autoregression (VAR) model is estimated and Impulse Response analysis as well as Forecast Error Variance Decomposition are carried out. The main finding is that higher AIT (more specifically, insider purchases) leads to a short-run increase in stock market volatility; this can be attributed to a combination of insiders manipulating the timing and content of the information they release and the revelation of new economy-wide information to the market. The UK being a well-regulated market, it is plausible that the main driver of the increase in stock market volatility should be the information effect. These results are shown to be robust to using alternative (direct) measures of AIT.
    Keywords: aggregate insider trading, stock market volatility, VAR, impulse responses
    JEL: C22 G14
    Date: 2023
  17. By: Samuel Rufat (CY - CY Cergy Paris Université, IUF - Institut Universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche); Karsten Uhing (Fraunhofer IML - Fraunhofer Institute for Material Flow and Logistics - Fraunhofer-Gesellschaft - Fraunhofer); Maike Vollmer (Fraunhofer INT - Fraunhofer Institute for Technological Trend Analysis - Fraunhofer-Gesellschaft - Fraunhofer); Alexander Fekete (THK - Institute of Rescue Engineering and Civil Protection, University of Applied Sciences Cologne); Giacomo Bianchi (EOS - European Organisation for Security); Christian Kuhlicke (UFZ - Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research)
    Abstract: The Third ECRP conference in June 2022 in Berlin, Germany, has gathered again our two communities, the Risk Perception and Behaviour Survey of Surveyors (Risk-SoS) and the H2020-DRS-01 Cluster on risk perception and adaptive behaviour (a grouping of several Horizon Europe – Disaster Resilient Societies projects, most notably RESILOC, BUILDERS, CORE, ENGAGE, LINKS, RiskPACC). One of the key challenges for risk, vulnerability and resilience research is how to address the role of risk perceptions and how perceptions influence behaviour. It remains unclear why people fail to act adaptively to reduce future losses, even when there is ever-richer information available on natural and human-made hazards (flood, drought, etc.). The current fragmentation of the field makes it an uphill battle to cross-validate the results of existing independent case studies. This, in turn, hinders comparability and transferability across scales and contexts and hampers recommendations for policy and risk management. The ECRP conference cycle aims to contribute to improve the ability of researchers in the field to work together and build cumulative knowledge.
    Keywords: mitigation, disaster, response, Disaster management, Hazard, risk, risk perception, behaviour, adaptation
    Date: 2022
  18. By: Sander Lammers (CPB Netherlands Bureau for Economic Policy Analysis); Massimo Giuliodori (UVA); Robert Schmitz; Adam Elbourne (CPB Netherlands Bureau for Economic Policy Analysis)
    Abstract: European companies heavily rely on bank credit to finance their operations and investments. Therefore, it is crucial for banks to take risks on corporate loans, although excessive risk-taking can have negative consequences for banks. There are indications in the literature that the financing structure used by banks plays a role in determining the risks they take. However, the economic literature does not provide clear consensus on how different elements of a bank's financing structure are related to risk. In this exploratory study, we investigated this relationship specifically focusing on corporate loans. This contributes to a better understanding of which companies receive funding and how a bank's financing structure itself can become a risk, particularly when riskier companies face bankruptcy. The financing structure of banks primarily consists of equity (capital buffer), deposits (savings from households and businesses), market financing (funds raised from international investors), and interbank loans (loans between banks, including central bank loans). We analyzed the extent to which these individual financing elements contribute to the risks banks take on loans to companies.
    JEL: G21 G32 E52
    Date: 2023–07
  19. By: Fabio Baschetti (Scuola Normale Superiore); Giacomo Bormetti (University of Bologna); Pietro Rossi (University of Bologna; Prometeia S.p.A)
    Abstract: We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. (2021) with the pointwise two-stage calibration of Bayer and Stemper (2018). Our methodology inherits robustness from the former while not suffering from the need for interpolation/extrapolation techniques, a clear advantage ensured by the pointwise approach. The crucial point to the entire procedure is the generation of implied volatility surfaces on random grids, which one dispenses to the network in the training phase. We support the validity of our calibration technique with several empirical and Monte Carlo experiments for the rough Bergomi and Heston models under a simple but effective parametrization of the forward variance curve. The approach paves the way for valuable applications in financial engineering - for instance, pricing under local stochastic volatility models - and extensions to the fast-growing field of path-dependent volatility models.
    Date: 2023–06
  20. By: Louis Abraham (UP1 - Université Paris 1 Panthéon-Sorbonne, PRISM Sorbonne - Pôle de recherche interdisciplinaire en sciences du management - UP1 - Université Paris 1 Panthéon-Sorbonne)
    Abstract: In this study, we present models where participants strategically select their risk levels and earn corresponding rewards, mirroring real-world competition across various sectors. Our analysis starts with a normal form game involving two players in a continuous action space, confirming the existence and uniqueness of a Nash equilibrium and providing an analytical solution. We then extend this analysis to multi-player scenarios, introducing a new numerical algorithm for its calculation. A key novelty of our work lies in using regret minimization algorithms to solve continuous games through discretization. This groundbreaking approach enables us to incorporate additional real-world factors like market frictions and risk correlations among firms. We also experimentally validate that the Nash equilibrium in our model also serves as a correlated equilibrium. Our findings illuminate how market frictions and risk correlations affect strategic risk-taking. We also explore how policy measures can impact risk-taking and its associated rewards, with our model providing broader applicability than the Diamond-Dybvig framework. We make our methodology and code open-source 1. Finally, we contribute methodologically by advocating the use of algorithms in economics, shifting focus from finite games to games with continuous action sets. Our study provides a solid framework for analyzing strategic interactions in continuous action games, emphasizing the importance of market frictions, risk correlations, and policy measures in strategic risk-taking dynamics.
    Date: 2023–05–31
  21. By: Arthur E. Attema (Erasmus University Rotterdam); Olivier L'Haridon (IUF - Institut Universitaire de France - M.E.N.E.S.R. - Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche, CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique); Gijs van de Kuilen (Tilburg University [Netherlands])
    Abstract: In this paper, we use the risk apportionment technique of Eeckhoudt, Rey and Schlesinger (2007) to study higher order risk preferences for others' health as well as ex-ante and ex-post inequality preferences for social risky distributions, and their interaction. In an experiment on a sample of university students acting as impartial spectators, we observe risk aversion towards social health losses and a dislike of ex-ante inequality. In addition, evidence for ex-post inequality seeking is much weaker than evidence for ex-ante inequality aversion. Because ex-ante inequality aversion is unrelated to risk aversion, we conclude that simple forms of utilitarianism are not relevant for individual judgment of social risk over health. Last, our investigation of precautionary distribution, which would occur when one particular group in the society suffers from background health risk, shows substantial polarization of preferences.
    Keywords: Social risk, Ex-ante social welfare, Ex-post social welfare, Risk apportionment
    Date: 2023
  22. By: Fernández-Gallardo, Álvaro (Universidad Alicante); Lloyd, Simon (Bank of England); Manuel, Ed (Bank of England)
    Abstract: We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth by incorporating a narrative-identification strategy within a quantile-regression framework. Exploiting a data set covering a range of macroprudential policy actions across advanced European economies, we identify unanticipated and exogenous macroprudential policy ‘shocks’ and employ them within a quantile-regression setup. While macroprudential policy has near-zero effects on the centre of the GDP-growth distribution, we find that tighter macroprudential policy brings benefits by reducing the variance of future GDP growth, significantly and robustly boosting the left tail while simultaneously reducing the right. Assessing a range of potential channels through which these effects could materialise, we find that macroprudential policy operates through opposing tails of GDP and credit. Tighter macroprudential policy reduces the right tail of the future credit-growth distribution (both household and corporate) which, in turn, is particularly important for mitigating the left tail of GDP growth (ie, GDP-at-risk).
    Keywords: Growth-at-risk; macroprudential policy; narrative identification; quantile local projections
    JEL: E32 E58 G28
    Date: 2023–06–16
  23. By: Rao, Xudong; Cai, Qingyin; Zhang, Yuehua
    Keywords: Risk and Uncertainty, Production Economics, Agribusiness
    Date: 2023
  24. By: Florian Schuster (University of Cologne and ECONtribute); Marco Wysietzki (University of Cologne and ECONtribute); Jonas Zdrzalek (University of Cologne and ECONtribute)
    Abstract: We present a theoretical framework to characterize how financial market participants contribute to systemic risk, allowing us to derive optimal corrective policy interventions. To that end, we embed belief heterogeneity in a model of frictional financial markets. We document the asymmetry that, by their behavior, relatively more optimistic agents contribute more strongly to financial distress than more pessimistic agents do. We further show that financial distress is generally more likely in an economy whose agents hold heterogeneous rather than homogeneous beliefs. Based on these findings, we propose a system of non-linear Pigouvian taxes as the optimal corrective policy, which proves to generate considerable welfare gains over the linear policy advocated by former studies.
    Keywords: Financial amplification, pecuniary externalities, collateral constraint, financial crisis, belief heterogeneity, macroprudential policy
    JEL: D84 E44 G28 H23
    Date: 2023–06
  25. By: Iader Giraldo; Carlos Giraldo; Jose E. Gomez-Gonzalez; Jorge M. Uribe
    Abstract: We use a combination of Extreme Gradient Boosting and SHAP Additive Explanations to uncover the determinants of sovereign risk across a wide range of countries from 2002 to 2021. By considering numerous variables established in existing literature within a single framework, we identify year-by-year determinants of sovereign credit risk. To gauge the liquidity and solvency aspects of sovereign risk, we utilize 5- and 10-year yield spreads as proxies. Our findings show that the key variables driving sovereign risk have remained relatively stable over time and exhibit similarities in both liquidity and solvency components. Among the prominent variables, various macroeconomic fundamentals play a crucial role, including the current account, GDP growth, per capita GDP growth, and the real exchange rate. Prior to the Global Financial Crisis, macroeconomic variables, particularly the current account, held the highest importance in explaining sovereign risk. However, following the GFC, the relative importance of these variables diminished, giving way to institutional variables, especially the rule of law.
    Keywords: Sovereign risk; Explainable AI; Extreme Gradient Boosting model; Macroeconomic and institutional factors.
    JEL: C33 F34 G15
    Date: 2023–05–24
  26. By: Nimalendran, Mahendrarajah; Rzayev, Khaladdin; Sagade, Satchit
    Abstract: Using a comprehensive panel of 2, 969, 829 stock-day data provided by the Securities and Exchange Commission (MIDAS), we find that HFT activity in the stock market increases market-making costs in the options markets. We consider two potential channels - the hedging channel and the arbitrage channel - and find that HFTs' liquidity-demanding orders increase the hedging costs due to a higher stock bid-ask spread and a higher price impact for larger hedging demand. The arbitrage channel subjects the options market-maker to the risk of trading at stale prices. We show that the hedging (arbitrage) channel is dominant for ATM (ITM) options. Given the significant growth in options trading, we believe that our study highlights the need to better understand the costs/risks due to HFT activities in equity markets on derivative markets.
    Keywords: market microstructure; high-frequency trading; options market-making; hedging; liquidity
    JEL: G10 G00
    Date: 2022–01–17
  27. By: Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies, Japan; Rimini Centre for Economic Analysis); Blessings Majoni (National Graduate Institute for Policy Studies, Japan)
    Abstract: We obtain a novel analytic expression of the likelihood for a stationary inverse gamma Stochastic Volatility (SV) model. This allows us to obtain the Maximum Likelihood Estimator for this non linear non Gaussian state space model. Further, we obtain both the filtering and smoothing distributions for the inverse volatilities as mixture of gammas and therefore we can provide the smoothed estimates of the volatility. We show that by integrating out the volatilities the model that we obtain has the resemblance of a GARCH in the sense that the formulas are similar, which simplifies computations significantly. The model allows for fat tails in the observed data. We provide empirical applications using exchange rates data for 7 currencies and quarterly inflation data for four countries. We find that the empirical fit of our proposed model is overall better than alternative models for 4 countries currency data and for 2 countries inflation data.
    Keywords: Hypergeometric Function, Particle Filter, Parallel Computing, Euler Acceleration
    JEL: C32 C58
    Date: 2023–07
  28. By: Camila Casas; Sergii Meleshchuk; Yannick Timmer
    Abstract: We propose a new channel through which exchange rates affect trade. Exploiting the heterogeneity in firms’ foreign currency debt maturity structure around a large depreciation in Colombia, we show that debt revaluation compresses imports due to higher delinquencies and interest rates, while exports are unaffected. Natural and financial hedging successfully mute the import contraction. A costly state verification model with dominant currency financing (DCF) and exporting rationalizes these findings. Quantitatively, DCF explains a significant part of external adjustment in addition to the expenditure switching channel. Pricing exports in the dominant vs. producer currency mutes the effect of DCF on trade.
    Keywords: imports, exports, foreign currency exposure, capital structure, exchange rates, debt revaluation, hedging
    JEL: F31 F32 F41 G15 G21 G32
    Date: 2023

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.