nep-rmg New Economics Papers
on Risk Management
Issue of 2022‒11‒21
twenty-six papers chosen by

  1. Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions By Jules Sadefo Kamdem; Danielle Selambi
  2. Back-testing, risk estimation models: a simulation study for two-asset portfolios By Gyöngyi Bugár; Máté Uzsoki
  3. Things That Have Never Happened Before Happen All the Time By Joshua V. Rosenberg
  4. W-shaped implied volatility curves in a variance-gamma mixture model By Martin Keller-Ressel
  5. Risk Management in Border Inspection By Hillberry,Russell Henry; Karabay,Bilgehan; Tan,Shawn Weiming
  6. Basel III and SME bank finance in Germany By Marek, Philipp; Stein, Ingrid
  7. The rough Hawkes Heston stochastic volatility model By Alessandro Bondi; Sergio Pulido; Simone Scotti
  8. The Most Expected Things Often Come as a Surprise: Analysis of the Impact of Monetary Surprises on the Bank's Risk and Activity By Melchisedek Joslem Ngambou Djatche
  9. Solomon Islands: Technical Assistance Report-Central Bank Risk Management By International Monetary Fund
  10. EXPLORING THE IMPACT OF LOAN RESTRUCTURING IN INDONESIAN BANKING By Wahyoe Soedarmono; Iman Gunadi; Fiskara Indawan; Carla Sheila Wulandari
  11. Identifying Structural Shocks to Volatility through a Proxy-MGARCH Model By Fengler, Matthias; Polivka, Jeanine
  12. Ambiguity, value of information and forest rotation decision under storm risk By Patrice Loisel; Marielle Brunette; Stéphane Couture
  13. Can Time-Varying Currency Risk Hedging Explain Exchange Rates? By Leonie Bräuer; Harald Hau
  14. Considerations for the allocation of non-default losses by financial market infrastructures By Daniele Costanzo; Radoslav Raykov
  15. Shannon entropy: an econophysical approach to cryptocurrency portfolios By Noe Rodriguez-Rodriguez; Octavio Miramontes
  16. Эконометрический анализ факторов банкротств российских компаний в обрабатывающем секторе By Bekirova, Olga; Zubarev, Andrey
  17. A la Recherche du Temps Perdu : Legal and Quantitative analysis of the First Documented Option Market - Paris 1844-1939 By Antoine Parent; Pierre-Charles Pradier
  18. Factor Investing with a Deep Multi-Factor Model By Zikai Wei; Bo Dai; Dahua Lin
  19. Have the risk policy shifts related to Seveso Upper Tier establishments in France led to an improvement in risk prevention? A focus on three risk prevention tools By Scarlett Tannous; Myriam Merad
  20. Forecasting Oil Prices: Can Large BVARs Help? By Bao H. Nguyen; Bo Zhang
  21. What drives most jumps in global crude oil prices? Fundamental shortage conditions, Cartel, geopolitics or the behavior of market financial participants By Refk Selmi; Shawkat Hammoudeh; Mark Wohar
  22. Uncertainty, Skewness, and the Business Cycle Through the MIDAS Lens By Efrem Castelnuovo; Lorenzo Mori
  23. Social Distancing and Risk Taking: Evidence from a Team Game Show * By Jean-Marc Bourgeon; José de Sousa; Alexis Noir-Luhalwe
  24. Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents By Michele Vodret; Iacopo Mastromatteo; Bence Tóth; Michael Benzaquen
  25. American options in the Volterra Heston model By Etienne Chevalier; Sergio Pulido; Elizabeth Zúñiga
  26. Fractal landscape dynamics in dense emulsions and stock prices By Clary Rodriguez-Cruz; Mehdi Molaei; Amruthesh Thirumalaiswamy; Klebert Feitosa; Vinothan N. Manoharan; Shankar Sivarajan; Daniel H. Reich; Robert A. Riggleman; John C. Crocker

  1. By: Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Danielle Selambi (African Institute for Mathematical Sciences (AIMS-Cameroon))
    Abstract: In this paper, we estimate the cost of a data breach using the number of compromised records. The number of such records is predicted by means of a machine learning model, particularly the Random Forest. We further analyse the fat tail phenomena which capture the underlying dynamics in the number of affected records. The objective is to calculate the maximum loss in order to answer the question of the insurability of cyber risk. Our results show that the total number of affected records follow a Frechet distribution, and we then estimate the Generalized Extreme Value (GEV) parameters to calculate the value at risk (VaR). This analysis is critical because it gives an idea of the maximum loss that can be generated by an enterprise data breach. These results are usable in anticipating the premiums for cyber risk coverage in the insurance markets.
    Keywords: Cyber insurance,Cyber risk,Machine Learning,Regression Trees,Random Forest,Generalized Extreme Value
    Date: 2022–10–13
    Abstract: The aim of the study is to check the validity of five different risk-estimation models for two-asset portfolios, a topic which is relevant in model selection both for determining the minimum capital requirements for trading book portfolios and for the regulatory monitoring of the performance of internal risk models. Simulations based on a real data set containing the FTSE 100 constituents were carried out, and the risk was gauged by Expected Shortfall, a measure which also captures tail risk. Given that the period studied includes that of the subprime crisis, there is an inherent opportunity to compare and contrast the results produced under disaster conditions with others from less stressful periods. Our empirical analysis has confirmed that using Expected Shortfall instead of Value-at-Risk alone is not enough, and that the risk model has to be carefully selected and back-tested. The general Pareto distribution proved to be a prudent choice for risk models. In fact, among the five models considered, the model when general Pareto marginals were coupled with Clayton copula showed the best performance. It was, however, also revealed that this model is susceptible to being “over-cautious” in estimating loss.
    Keywords: Risk estimation models, Portfolio, Back-testing, Expected Shortfall, Copula
    Date: 2021–08
  3. By: Joshua V. Rosenberg
    Abstract: Remarks at the Central Bank of Nigeria’s Second National Risk Management Conference (delivered via videoconference).
    Keywords: risk management
    Date: 2022–10–27
  4. By: Martin Keller-Ressel
    Abstract: In liquid option markets, W-shaped implied volatility curves have occasionally be observed. We show that such shapes can be reproduced in a mixture of two variance-gamma models. This is in contrast to lognormal models, where at least three different distributions have to be mixed in order to produce a W-shape, as recently shown by Glasserman and Pirjol.
    Date: 2022–09
  5. By: Hillberry,Russell Henry; Karabay,Bilgehan; Tan,Shawn Weiming
    Abstract: As part of their commitments under the World Trade Organization's Agreement on Trade Facilitation, many developing countries are set to adopt risk management, a strategy for selecting import shipments for inspection. This paper formalizes key enforcement issues related to risk management. It argues that the complexities of international trade oversight mean that inspecting agencies lack certainty about the conditional probability that a given shipment will not comply with import regulations. Ambiguity of this sort is likely to be especially important in developing countries that lack the sophisticated information technology used in advanced risk management systems. This paper formalizes a role for ambiguity in a theoretical model of border inspection. It provides evidence suggesting that ambiguity affects inspection rates. Finally, the paper calibrates the model and shock the ambiguity parameters to illustrate the consequences of an information technology-driven improvement in risk management capabilities for equilibrium rates of search and compliance.
    Keywords: International Trade and Trade Rules,Information Technology,Trade Facilitation,Financial Sector Policy,Human Rights
    Date: 2020–10–14
  6. By: Marek, Philipp; Stein, Ingrid
    Abstract: This paper examines how Basel III capital reforms affected bank lending in Ger- many. We focus on the increase of minimum risk-based capital requirements and the introduction of the leverage ratio. The announcement of stricter risk-based capital regulation significantly affected low capitalized banks. The impact depends on a bank's credit risk model, i.e. whether a bank applies the standardized approach (SA) or an internal ratings-based approach (IRBA) to determine risk weights. Low capitalized SA banks significantly cut lending whereas IRBA banks did not ad- just lending volumes. By contrast, low capitalized IRBA banks significantly in- creased collateralization while low capitalized SA banks adjusted collateralization only marginally. Moreover, the impact on SMEs and large companies also differs. In terms of lending, SMEs were affected more strongly, whilst in terms of collateralization the impact on large companies was bigger. The announcement of the leverage ratio had, however, a rather limited impact. We find some evidence that low capitalized banks reduced lending. Furthermore, low capitalized banks somewhat tightened collateral requirements, especially for large companies.
    Keywords: Basel III,bank lending,nancial regulation,small and medium-sizedenterprises (SMEs)
    JEL: D22 E58 G21
    Date: 2022
  7. By: Alessandro Bondi; Sergio Pulido; Simone Scotti
    Abstract: We study an extension of the Heston stochastic volatility model that incorporates rough volatility and jump clustering phenomena. In our model, named the rough Hawkes Heston stochastic volatility model, the spot variance is a rough Hawkes-type process proportional to the intensity process of the jump component appearing in the dynamics of the spot variance itself and the log returns. The model belongs to the class of affine Volterra models. In particular, the Fourier-Laplace transform of the log returns and the square of the volatility index can be computed explicitly in terms of solutions of deterministic Riccati-Volterra equations, which can be efficiently approximated using a multi-factor approximation technique. We calibrate a parsimonious specification of our model characterized by a power kernel and an exponential law for the jumps. We show that our parsimonious setup is able to simultaneously capture, with a high precision, the behavior of the implied volatility smile for both S&P 500 and VIX options. In particular, we observe that in our setting the usual shift in the implied volatility of VIX options is explained by a very low value of the power in the kernel. Our findings demonstrate the relevance, under an affine framework, of rough volatility and self-exciting jumps in order to capture the joint evolution of the S&P 500 and VIX.
    Date: 2022–10
  8. By: Melchisedek Joslem Ngambou Djatche (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur)
    Abstract: In this paper, we analyse the link between monetary and banks' activity and risk-taking. Some theoretical and empirical studies show that monetary easing increases banks' appetite for risk, affect credit allocation and bank's profitability. Our study adds to analyses of the monetary risk-taking channel considering monetary surprise, i.e. the impact of unexpected changes in monetary policy on bank's risk and activity. Using a dataset of US banks, we find that higher increase or lower decrease of interest rates than expected (negative surprise) leads banks to take more risk, to grant more corporate loans than consumption loans, and to be more profitable. We complement the literature on the risk-taking channel and provide arguments that Central Banks can manage financial stability.
    Date: 2021
  9. By: International Monetary Fund
    Abstract: At the request of the Central Bank of Solomon Islands (CBSI), a Monetary and Capital Markets Department (MCM) mission provided technical assistance on central bank risk management during the period August–September 2021. The mission comprised Mr. Paul Woods (Central Bank of Ireland) and Mr. Chris Aylmer (formerly with the Reserve Bank of Australia), under supervision of Mr. Ashraf Khan (MCM, Central Bank Operations Division) The purpose of the mission was to guide the CBSI on how to establish an Enterprise Risk Management (ERM) framework. The mission focused in particular on establishing a strengthened risk culture throughout the organization, and strengthening risk governance - including the role of the CBSI’s risk management unit.
    Date: 2022–10–21
  10. By: Wahyoe Soedarmono; Iman Gunadi (Bank Indonesia); Fiskara Indawan (Bank Indonesia); Carla Sheila Wulandari
    Abstract: This paper investigates the impact of loan restructuring on risk and performance in Indonesian banking. We find that higher restructured loans increase non-performing loans. Concomittantly, higher restructured loans are associated with higher capital ratio and lower insolvency risk. In this regard, higher capital ratio is sufficient to offset an increase in credit risk, which in turn enhances bank solvency. A deeper analysis suggests that such findings are driven by banks with lower capitalization and private-owned banks. For banks with higher capitalization and government-owned banks, higher restructured loans may deteriorate bank solvency. Moreover, the role of loan restructuring in strengthening financial stability is more pronounced during economic downturns in general. Although loan restructuring matters for financial stability regardless of the degree of economic growth, the effectiveness of loan restructuring policy is conditional.
    Keywords: Bank loan restructuring, risk, capital ratio, performance, Indonesia
    JEL: G21 G28
    Date: 2021
  11. By: Fengler, Matthias; Polivka, Jeanine
    JEL: C32 C51 C58 G12
    Date: 2022
  12. By: Patrice Loisel (MISTEA - Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie - INRA - Institut National de la Recherche Agronomique - Montpellier SupAgro - Institut national d’études supérieures agronomiques de Montpellier); Marielle Brunette (BETA - Bureau d'Économie Théorique et Appliquée - AgroParisTech - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Stéphane Couture (MIAT INRAE - Unité de Mathématiques et Informatique Appliquées de Toulouse - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: Storm is a major risk in forestry. However, due to the more or less pessimistic scenarios of future climate change, storm frequency is now ambiguous and only partially known (i.e., scenario ambiguity). Furthermore, within each scenario, the quantification of storm frequency is also ambiguous due to the differences in risk quantification by experts, creating a second level of ambiguity (i.e., frequency ambiguity). In such an ambiguous context, knowledge of the future climate through accurate information about this risk is fundamental and can be of significant value. In this paper, we question how ambiguity and ambiguity aversion affect forest management, in particular, optimal cutting age. Using a classical Faustmann framework of forest rotation decisions, we compare three different situations: risk, scenario ambiguity and frequency ambiguity. We show that risk and risk aversion significantly reduce the optimal cutting age. We also show that both scenario and frequency ambiguities reinforce the effect of risk. Inversely, ambiguity aversion has no effect. The value of information that resolves scenario ambiguity is high, whereas it is null for frequency ambiguity.
    Keywords: Rotation decision,Risk,Ambiguity,Ambiguity Aversion,Risk Aversion,Value of Information,Forests,Faustmann criterion
    Date: 2022–10–04
  13. By: Leonie Bräuer (University of Geneva; Swiss Finance Institute, Students); Harald Hau (University of Geneva - Geneva Finance Research Institute (GFRI); Swiss Finance Institute; Centre for Economic Policy Research (CEPR); CESifo (Center for Economic Studies and Ifo Institute))
    Abstract: Over the last decade foreign bond portfolio positions in US dollar assets have risen above the reciprocal US investor positions in foreign currencies. In periods of increased economic uncertainty, institutional investors hedge their international bond positions, which creates a net hedging demand for dollar assets that depreciates USD rates in both the forward and spot markets. We document the time-varying nature of this net hedging demand and show how it relates to economic uncertainty and the US net foreign bond position in various currencies. Based on a parsimonious VAR model, we find that changes in FX hedging pressure can account for approximately 30% of all monthly variation in the seven most important dollar exchange rates from 2012 to 2022.
    Keywords: Exchange Rate, Hedging Channel, Institutional Investors
    JEL: E44 F31 F32 G11 G15 G23
    Date: 2022–10
  14. By: Daniele Costanzo; Radoslav Raykov
    Abstract: Non-default losses of financial market infrastructures (FMIs) have gained attention due to their potential impacts on FMIs and FMI participants, and the lack of a common approach to address them. A key question is, who should absorb these losses?
    Keywords: Financial markets; Financial system regulation and policies
    JEL: G32 G33 G23 G28
    Date: 2022–11
  15. By: Noe Rodriguez-Rodriguez; Octavio Miramontes
    Abstract: Cryptocurrency markets have attracted many interest for global investors because of their novelty, wide online availability, increasing capitalization and potential profits. In the econophysics tradition we show that many of the most available cryptocurrencies have return statistics that do not follow Gaussian distributions but heavy--tailed distributions instead. Entropy measures are also applied showing that portfolio diversification is a reasonable practice for decreasing return uncertainty.
    Date: 2022–10
  16. By: Bekirova, Olga; Zubarev, Andrey
    Abstract: This work is devoted to the analysis of the factors influencing the bankruptcy of the Russian manufacturing industry companies for the period from 2012 to 2020. Logistic regression was used as an econometric tool for the modelling the probability of companies’ default. According to the results, financial indicators of profitability, liquidity and business activity play a significant role in explaining the probability of default of Russian manufacturing companies. Special attention was paid to the impact on the probability of bankruptcy of corporate governance and ownership structure factors. First, including these indicators into the model led to an increase in its predictive power. Secondly, CEO-duality increases the stability of the company, and too high maximum share of ownership increases the likelihood of bankruptcy.
    Keywords: probability of default, logistic regression, corporate governance.
    JEL: C25 C51 G32 G33 G34 L60
    Date: 2022–06
  17. By: Antoine Parent (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis, IXXI - Institut Rhône-Alpin des systèmes complexes - ENS Lyon - École normale supérieure - Lyon - UL2 - Université Lumière - Lyon 2 - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes, OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po); Pierre-Charles Pradier (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We provide the first ever quantitative analysis of pricing and profitability of option trading in Paris from 1843 to 1939 based on a data source featuring more than 75,000 option prices. Using a special case of the Black (1976) option pricing model, we show that, albeit options were consistently undervalued, the market was still profitable for all the parties. We prove that the exceptional longevity of the Paris options market was based on a 4-pillars market microstructure: (1.) systematic underpricing of cheap options to attract gamblers, (2.) administration of settlement price by the brokers' syndicate, (3.) parimutuel-like betting operation and safety thanks to (4.) a sophisticated risk management in the position-taking style which minimized actual clearing price manipulation.
    Keywords: Option pricing,financial risk management,betting markets,alternative investments
    Date: 2022–10
  18. By: Zikai Wei; Bo Dai; Dahua Lin
    Abstract: Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks. Both academia and industry are striving to find new factors that have good explanatory power for future stock returns and good stability of their predictive power. In practice, factor investing is still largely based on linear multi-factor models, although many deep learning methods show promising results compared to traditional methods in stock trend prediction and portfolio risk management. However, the existing non-linear methods have two drawbacks: 1) there is a lack of interpretation of the newly discovered factors, 2) the financial insights behind the mining process are unclear, making practitioners reluctant to apply the existing methods to factor investing. To address these two shortcomings, we develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights, which help us easily build a dynamic and multi-relational stock graph in a hierarchical structure to learn the graph representation of stock relationships at different levels, e.g., industry level and universal level. Subsequently, graph attention modules are adopted to estimate a series of deep factors that maximize the cumulative factor returns. And a factor-attention module is developed to approximately compose the estimated deep factors from the input factors, as a way to interpret the deep factors explicitly. Extensive experiments on real-world stock market data demonstrate the effectiveness of our deep multi-factor model in the task of factor investing.
    Date: 2022–10
  19. By: Scarlett Tannous (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique, DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique); Myriam Merad (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique)
    Date: 2022–10–10
  20. By: Bao H. Nguyen; Bo Zhang
    Abstract: Large Bayesian Vector Autoregressions (BVARs) have been a successful tool in the forecasting literature and most of this work has focused on macroeconomic variables. In this paper, we examine the ability of large BVARs to forecast the real price of crude oil using a large dataset with over 100 variables. We find consistent results that the large BVARs do not beat the BVARs with small and medium sizes for short forecast horizons but offer better forecasts at long horizons. In line with the forecasting macroeconomic literature, we also find that the forecast ability of the large models further improves upon the competing standard BVARs once endowed with flexible error structures.
    Keywords: forecasting, non-Gaussian, stochastic volatility, oil prices, big data
    JEL: C11 C32 C52 Q41 Q47
    Date: 2022–10
  21. By: Refk Selmi (ESC PAU - Ecole Supérieure de Commerce, Pau Business School); Shawkat Hammoudeh (Lebow College of Business, Drexel University - CERAG - Centre d'études et de recherches appliquées à la gestion - UPMF - Université Pierre Mendès France - Grenoble 2 - CNRS - Centre National de la Recherche Scientifique); Mark Wohar (University of Nebraska Omaha - University of Nebraska System)
    Abstract: Several studies have emphasized the potential role of oil price volatility as a leading macroeconomic indicator, since it provides prominent information to energy traders, market participants and policymakers. In an effort to shed fresh insights on the underlying factors of wide oil price changes, the objective of this paper is twofold. First to capture large oil price changes caused by the arrival of surprising news (i.e., jumps); second to distinguish between short-, medium-and long-term determinants of jumps in oil prices due to changes in oil supply and demand fundamentals, factors associated with the market power of oil producers, speculation, geopolitical risks and OPEC's spare capacity. Using an Empirical Mode Decomposition (EMD), we find that oil supply and demand forces are the most prevalent in determining oil price changes in the long run, which is quite predictable. OPEC gains increasing importance in the medium-and long-terms. Our method also allows us to show that OPEC's use of spare capacity moderately reduces oil price volatility in the short-term, thus suggesting a limited stabilizing influence on the oil market. We consider broader policy implications for our results.
    Keywords: Oil price jumps,oil price determinants,Empirical Mode Decomposition,Empirical Mode Decomposition JEL classification: G15,C11,C58,Q30,Q31
    Date: 2022–08–01
  22. By: Efrem Castelnuovo (University of Padova); Lorenzo Mori (University of Padova)
    Abstract: We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles of the estimated conditional distribution to produce measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes in uncertainty are associated with an increase in (left) skewness and a downturn in real activity. Empirical findings related to VAR impulse responses and forecast error variance decomposition are shown to depend on the inclusion/omission of monthly-level information on financial conditions when estimating real GDP growth’s conditional density. Effects are significantly downplayed if we consider a quarterly-only quantile regression model. A counterfactual simulation conducted by shutting down the endogenous response of skewness to uncertainty shocks shows that skewness substantially amplifies the recessionary effects of uncertainty.
    Keywords: Uncertainty, skewness, quantile regressions, vector autoregressions, MIDAS
    Date: 2022–10
  23. By: Jean-Marc Bourgeon (ECO-PUB - Economie Publique - INRA - Institut National de la Recherche Agronomique - AgroParisTech, X-DEP-ECO - Département d'Économie de l'École Polytechnique - X - École polytechnique); José de Sousa (Université Paris-Saclay, RITM - Réseaux Innovation Territoires et Mondialisation - Université Paris-Saclay, LIEPP - Laboratoire interdisciplinaire d'évaluation des politiques publiques (Sciences Po) - Sciences Po - Sciences Po); Alexis Noir-Luhalwe (Université Paris-Saclay, RITM - Réseaux Innovation Territoires et Mondialisation - Université Paris-Saclay)
    Abstract: We examine the risky choices of pairs of contestants in a popular radio game show in France. At the onset of the COVID-19 pandemic, the show, held in person, had to switch to an all-remote format. We find that such an exogenous change in social context affected risk-taking behavior. Remotely, pairs take far fewer risks when the stakes are high than in the flesh. This behavioral difference is consistent with prosocial behavior theories, which argue that the nature of social interactions influences risky choices. Our results suggest that working from home may reduce participation in profitable but risky team projects.
    Keywords: COVID-19,Social Distancing,Social Pressure,Decision Making,Risk
    Date: 2022–09–30
  24. By: Michele Vodret; Iacopo Mastromatteo; Bence Tóth; Michael Benzaquen (LadHyX - Laboratoire d'hydrodynamique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique)
    Abstract: We relax the strong rationality assumption for the agents in the paradigmatic Kyle model of price formation, thereby reconciling the framework of asymmetrically informed traders with the Adaptive Market Hypothesis, where agents use inductive rather than deductive reasoning. Building on these ideas, we propose a stylised model able to account parsimoniously for a rich phenomenology, ranging from excess volatility to volatility clustering. While characterising the excess-volatility dynamics, we provide a microfoundation for GARCH models. Volatility clustering is shown to be related to the self-excited dynamics induced by traders' behaviour, and does not rely on clustered fundamental innovations. Finally, we propose an extension to account for the fragile dynamics exhibited by real markets during flash crashes.
    Keywords: adaptive agents,volatility clustering,excess volatility,price impact
    Date: 2022–10–04
  25. By: Etienne Chevalier (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - UEVE - Université d'Évry-Val-d'Essonne - ENSIIE - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Sergio Pulido (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - UEVE - Université d'Évry-Val-d'Essonne - ENSIIE - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Elizabeth Zúñiga (LaMME - Laboratoire de Mathématiques et Modélisation d'Evry - UEVE - Université d'Évry-Val-d'Essonne - ENSIIE - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: We price American options using kernel-based approximations of the Volterra Heston model. We choose these approximations because they allow simulation-based techniques for pricing. We prove the convergence of American option prices in the approximating sequence of models towards the prices in the Volterra Heston model. A crucial step in the proof is to exploit the affine structure of the model in order to establish explicit formulas and convergence results for the conditional Fourier--Laplace transform of the log price and an adjusted version of the forward variance. We illustrate with numerical examples our convergence result and the behavior of American option prices with respect to certain parameters of the model.
    Date: 2022–04–27
  26. By: Clary Rodriguez-Cruz; Mehdi Molaei; Amruthesh Thirumalaiswamy; Klebert Feitosa; Vinothan N. Manoharan; Shankar Sivarajan; Daniel H. Reich; Robert A. Riggleman; John C. Crocker
    Abstract: Many soft and biological materials display so-called 'soft glassy' dynamics; their constituents undergo anomalous random motion and intermittent cooperative rearrangements. Stock prices show qualitatively similar dynamics, whose origins also remain poorly understood. Recent simulations of a foam have revealed that such motion is due to the system evolving in a high-dimensional configuration space via energy minimization on a slowly changing, fractal energy landscape. Here we show that the salient geometrical features of such energy landscapes can be explored and quantified not only in simulation but empirically using real-world, high-dimensional data. In a mayonnaise-like dense emulsion, the experimentally observed motion of oil droplets shows that the fractal geometry of the configuration space paths and energy landscape gives rise to the anomalous random motion and cooperative rearrangements, confirming corresponding simulations in detail. Our empirical approach allows the same analyses to be applied to the component stock prices of the Standard and Poor's 500 Index. This analysis yields remarkably similar results, revealing that stock return dynamics also appear due to prices moving on a similar, slowly evolving, high-dimensional fractal landscape.
    Date: 2022–10

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