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on Risk Management |
| By: | Bingchu Nie; Dejian Tian; Long Jiang |
| Abstract: | In this paper, we introduce a new class of set-valued risk measures, named set-valued star-shaped risk measures. Motivated by the results of scalar monetary and star-shaped risk measures, this paper investigates the representation theorems in the set-valued framework. It is demonstrated that set-valued risk measures can be represented as the union of a family of set-valued convex risk measures, and set-valued normalized star-shaped risk measures can be represented as the union of a family of set-valued normalized convex risk measures. The link between set-valued risk measures and set-valued star-shaped risk measures is also established. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.18014&r=rmg |
| By: | Alexis Direr (LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne) |
| Abstract: | I study the allocation problem of investors who hold their portfolio until reaching a target wealth. The strategy suppresses final wealth uncertainty but creates a time horizon risk. I begin with a classical mean variance model transposed in the duration domain, then study a dynamic portfolio choice problem with Generalized Expected Discounted Utility preferences. Using long-term US return data, I show in the mean variance model that a large amount of time horizon risk can be diversified away by investing a significant share of equities. In the dynamic model, more impatient investors are also more averse to timing risk and invest less in equities. The optimal equity share is downward trending as accumulated wealth approaches its target. |
| Keywords: | timing risk, portfolio choice, risk aversion |
| Date: | 2023–12–29 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04501750&r=rmg |
| By: | M\"ucahit Ayg\"un; Fabio Bellini; Roger J. A. Laeven |
| Abstract: | Geometrically convex functions constitute an interesting class of functions obtained by replacing the arithmetic mean with the geometric mean in the definition of convexity. As recently suggested, geometric convexity may be a sensible property for financial risk measures ([7, 13, 4]). We introduce a notion of GG-convex conjugate, parallel to the classical notion of convex conjugate introduced by Fenchel, and we discuss its properties. We show how GG-convex conjugation can be axiomatized in the spirit of the notion of general duality transforms introduced in [2, 3]. We then move to the study of GG-convex risk measures, which are defined as GG-convex functionals defined on suitable spaces of random variables. We derive a general dual representation that extends analogous expressions presented in [4] under the additional assumptions of monotonicity and positive homogeneity. As a prominent example, we study the family of Orlicz risk measures. Finally, we introduce multiplicative versions of the convex and of the increasing convex order and discuss related consistency properties of law-invariant GG-convex risk measures. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.06188&r=rmg |
| By: | Michaël Allouche (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Clara Lage (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Edwin Mangin (BNPP) |
| Abstract: | Rating Migration Matrix is a crux to assess credit risks. Modeling and predicting these matrices are then an issue of great importance for risk managers in any financial institution. As a challenger to usual parametric modeling approaches, we propose a new structured dictionary learning model with auto-regressive regularization that is able to meet key expectations and constraints: small amount of data, fast evolution in time of these matrices, economic interpretability of the calibrated model. To show the model applicability, we present a numerical test with real data. The source code and the data are available at https://github.com/michael-allouche/ dictionary-learning-RMM.git for the sake of reproducibility of our research. |
| Keywords: | Rating Migration Matrix, Dictionary learning, auto-regressive modeling, interpretability |
| Date: | 2024–01–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03715954&r=rmg |
| By: | Kräussl, Roman; Oladiran, Tobi; Stefanova, Denitsa |
| Abstract: | We examine whether the uncertainty related to environmental, social, and governance (ESG) regulation developments is reflected in asset prices. We proxy the sensitivity of firms to ESG regulation uncertainty by the disparity across the components of their ESG ratings. Firms with high ESG disparity have a higher option-implied cost of protection against downside tail risk. The impact of the misalignment across the different dimensions of the ESG score is distinct from that of ESG score level itself. Aggregate downside risk bears a negative price for firms with low ESG disparity. |
| Keywords: | ESG, rating, downside risk, options, regulation, risk premium |
| JEL: | G12 G18 G32 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cfswop:285370&r=rmg |
| By: | Florian Bourgey (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique, Bloomberg L.P. Quantitative Finance Research - Bloomberg L.P.); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Ying Jiao (ISFA - Institut de Science Financière et d'Assurances) |
| Abstract: | This paper investigates the impact of transition risk on a firm's low-carbon production. As the world is facing global climate changes, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbon-neutral scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been proposed in the literature for different production sectors in more comprehensive socioeconomic context. In this paper, we consider, on the one hand, a firm that aims to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios. Solving the penalized optimization problem provides the optimal emission according to a given SSP benchmark. On the other hand, such transitions affect the firm's credit risk. We model the default time by using the structural default approach. We are particularly concerned with how the adopted strategies by following different SSPs scenarios may influence the firm's default probability. |
| Keywords: | Climate risk, transition risk, credit risk, Shared Socioeconomic Pathways, carbon emission reduction, optimal production profit |
| Date: | 2022–12–19 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03458299&r=rmg |
| By: | Koresh Galil (BGU); Eva Varon (BGU) |
| Keywords: | Banks, Stocks volatility, National culture, Covid-19, Uncertainty avoidance, Individualism |
| JEL: | G12 G21 Z10 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:bgu:wpaper:2307&r=rmg |
| By: | Degui Li; Oliver Linton; Haoxuan Zhang |
| Abstract: | We propose a new estimator of high-dimensional spot volatility matrices satisfying a low-rank plus sparse structure from noisy and asynchronous high-frequency data collected for an ultra-large number of assets. The noise processes are allowed to be temporally correlated, heteroskedastic, asymptotically vanishing and dependent on the efficient prices. We define a kernel-weighted pre-averaging method to jointly tackle the microstructure noise and asynchronicity issues, and we obtain uniformly consistent estimates for latent prices. We impose a continuous-time factor model with time-varying factor loadings on the price processes, and estimate the common factors and loadings via a local principal component analysis. Assuming a uniform sparsity condition on the idiosyncratic volatility structure, we combine the POET and kernel-smoothing techniques to estimate the spot volatility matrices for both the latent prices and idiosyncratic errors. Under some mild restrictions, the estimated spot volatility matrices are shown to be uniformly consistent under various matrix norms. We provide Monte-Carlo simulation and empirical studies to examine the numerical performance of the developed estimation methodology. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.06246&r=rmg |
| By: | Gianluca Pallante; Mattia Guerini; Mauro Napoletano; Andrea Roventini |
| Abstract: | We extend the Schumpeter meeting Keynes (K+S; see Dosi et al., 2010, 2013, 2015) to model the emergence and the dynamics of an interbank network in the money market. The extended model allows banks to directly exchange funds, while evaluating their interbank positions using a network- based clearing mechanism (NEVA, see Barucca et al., 2020). These novel adds on, allow us to better measure financial contagion and systemic risk events in the model and to study the possible interactions between micro-prudential and macro-prudential policies. We find that the model can replicate new stylized facts concerning the topology of the interbank network, as well as the dynamics of individual banks’ balance sheets. Policy results suggest that the economic system at large can benefit from the introduction of a micro-prudential regulation that takes into account the interbank network relationships. Such a policy decreases the incidence of systemic risk events and the bankruptcies of financial institutions. Moreover, a trade-off between financial stability and macroeconomic performance does not emerge in a two-pillar regulatory framework grounded on i) a Basel III macro-prudential regulation and ii) a NEVA-based micro-prudential policy. Indeed, the NEVA allows the economic system to achieve financial stability without overly stringent capital requirements. |
| Keywords: | Financial contagion, Systemic risk, Micro-prudential policy, Macro-prudential policy, Macroeconomic stability, Agent-based computational economics |
| Date: | 2024–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2024/08&r=rmg |
| By: | Salem, Leila Ben (University of Sousse); Zayati, Montassar (University of Sousse); Nouira, Ridha (University of Sousse); Rault, Christophe (University of Orléans) |
| Abstract: | This paper investigates the co-movements of oil prices and the exchange rates of 10 top oil-importing and oil-exporting countries. Firstly, we estimated the total static spillover index based on vector autoregressive (VAR) models. Secondly, we adopted the recent DCC-GARCH-CONNECTEDNESS approach proposed by Gabauer (2020) to conduct a time-varying analysis that investigates the directionally dynamic connectedness among WTI and Shanghai crude oil futures and currency markets. We explored contagion spillover volatility by focusing on a sample of major oil-exporting and oil-importing countries using daily data from 4 March 2018 to 25 August 2023. We analysed this relationship during four phases: the entire sample; before COVID-19; during COVID-19; and during the Russian–Ukrainian war. Our results confirm the persistence of volatility for the series studied, thereby justifying the use of the dynamic connectedness approach. Our findings also reveal strong evidence of volatility transmission between oil prices and exchange-rate markets. However, the COVID-19 pandemic and the Russian–Ukrainian war have altered this link. The connectedness between the two markets (petrol and exchange) was stronger at the beginning of the crisis period and then gradually depreciated in value over time. Our findings reveal that exchange rates for both oil-exporting and oil-importing countries are more sensitive to oil price shocks during crises than in normal periods. This suggests that volatility contagion between these two markets continues to exist, thus emphasising the role of oil price shocks as net transmitters across the network during extreme scenarios. |
| Keywords: | DCC-GARCH-Connectedness, exchange rates, WTI, Shanghai futures, COVID-19, Russian–Ukrainian war |
| JEL: | C5 Q4 Q43 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16832&r=rmg |
| By: | Kiriliouk, Anna (Université de Namur); Lee, Jeongjin (Université de Namur); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | Regular vine sequences permit the organisation of variables in a random vector along a sequence of trees. Regular vine models have become greatly popular in dependence modelling as a way to combine arbitrary bivariate copulas into higher-dimensional ones, offering flexibility, parsimony, and tractability. In this project, we use regular vine structures to decompose and construct the exponent measure density of a multivariate extreme value distribution, or, equivalently, the tail copula density. Although these densities pose theoretical challenges due to their infinite mass, their homogeneity property offers simplifications. The theory sheds new light on existing parametric families and facilitates the construction of new ones, called X-vines. Computations proceed via recursive formulas in terms of bivariate model components. We develop simulation algorithms for X-vine multivariate Pareto distributions as well as methods for parameter estimation and model selection on the basis of threshold exceedances. The methods are illustrated by Monte Carlo experiments and a case study on US flight delay data. |
| Keywords: | Exponent measure ; graphical model ; multivariate Pareto distribution ; pair copula construction ; regular vine ; tail copula |
| Date: | 2023–12–22 |
| URL: | http://d.repec.org/n?u=RePEc:aiz:louvad:2023038&r=rmg |
| By: | Brendan J. Chapuis; John Coglianese |
| Abstract: | In this note, we introduce a measure of unemployment risk, the likelihood of a worker becoming unemployed within the next twelve months. By using nonparametric machine learning applied to data on millions of workers in the US, we can estimate how unemployment risk varies across individuals and over time. |
| Date: | 2024–03–08 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfn:2024-03-08-1&r=rmg |
| By: | Khizar Qureshi; Tauhid Zaman |
| Abstract: | Pairs trading, a strategy that capitalizes on price movements of asset pairs driven by similar factors, has gained significant popularity among traders. Common practice involves selecting highly cointegrated pairs to form a portfolio, which often leads to the inclusion of multiple pairs sharing common assets. This approach, while intuitive, inadvertently elevates portfolio variance and diminishes risk-adjusted returns by concentrating on a small number of highly cointegrated assets. Our study introduces an innovative pair selection method employing graphical matchings designed to tackle this challenge. We model all assets and their cointegration levels with a weighted graph, where edges signify pairs and their weights indicate the extent of cointegration. A portfolio of pairs is a subgraph of this graph. We construct a portfolio which is a maximum weighted matching of this graph to select pairs which have strong cointegration while simultaneously ensuring that there are no shared assets within any pair of pairs. This approach ensures each asset is included in just one pair, leading to a significantly lower variance in the matching-based portfolio compared to a baseline approach that selects pairs purely based on cointegration. Theoretical analysis and empirical testing using data from the S\&P 500 between 2017 and 2023, affirm the efficacy of our method. Notably, our matching-based strategy showcases a marked improvement in risk-adjusted performance, evidenced by a gross Sharpe ratio of 1.23, a significant enhancement over the baseline value of 0.48 and market value of 0.59. Additionally, our approach demonstrates reduced trading costs attributable to lower turnover, alongside minimized single asset risk due to a more diversified asset base. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2403.07998&r=rmg |
| By: | Tervola, Jussi; Iivonen, Saija; Hiilamo, Heikki |
| Abstract: | Social insurance and social assistance reflect fundamental principles of social policies. Social insurance benefits cover employed individuals against a social risk event such as unemployment or disability in exchange of paid contributions. Social assistance benefits, in turn, are designed typically to secure the minimum standard of living, regardless of past contribution. In this article we ask if the dualism is feasible to depict contemporary social benefits that cover traditional social risks: unemployment, childbirth, sickness, disability, and old age. A policy analysis of six European countries with extensive social security systems – Denmark, Estonia, Finland, Netherlands, Sweden, and United Kingdom – demonstrates that while traditional insurance benefits and assistance benefits still make up the majority of risk-based benefits, also different kinds of deviations from the pure forms are observed. Some countries provide hybrid benefits where past contribution affects benefit rate, but non-contributory minimum is guaranteed for all facing the risk. Some countries provide income-tested contributory benefits which is against the traditional insurance logic. Moreover, universal flat-rate benefits are found especially covering the risk of old age. |
| Date: | 2024–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:97xzj&r=rmg |
| By: | Koresh Galil (BGU); Ami Hauptman (Computer Science Department of Sapir College); Rosit Levy Rosenboim (Applied Economics Department of Sapir College) |
| Keywords: | Corporate Ratings, Machine Learning, Classification and Regression Tree, Support Vector Regression, CART, SVR, Size |
| JEL: | C45 C53 G24 G32 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:bgu:wpaper:2308&r=rmg |
| By: | Mnacho Echenim (LIG - Laboratoire d'Informatique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, CAPP - Calculs algorithmes programmes et preuves - LIG - Laboratoire d'Informatique de Grenoble - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Emmanuel Gobet (CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique - X - École polytechnique - CNRS - Centre National de la Recherche Scientifique); Anne-Claire Maurice |
| Abstract: | We design a novel calibration procedure that is designed to handle the specific characteristics of options on cryptocurrency markets, namely large bid-ask spreads and the possibility of missing or incoherent prices in the considered data sets. We show that this calibration procedure is significantly more robust and accurate than the standard one based on trade and mid-prices. |
| Keywords: | implied volatility, calibration, bid-ask spread, missing data, data augmentation |
| Date: | 2023–07–20 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03715921&r=rmg |
| By: | Mitsunobu MIYAKE |
| Abstract: | This note elaborates on Luce and Narens' (1978) axiomatic derivation of subjective probability, which generically satisfies the multiplicative law by removing requirement for the derived subjective probability to be non-atomic. For the two original sample spaces, we add the sample space defined by the direct product of the original sample spaces and the sample space of the auxiliary experiment. As a main result, the necessary and sufficient conditions are provided for the likelihood relation on the events of the sample spaces to be represented by the subjective probability satisfying the law with respect to the direct product, allowing atoms in the original sample spaces. |
| Date: | 2024–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:toh:tergaa:481&r=rmg |
| By: | Takuma Kunieda (Kwansei Gakuin University); Akihisa Shibata (Kyoto University) |
| Abstract: | Although many studies in macroeconomics have examined the role of insurance in the presence of income risk, whether aggregate shocks are insurable has not been sufficiently investigated. We present a simple two-period general equilibrium model to show the conditions under which insurance against aggregate shocks works in an economy with constant-elasticity-substitution (CES) production technology and the Greenwood- Hercowitz-Huffman (GHH) utility function (Greenwood et al., 1988). Our theoretical investigation clarifies that only when agents are heterogeneous in their ability or initial wealth can aggregate shocks be insurable. From our quantitative investigation, we find that (i) agents with lower ability enjoy greater welfare improvement from insurance, and as agents’ ability increases, the welfare improvement diminishes, (ii) agents enjoy greater welfare improvement when the damage from disasters is more severe and when the frequency of disasters is greater, and (iii) although the welfare improvement increases as agents’initial wealth increases, the impact of a difference in agents' initial wealth on the difference in the contribution of insurance is very moderate. |
| Keywords: | aggregate shocks, heterogeneous agents, state-contingent claims, incomplete market |
| JEL: | D52 G12 |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:kyo:wpaper:1102&r=rmg |
| By: | Matteo Rizzato (Advestis); Julien Wallart (Fujitsu Systems Europe); Christophe Geissler (Advestis); Nicolas Morizet (Advestis); Noureddine Boumlaik |
| Abstract: | The finance industry is producing an increasing amount of datasets that investment professionals can consider to be influential on the price of financial assets. These datasets were initially mainly limited to exchange data, namely price, capitalization and volume. Their coverage has now considerably expanded to include, for example, macroeconomic data, supply and demand of commodities, balance sheet data and more recently extra-financial data such as ESG scores. This broadening of the factors retained as influential constitutes a serious challenge for statistical modeling. Indeed, the instability of the correlations between these factors makes it practically impossible to identify the joint laws needed to construct scenarios. Fortunately, spectacular advances in Deep Learning field in recent years have given rise to GANs. GANs are a type of generative machine learning models that produce new data samples with the same characteristics as a training data distribution in an unsupervised way, avoiding data assumptions and human induced biases. In this work, we are exploring the use of GANs for synthetic financial scenarios generation. This pilot study is the result of a collaboration between Fujitsu and Advestis and it will be followed by a thorough exploration of the use cases that can benefit from the proposed solution. We propose a GANs-based algorithm that allows the replication of multivariate data representing several properties (including, but not limited to, price, market capitalization, ESG score, controversy score, . . .) of a set of stocks. This approach differs from examples in the financial literature, which are mainly focused on the reproduction of temporal asset price scenarios. We also propose several metrics to evaluate the quality of the data generated by the GANs. This approach is well fit for the generation of scenarios, the time direction simply arising as a subsequent (eventually conditioned) generation of data points drawn from the learned distribution. Our method will allow to simulate high dimensional scenarios (compared to ≲ 10 features currently employed in most recent use cases) where network complexity is reduced thanks to a wisely performed feature engineering and selection. Complete results will be presented in a forthcoming study. |
| Keywords: | Data Augmentation, Financial Scenarios, Risk Management, Generative Adversarial Networks |
| Date: | 2023–08 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03716692&r=rmg |
| By: | Karimov, Nodirbek; Kara, Alper; Downing, Gareth; Marqués-Ibáñez, David |
| Abstract: | We examine rating behaviour after the introduction of new regulations regarding Credit Rating Agencies (CRAs) in the European securitisation market. Employing a large sample of 12, 469 ABS tranches issued between 1998 and 2018, we examine the information content of yield spreads of ABS at the issuance and compare the pre- and post-GFC periods. We find that the regulatory changes have been effective in tackling conflicts of interest between issuers and CRAs in securitisation. Rating catering seems to have disappeared in the post-GFC period. Yet we see limited effectiveness on rating shopping. It follows that rating over-reliance might be an issue, especially for investors of higher-quality ABS. JEL Classification: G21, G28 |
| Keywords: | asset-backed securities, credit rating agencies, Europe, rating catering, rating inflation, rating shopping, securitisation |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20242920&r=rmg |
| By: | Annalisa Frigo (Bank of Italy); Andrea Venturini (Bank of Italy) |
| Abstract: | In light of increased natural risks, this work studies the demand by firms for insurance against the physical risks posed by climate change. This analysis is based on the Invind sample, a survey conducted by the Bank of Italy on a sample of Italian companies in 2021. The questions relating to insurance coverage are cross-referenced with the characteristics of the company and the riskiness of their territory, geolocating the companies' production plants and exploiting the granularity of the climatological maps provided by the Euro-Mediterranean Center on Climate Change and the seismic risk maps of the National Institute of Geophysics and Volcanology. The empirical analysis suggests that exposure to seismic risk is positively associated with the demand for insurance policies against natural and climate damage, with heterogeneous effects on companies operating in industry and services. On the other hand, no evidence emerges that the potential impacts from climate change contribute to demand for insurance. |
| Keywords: | insurance, climate, natural disasters |
| JEL: | G22 Q54 |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:opques:qef_830_24&r=rmg |
| By: | Haim Shalit (BGU) |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:bgu:wpaper:2401&r=rmg |
| By: | Fabien Le Floc'h |
| Abstract: | We present closed analytical approximations for the pricing of Asian options with discrete averaging under the Black-Scholes model with time-dependent parameters. The formulae are obtained by using a stochastic Taylor expansion around a log-normal proxy model and are found to be highly accurate in practice. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.17684&r=rmg |