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on Risk Management |
| By: | Bingzhen Geng; Yang Liu; Hongfu Wan |
| Abstract: | Systemic risk is receiving increasing attention in the insurance industry, as these risks can have severe impacts on the entire financial system. In this paper, we propose a multi-dimensional L/'{e}vy process-based renewal risk model with heterogeneous insurance claims, where every dimension indicates a business line of an insurer. We use the systemic expected shortfall (SES) and marginal expected shortfall (MES) defined with a Value-at-Risk (VaR) target level as the measurement of systemic risks. Assuming that all the claim sizes are pairwise asymptotically independent (PAI), we derive asymptotic formulas for the tail probabilities of discounted aggregate claims and total loss, which holds uniformly for all time horizons. We further obtain the asymptotics of the above systemic risk measures. The main technical issues involve the treatment of uniform convergence in the dynamic time setting. Finally, we conduct a Monte Carlo numerical study and verify that our asymptotics are accurate and convenient in computation. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00158 |
| By: | Qian Hui; Tiandong Wang |
| Abstract: | In financial markets marked by inherent volatility, extreme events can result in substantial investor losses. This paper proposes a portfolio strategy designed to mitigate extremal risks. By applying extreme value theory, we evaluate the extremal dependence between stocks and develop a network model reflecting these dependencies. We use a threshold-based approach to construct this complex network and analyze its structural properties. To improve risk diversification, we utilize the concept of the maximum independent set from graph theory to develop suitable portfolio strategies. Since finding the maximum independent set in a given graph is NP-hard, we further partition the network using either sector-based or community-based approaches. Additionally, we use value at risk and expected shortfall as specific risk measures and compare the performance of the proposed portfolios with that of the market portfolio. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.12208 |
| By: | Jascha Alexander; Christian Laudag\'e; J\"orn Sass |
| Abstract: | We address the problem that classical risk measures may not detect the tail risk adequately. This can occur for instance due to the averaging process when computing Expected Shortfall. The current literature proposes a solution, the so-called adjusted Expected Shortfall. This risk measure is the supremum of Expected Shortfalls for all possible levels, adjusted with a function $g$, the so-called target risk profile. We generalize this idea by using other risk measures instead of Expected Shortfall. Therefore, we introduce the concept of general adjusted risk measures. For these the realization of the adjusted risk measure quantifies the minimal amount of capital that has to be raised and injected in a financial position $X$ to ensure that the risk measure is always smaller or equal to the adjustment function $g(p)$ for all levels $p\in[0, 1]$. We discuss a variety of assumptions such that desirable properties for risk measures are satisfied in this setup. From a theoretical point of view, our main contribution is the analysis of equivalent assumptions such that a general adjusted risk measure is positive homogeneous and subadditive. Furthermore, we show that these conditions hold for a bunch of new risk measures, beyond the adjusted Expected Shortfall. For these risk measures, we derive their dual representations. Finally, we test the performance of these new risk measures in a case study based on the S$\&$P $500$. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17676 |
| By: | Giampiero Gallo; Ostap Okhrin; Giuseppe Storti |
| Abstract: | This paper compares the accuracy of tail risk forecasts with a focus on including realized skewness and kurtosis in "additive" and "multiplicative" models. Utilizing a panel of 960 US stocks, we conduct diagnostic tests, employ scoring functions, and implement rolling window forecasting to evaluate the performance of Value at Risk (VaR) and Expected Shortfall (ES) forecasts. Additionally, we examine the impact of the window length on forecast accuracy. We propose model specifications that incorporate realized skewness and kurtosis for enhanced precision. Our findings provide insights into the importance of considering skewness and kurtosis in tail risk modeling, contributing to the existing literature and offering practical implications for risk practitioners and researchers. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13516 |
| By: | Maysam Khodayari Gharanchaei; Reza Babazadeh |
| Abstract: | Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1, 000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.14510 |
| By: | Brice Corgnet (EM - EMLyon Business School, GATE - Groupe d'analyse et de théorie économique - UL2 - Université Lumière - Lyon 2 - ENS LSH - Ecole Normale Supérieure Lettres et Sciences Humaines - CNRS - Centre National de la Recherche Scientifique); Camille Cornand; Nobuyuki Hanaki |
| Abstract: | We design a novel experiment to assess investors' behavioural and physiological reactions to negative tail events. Investors who observed, without suffering from, tail events decreased their bids whereas investors suffering tail losses increased them. However, the increase in bids after tail losses was not observed for those who exhibited no emotional arousal. This suggests that emotions are key in explaining Prospect Theory prediction of risk seeking in the loss domain. |
| Keywords: | tail events, emotions, risk |
| Date: | 2023–09–28 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04228190 |
| By: | Krishan Mohan Nagpal |
| Abstract: | Value at Risk (VaR) and stress testing are two of the most widely used approaches in portfolio risk management to estimate potential market value losses under adverse market moves. VaR quantifies potential loss in value over a specified horizon (such as one day or ten days) at a desired confidence level (such as 95'th percentile). In scenario design and stress testing, the goal is to construct extreme market scenarios such as those involving severe recession or a specific event of concern (such as a rapid increase in rates or a geopolitical event), and quantify potential impact of such scenarios on the portfolio. The goal of this paper is to propose an approach for incorporating prevailing market conditions in stress scenario design and estimation of VaR so that they provide more accurate and realistic insights about portfolio risk over the near term. The proposed approach is based on historical data where historical observations of market changes are given more weight if a certain period in history is "more similar" to the prevailing market conditions. Clusters of market conditions are identified using a Machine Learning approach called Variational Inference (VI) where for each cluster future changes in portfolio value are similar. VI based algorithm uses optimization techniques to obtain analytical approximations of the posterior probability density of cluster assignments (market regimes) and probabilities of different outcomes for changes in portfolio value. Covid related volatile period around the year 2020 is used to illustrate the performance of the proposed approach and in particular show how VaR and stress scenarios adapt quickly to changing market conditions. Another advantage of the proposed approach is that classification of market conditions into clusters can provide useful insights about portfolio performance under different market conditions. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.18970 |
| By: | Pascal K\"undig; Fabio Sigrist |
| Abstract: | We introduce a novel machine learning model for credit risk by combining tree-boosting with a latent spatio-temporal Gaussian process model accounting for frailty correlation. This allows for modeling non-linearities and interactions among predictor variables in a flexible data-driven manner and for accounting for spatio-temporal variation that is not explained by observable predictor variables. We also show how estimation and prediction can be done in a computationally efficient manner. In an application to a large U.S. mortgage credit risk data set, we find that both predictive default probabilities for individual loans and predictive loan portfolio loss distributions obtained with our novel approach are more accurate compared to conventional independent linear hazard models and also linear spatio-temporal models. Using interpretability tools for machine learning models, we find that the likely reasons for this outperformance are strong interaction and non-linear effects in the predictor variables and the presence of large spatio-temporal frailty effects. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.02846 |
| By: | Roba Bairakdar; Debbie Dupuis; Melina Mailhot |
| Abstract: | Insurance risk arising from catastrophes such as earthquakes a component of the Minimum Capital Test for federally regulated property and casualty insurance companies. Analyzing earthquake insurance risk requires well-fitted spatio-temporal point process models. Given the spatial heterogeneity of earthquakes, the ability to assess whether the fits are adequate in certain locations is crucial in obtaining usable models. Accordingly, we extend the use of Voronoi residuals to calculate deviance Voronoi residuals. We also create a simulation-based approach, in which losses and insurance claim payments are calculated by relying on earthquake hazard maps of Canada. As an alternative to the current guidelines of OSFI, a formula to calculate the country-wide minimum capital test is proposed based on the correlation between the provinces. Finally, an interactive web application is provided which allows the user to simulate earthquake damage and the resulting financial losses and insurance claims, at a chosen epicenter location. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.04369 |
| By: | Zeda Xu; John Liechty; Sebastian Benthall; Nicholas Skar-Gislinge; Christopher McComb |
| Abstract: | Volatility, which indicates the dispersion of returns, is a crucial measure of risk and is hence used extensively for pricing and discriminating between different financial investments. As a result, accurate volatility prediction receives extensive attention. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its succeeding variants are well established models for stock volatility forecasting. More recently, deep learning models have gained popularity in volatility prediction as they demonstrated promising accuracy in certain time series prediction tasks. Inspired by Physics-Informed Neural Networks (PINN), we constructed a new, hybrid Deep Learning model that combines the strengths of GARCH with the flexibility of a Long Short-Term Memory (LSTM) Deep Neural Network (DNN), thus capturing and forecasting market volatility more accurately than either class of models are capable of on their own. We refer to this novel model as a GARCH-Informed Neural Network (GINN). When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination ($R^2$), Mean Squared Error (MSE), and Mean Absolute Error (MAE). |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.00288 |
| By: | Mengshuo Zhao; Chuancun Yin |
| Abstract: | This paper derives the best- and worst-case GlueVaR distortion risk measure within a unified framework, based on partial information of the underlying distributions and shape information such as symmetry. In addition, we characterize the extremal distributions of GlueVaR with convex envelopes of the corresponding distortion functions. As examples, extremal cases of VaR, TVaR and RVaR are derived. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.19902 |
| By: | Marek Rutkowski; Huansang Xu |
| Abstract: | In this paper, we explore the pricing and hedging strategies for an innovative insurance product called the equity protection swap(EPS). Notably, we focus on the application of EPSs involving cross-currency reference portfolios, reflecting the realities of investor asset diversification across different economies. The research examines key considerations regarding exchange rate fluctuations, pricing and hedging frameworks, in order to satisfy dynamic requirements from EPS buyers. We differentiate between two hedging paradigms: one where domestic and foreign equities are treated separately using two EPS products and another that integrates total returns across currencies. Through detailed analysis, we propose various hedging strategies with consideration of different types of returns - nominal, effective, and quanto - for EPS products in both separate and aggregated contexts. The aggregated hedging portfolios contain basket options with cross-currency underlying asset, which only exists in the OTC market, thus we further consider a superhedging strategy using single asset European options for aggregated returns. A numerical study assesses hedging costs and performance metrics associated with these hedging strategies, illuminating practical implications for EPS providers and investors engaged in international markets. We further employ Monte Carlo simulations for the basket option pricing, together with two other approximation methods - geometric averaging and moment matching. This work contributes to enhancing fair pricing mechanisms and risk management strategies in the evolving landscape of cross-currency financial derivatives. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.19387 |
| By: | Azhari Amine (FSJES Agadir) |
| Abstract: | Résumé Cette étude explore l'impact de l'intégration de l'intelligence artificielle sur la qualité de l'information comptable. En s'appuyant sur un échantillon de 86 observations auprès de professionnels du domaine de la comptabilité, et en utilisant des modèles d'équations structurelles, nous avons analysé comment l'IA améliore la prévision financière et la gestion des risques, contribuant ainsi à la précision et à la fiabilité des informations comptables. Les résultats montrent que la prévision financière assistée par l'IA permet une anticipation plus précise des flux de trésorerie et des besoins en financement, tout en optimisant la planification stratégique. De plus, la gestion des risques optimisée par l'IA améliore la détection des anomalies et l'évaluation proactive des risques, renforçant la stabilité financière des entreprises. Ces découvertes soulignent l'importance stratégique de l'IA dans la comptabilité moderne, tout en mettant en évidence les défis liés à son adoption, tels que la formation continue et la sécurité des données. Nos conclusions fournissent des recommandations pratiques pour les entreprises souhaitant intégrer ces technologies et ouvrent la voie à de futures recherches sur l'impact de l'IA en comptabilité. Mots clés : Intelligence artificielle, Prévision financière, Gestion des risques, Modèles d'équations structurelles, Qualité de l'information comptable Abstract This study explores the impact of integrating artificial intelligence (AI) on the quality of accounting information. Based on a sample of 86 observations from professionals in the accounting field, and using structural equation modeling (SEM), we analyzed how AI enhances financial forecasting and risk management, contributing to the accuracy and reliability of accounting information. The results show that AI-assisted financial forecasting allows for more precise anticipation of cash flows and financing needs, while optimizing strategic planning. Additionally, AI-optimized risk management improves anomaly detection and proactive risk assessment, strengthening companies' financial stability. These findings highlight the strategic importance of AI in modern accounting, while also addressing challenges related to its adoption, such as continuous training and data security. Our conclusions provide practical recommendations for companies looking to integrate these technologies and pave the way for future research on AI's impact on accounting. Keywords: Artificial intelligence, Financial forecasting, Risk management, Structural equation modeling, Quality of accounting information |
| Abstract: | Résumé Cette étude explore l'impact de l'intégration de l'intelligence artificielle sur la qualité de l'information comptable. En s'appuyant sur un échantillon de 86 observations auprès de professionnels du domaine de la comptabilité, et en utilisant des modèles d'équations structurelles, nous avons analysé comment l'IA améliore la prévision financière et la gestion des risques, contribuant ainsi à la précision et à la fiabilité des informations comptables. Les résultats montrent que la prévision financière assistée par l'IA permet une anticipation plus précise des flux de trésorerie et des besoins en financement, tout en optimisant la planification stratégique. De plus, la gestion des risques optimisée par l'IA améliore la détection des anomalies et l'évaluation proactive des risques, renforçant la stabilité financière des entreprises. Ces découvertes soulignent l'importance stratégique de l'IA dans la comptabilité moderne, tout en mettant en évidence les défis liés à son adoption, tels que la formation continue et la sécurité des données. Nos conclusions fournissent des recommandations pratiques pour les entreprises souhaitant intégrer ces technologies et ouvrent la voie à de futures recherches sur l'impact de l'IA en comptabilité. Mots clés : Intelligence artificielle, Prévision financière, Gestion des risques, Modèles d'équations structurelles, Qualité de l'information comptable Abstract This study explores the impact of integrating artificial intelligence (AI) on the quality of accounting information. Based on a sample of 86 observations from professionals in the accounting field, and using structural equation modeling (SEM), we analyzed how AI enhances financial forecasting and risk management, contributing to the accuracy and reliability of accounting information. The results show that AI-assisted financial forecasting allows for more precise anticipation of cash flows and financing needs, while optimizing strategic planning. Additionally, AI-optimized risk management improves anomaly detection and proactive risk assessment, strengthening companies' financial stability. These findings highlight the strategic importance of AI in modern accounting, while also addressing challenges related to its adoption, such as continuous training and data security. Our conclusions provide practical recommendations for companies looking to integrate these technologies and pave the way for future research on AI's impact on accounting. Keywords: Artificial intelligence, Financial forecasting, Risk management, Structural equation modeling, Quality of accounting information |
| Keywords: | Intelligence artificielle, Prévision financière, Gestion des risques, Modèles d'équations structurelles, Qualité de l'information comptable, African Scientific Journal, Intelligence artificielle Prévision financière Gestion des risques Modèles d'équations structurelles Qualité de l Artificial intelligence Financial forecasting Risk management Structural equation modeling Quality of accounting information, Qualité de l Artificial intelligence, Financial forecasting, Risk management, Structural equation modeling, Quality of accounting information |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04694939 |
| By: | Bingyao Liu; Iris Li; Jianhua Yao; Yuan Chen; Guanming Huang; Jiajing Wang |
| Abstract: | This paper takes the graph neural network as the technical framework, integrates the intrinsic connections between enterprise financial indicators, and proposes a model for enterprise credit risk assessment. The main research work includes: Firstly, based on the experience of predecessors, we selected 29 enterprise financial data indicators, abstracted each indicator as a vertex, deeply analyzed the relationships between the indicators, constructed a similarity matrix of indicators, and used the maximum spanning tree algorithm to achieve the graph structure mapping of enterprises; secondly, in the representation learning phase of the mapped graph, a graph neural network model was built to obtain its embedded representation. The feature vector of each node was expanded to 32 dimensions, and three GraphSAGE operations were performed on the graph, with the results pooled using the Pool operation, and the final output of three feature vectors was averaged to obtain the graph's embedded representation; finally, a classifier was constructed using a two-layer fully connected network to complete the prediction task. Experimental results on real enterprise data show that the model proposed in this paper can well complete the multi-level credit level estimation of enterprises. Furthermore, the tree-structured graph mapping deeply portrays the intrinsic connections of various indicator data of the company, and according to the ROC and other evaluation criteria, the model's classification effect is significant and has good "robustness". |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.17909 |
| By: | Diana Barro (Dept. of Economics, University of Venice); Antonella Basso (Dept. of Economics, University of Venice); Stefania Funari (Dept. of Management, University of Venice); Guglielmo Alessandro Visentin (Dept. of Management, University of Venice) |
| Abstract: | In the last decades, financial markets have experienced great uncertainty that has led investors to look for alternative assets to further diversify their portfolios. Art and collectibles fall under such category, and there is a lively debate among academics and practitioners regarding the role of art in financial markets and portfolio choices. For such reason, we investigate the financial characteristics of the art market, and build a portfolio diversified with art to asses whether it outperforms, in terms of risk and return, portfolios which do not include art. We show that art performs well compared with standard and other alternative investments with which art is low correlated. In addition, we find that art returns follow an untypical seasonal pattern, and that much of the volatility in the art market can be attributed to the seasonal component of the time series. Finally, the results of the portfolio optimization analysis indicate that art enters efficient portfolios, and when additional constraints are implemented into the classical mean-variance optimization model, to account for investors’ preferences for liquidity, portfolios which include art still perform well. |
| Keywords: | Art investment, Alternative assets, Portfolio diversification, STL decomposition, Mean-variance optimization |
| JEL: | C22 G11 Z11 |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:vnm:wpdman:205 |
| By: | John Armstrong; George Tatlow |
| Abstract: | We train neural networks to learn optimal replication strategies for an option when two replicating instruments are available, namely the underlying and a hedging option. If the price of the hedging option matches that of the Black--Scholes model then we find the network will successfully learn the Black-Scholes gamma hedging strategy, even if the dynamics of the underlying do not match the Black--Scholes model, so long as we choose a loss function that rewards coping with model uncertainty. Our results suggest that the reason gamma hedging is used in practice is to account for model uncertainty rather than to reduce the impact of transaction costs. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13567 |
| By: | Pradeep Dubey; Siddhartha Sahi; Guanyang Wang |
| Abstract: | We give examples of situations -- stochastic production, military tactics, corporate merger -- where it is beneficial to concentrate risk rather than to diversify it, that is, to put all eggs in one basket. Our examples admit a dual interpretation: as optimal strategies of a single player (the `principal') or, alternatively, as dominant strategies in a non-cooperative game with multiple players (the `agents'). The key mathematical result can be formulated in terms of a convolution structure on the set of increasing functions on a Boolean lattice (the lattice of subsets of a finite set). This generalizes the well-known Harris inequality from statistical physics and discrete mathematics; we give a simple self-contained proof of this result, and prove a further generalization based on the game-theoretic approach. |
| Date: | 2024–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2403.15957 |
| By: | Shuaiyu Chen; T. Clifton Green; Huseyin Gulen; Dexin Zhou |
| Abstract: | We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance similar to humans. LLM forecasts appear optimistic relative to historical and future realized returns. When prompted for 80% confidence interval predictions, LLM responses are better calibrated than survey evidence but are pessimistic about outliers, leading to skewed forecast distributions. The findings suggest LLMs manifest common behavioral biases when forecasting expected returns but are better at gauging risks than humans. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.11540 |
| By: | Zhaobo Zhu (Audencia Business School); Licheng Sun (Old Dominion University, Strome College of Business) |
| Abstract: | Motivated by the implication of return extrapolation models that a joint consideration of past price changes and firm fundamentals could efficiently identify stock mispricing, we propose an integrated approach that combines fundamental and technical information.This integrated approach generates substantial economic gains, which are comparable to those of strategies double-sorted on characteristics related to high turnover and trading costs and state-of-the-art machine learning strategies in existing studies. The performance net of transaction costs is still attractive. Simple transaction cost mitigation approaches could further enhance the performance of the integrated approach by reducing portfolio turnover. Consistent with behavioral models, limits to arbitrage and information asymmetry play a significant role in explaining the super performance of this integrated approach. |
| Keywords: | Fundamental Analysis, Technical Analysis, Arbitrage Risk, Informed Trading |
| Date: | 2024–09–19 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-04703041 |