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on Risk Management |
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Issue of 2024‒07‒08
33 papers chosen by |
| By: | Christiane Baumeister; Florian Huber; Massimiliano Marcellino |
| Abstract: | The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024. |
| JEL: | C11 C32 C53 Q41 Q47 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32524&r= |
| By: | Shivam Patel; Vivek Borkar |
| Abstract: | Risk sensitive decision making finds important applications in current day use cases. Existing risk measures consider a single or finite collection of random variables, which do not account for the asymptotic behaviour of underlying systems. Conditional Value at Risk (CVaR) is the most commonly used risk measure, and has been extensively utilized for modelling rare events in finite horizon scenarios. Naive extension of existing risk criteria to asymptotic regimes faces fundamental challenges, where basic assumptions of existing risk measures fail. We present a complete simulation based approach for sequentially computing Asymptotic CVaR (ACVaR), a risk measure we define on limiting empirical averages of markovian rewards. Large deviations theory, density estimation, and two-time scale stochastic approximation are utilized to define a 'tilted' probability kernel on the underlying state space to facilitate ACVaR simulation. Our algorithm enjoys theoretical guarantees, and we numerically evaluate its performance over a variety of test cases. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.13513&r= |
| By: | Fernando Acebes; Jos\'e Manuel Gonz\'alez-Varona; Adolfo L\'opez-Paredes; Javier Pajares |
| Abstract: | The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.20679&r= |
| By: | Lei Fan; Justin Sirignano |
| Abstract: | Stochastic differential equation (SDE) models are the foundation for pricing and hedging financial derivatives. The drift and volatility functions in SDE models are typically chosen to be algebraic functions with a small number (less than 5) parameters which can be calibrated to market data. A more flexible approach is to use neural networks to model the drift and volatility functions, which provides more degrees-of-freedom to match observed market data. Training of models requires optimizing over an SDE, which is computationally challenging. For European options, we develop a fast stochastic gradient descent (SGD) algorithm for training the neural network-SDE model. Our SGD algorithm uses two independent SDE paths to obtain an unbiased estimate of the direction of steepest descent. For American options, we optimize over the corresponding Kolmogorov partial differential equation (PDE). The neural network appears as coefficient functions in the PDE. Models are trained on large datasets (many contracts), requiring either large simulations (many Monte Carlo samples for the stock price paths) or large numbers of PDEs (a PDE must be solved for each contract). Numerical results are presented for real market data including S&P 500 index options, S&P 100 index options, and single-stock American options. The neural-network-based SDE models are compared against the Black-Scholes model, the Dupire's local volatility model, and the Heston model. Models are evaluated in terms of how accurate they are at pricing out-of-sample financial derivatives, which is a core task in derivative pricing at financial institutions. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00459&r= |
| By: | Fernando Acebes; David Curto; Juan de Anton; Felix Villafanez |
| Abstract: | Risk management is a fundamental discipline in project management, which includes, among others, quantitative risk analysis. Throughout several years of teaching, we have observed difficulties in students performing Monte Carlo Simulation within the quantitative analysis of risks. This article aims to present MCSimulRisk as a teaching tool that allows students to perform Monte Carlo simulation and apply it to projects of any complexity simply and intuitively. This tool allows for incorporating any uncertainty identified in the project into the model. |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2405.20688&r= |
| By: | Murphy, David |
| Abstract: | Initial margin is typically calculated by applying a risk-sensitive model to a portfolio of derivatives with a counterparty. This paper presents an approach to testing initial margin models based on their predictions of the whole future distribution of returns of the relevant portfolio. This testing methodology is substantially more powerful than the usual “backtesting” approach based on returns in excess of margin estimates. The approach presented also provides a methodology for calibrating margin models via the examination of how test results vary as the model parameters change. We present the results of testing some popular classes of initial margin models for various calibrations. These give some insight into what it is reasonable to expect from an initial margin model. In particular, we find that margin models meet regulators’ expectations that they are accurate around the 99th and 99.5th percentile of returns, but that they do not, for the examples studied, accurately model the far tails. Moreover, different models, all of which meet regulatory expectations, are shown to provide substantially different margin estimates in the far tails. The policy implications of these findings are discussed. |
| Keywords: | backtesting; conditional volatility; filtered volatility; initial margin model; margin model testing; volatility estimation |
| JEL: | F3 G3 |
| Date: | 2023–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:118281&r= |
| By: | Yang Liu; Zhenyu Shen |
| Abstract: | In hedge funds, convex compensation schemes are popular to stimulate a high-profit performance for portfolio managers. In economics, non-monotone risk aversion is proposed to argue that individuals may not be risk-averse when the wealth level is low. Combining these two ingredients, we study the optimal control strategy of the manager in incomplete markets. Generally, we propose a wide class of utility functions, the Piecewise Symmetric Asymptotic Hyperbolic Absolute Risk Aversion (PSAHARA) utility, to model the two ingredients, containing both non-concavity and non-differentiability as some abnormalities. Significantly, we derive an explicit optimal control for the family of PSAHARA utilities. This control is expressed into a unified four-term structure, featuring the asymptotic Merton term and the risk adjustment term. Furthermore, we provide a detailed asymptotic analysis and numerical illustration of the optimal portfolio. We obtain the following key insights: (i) A manager with the PSAHARA utility becomes extremely risk-seeking when his/her wealth level tends to zero; (ii) The optimal investment ratio tends to the Merton constant as the wealth level approaches infinity and the negative Merton constant when the wealth falls to negative infinity, implying that such a manager takes a risk-seeking investment as the wealth falls negatively low; (iii) The convex compensation still induces a great risk-taking behavior in the case that the preference is modeled by SAHARA utility. Finally, we conduct a real-data analysis of the U.S. stock market under the above model and conclude that the PSAHARA portfolio is very risk-seeking and leads to a high return and a high volatility (two-peak Sharpe ratio). |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.00435&r= |
| By: | Francisco González (UNIVERSIDAD DE OVIEDO); José E. Gutiérrez (BANCO DE ESPAÑA); José María Serena (BANCO DE ESPAÑA) |
| Abstract: | This paper analyzes how lending relationships affect firms’ incentives to default, drawing on loan-level data in Spain. We provide new evidence showing that firms first default on loans from less important (“non-main”) banks to preserve their most valuable lending relationships. Our findings also indicate that banks integrate this borrower behavior into their credit risk management because the most important banks within a borrower’s set of lending relationships recognize lower discretionary loan impairments. The results are robust to alternative difference-in-difference (DID) analyses and control for potential bank forbearance, loan characteristics, and a variety of time-varying bank and firm fixed effects. |
| Keywords: | lending relationships, loan default, non-performing loans, loan-loss recognition, forbearance |
| JEL: | G21 G28 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2420&r= |
| By: | Grzegorz Halaj; Ruben Hipp |
| Abstract: | We estimate a structural model derived from the balance sheet identity to evaluate the effects of contagion and common exposure on banks’ capital, which varies endogenously as a function of assets and liabilities. Through a regression approach inspired by the literature on structural vector autoregression, we infer the interdependence of banks’ financial conditions. In this model, contagion can occur through direct exposures, fire sales, and market-based sentiment, while common exposures result from portfolio overlaps. We apply this model to granular balance sheet and interbank exposure data of the Canadian banking market. First, we document that contagion varies over time, with the highest levels around the Great Financial Crisis in 2008 and somewhat lower levels for the pandemic period. Second, we find that since the introduction of Basel III, the relative importance of risks has changed, hinting that sources of systemic risk have changed structurally. Our new framework complements traditional stress-testing exercises focused on single institutions by providing a holistic view of risk transmission. |
| Keywords: | Econometric and statistical methods; Economic models; Financial institutions; Financial stability |
| JEL: | G21 C32 C51 L14 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:24-19&r= |
| By: | Tomohiro Ando; Jushan Bai; Lina Lu; Cindy M. Vojtech |
| Abstract: | We characterize the U.S. interbank liquidity risk network based on a supervisory dataset, using a scenario-based quantile network connectedness approach. In terms of methodology, we consider a quantile vector autoregressive model with unobserved heterogeneity and propose a Bayesian nuclear norm estimation method. A common factor structure is employed to deal with unobserved heterogeneity that may exhibit endogeneity within the network. Then we develop a scenario-based quantile network connectedness framework by accommodating various economic scenarios, through a scenario-based moving average expression of the model where forecast error variance decomposition under a future pre-specified scenario is derived. The methodology is used to study the quantile-dependent liquidity risk network among large U.S. bank holding companies. The estimated quantile liquidity risk network connectedness measures could be useful for bank supervision and financial stability monitoring by providing leading indicators of the system-wide liquidity risk connectedness not only at the median but also at the tails or even under a pre-specified scenario. The measures also help identify systemically important banks and vulnerable banks in the liquidity risk transmission of the U.S. banking system. |
| Keywords: | nuclear norm; Bayesian analysis; scenario-based quantile connectedness; bank supervision; financial stability |
| JEL: | C11 C31 C32 C33 C58 G21 G28 |
| Date: | 2024–04–18 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedbqu:98335&r= |
| By: | Jinxin Xu; Kaixian Xu; Yue Wang; Qinyan Shen; Ruisi Li |
| Abstract: | Financial market risk forecasting involves applying mathematical models, historical data analysis and statistical methods to estimate the impact of future market movements on investments. This process is crucial for investors to develop strategies, financial institutions to manage assets and regulators to formulate policy. In today's society, there are problems of high error rate and low precision in financial market risk prediction, which greatly affect the accuracy of financial market risk prediction. K-means algorithm in machine learning is an effective risk prediction technique for financial market. This study uses K-means algorithm to develop a financial market risk prediction system, which significantly improves the accuracy and efficiency of financial market risk prediction. Ultimately, the outcomes of the experiments confirm that the K-means algorithm operates with user-friendly simplicity and achieves a 94.61% accuracy rate |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.13076&r= |
| By: | Elena Capatina; Gary Hansen; Minchung Hsu |
| Abstract: | This paper compares the impact of long term care (LTC) risk on single and married households and studies the roles played by informal care (IC), consumption sharing within households, and Medicaid in insuring this risk. We develop a life-cycle model where individuals face survival and health risk, including the possibility of becoming highly disabled and needing LTC. Households are heterogeneous in various important dimensions including education, productivity, and the age difference between spouses. Health evolves stochastically. Agents make consumption-savings decisions in a framework featuring an LTC statedependent utility function. We find that household expenditures increase significantly when LTC becomes necessary, but married individuals are well insured against LTC risk due to IC. However, they still hold considerable assets due to the concern for the spouse who might become a widow/widower and can expect much higher LTC costs. IC significantly reduces precautionary savings for middle and high income groups, but interestingly, it encourages asset accumulation among low income groups because it reduces the probability of meanstested Medicaid LTC. |
| Keywords: | Long Term Care, Household Risk, Precautionary Savings, Medicaid |
| JEL: | D91 E21 H31 I10 I38 J14 |
| Date: | 2024–02 |
| URL: | https://d.repec.org/n?u=RePEc:acb:cbeeco:2024-697&r= |
| By: | Elena Capatina (Australian National University, Acton Canberra, Australia); Gary Hansen (UCLA, Department of Economics, Los Angeles, USA); Minchung Hsu (National Graduate Institute for Policy Studies, Tokyo, Japan) |
| Abstract: | This paper compares the impact of long term care (LTC) risk on single and married households and studies the roles played by informal care (IC), consumption sharing within households, and Medicaid in insuring this risk. We develop a life-cycle model where individuals face survival and health risk, including the possibility of becoming highly disabled and needing LTC. Households are heterogeneous in various important dimensions including education, productivity, and the age difference between spouses. Health evolves stochastically. Agents make consumption-savings decisions in a framework featuring an LTC statedependent utility function. We find that household expenditures increase significantly when LTC becomes necessary, but married individuals are well insured against LTC risk due to IC. However, they still hold considerable assets due to the concern for the spouse who might become a widow/widower and can expect much higher LTC costs. IC significantly reduces precautionary savings for middle and high income groups, but interestingly, it encourages asset accumulation among low income groups because it reduces the probability of meanstested Medicaid LTC. |
| Keywords: | Long Term Care, Household Risk, Precautionary Savings, Medicaid, Informal Care |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:ngi:dpaper:23-14&r= |
| By: | Antoine Camous; Alejandro Van der Ghote |
| Abstract: | Classical accounts of financial crises emphasize the joint contribution of extrapolative beliefs and leveraged risk-taking to financial instability. This paper proposes a simple macro-finance framework to evaluate these views. We find a novel interplay between non-rational extrapolation and investment risk-taking that amplifies financial instability relative to a rational expectation benchmark. Furthermore, the analysis provides guidance on the design of cyclical policy intervention. Specifically, extrapolative expectations command tighter financial regulation, irrespective of the regulator's degree of non-rational extrapolation. |
| Keywords: | financial frictions, financial amplifications, diagnostic expectations, financial regulation |
| JEL: | E44 E60 E70 G20 G40 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2023_431v2&r= |
| By: | International Monetary Fund |
| Abstract: | This technical note reviews the functioning and effectiveness of the regulation, supervision, and systemic risk monitoring of investment funds in Japan. It focuses on the requirements that are directly relevant to maintaining financial stability, namely, valuation, segregation and safekeeping of fund assets, liquidity risk management and redemption of fund units. The note also reviews the efficacy with which the authorities: i) analyze and monitor the systemic risk arising from fund management activities in Japan; ii) apply the domestic regulatory framework pertinent to investment funds; and iii) supervise compliance with the regulatory framework. The note sets out a series of recommendations to further strengthen the domestic regulatory, supervisory, and risk monitoring frameworks. |
| Date: | 2024–05–13 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfscr:2024/114&r= |
| By: | Joaquin Garcia-Cabo; Rocio Madera |
| Abstract: | This paper documents the role of unemployment and earnings risk in reconciling evidence in payoff differentials between self-employment and paid-employment. Using Spanish administrative data, we characterize the distribution and dynamics of earnings and document lower and less dispersed earnings in self-employment. We consider alternative hypotheses and highlight the role of lower unemployment risk in self-employment. We decompose earnings risk dynamics by estimating a life-cycle earnings process. Indeed, the self-employed experience lower returns but also face lower volatility and persistence of shocks throughout their life-cycle. Our results challenge the conventional view that self-employment necessarily entails higher risk and highlight that accounting for differences in labor earnings risk is important to reconcile the payoff differentials between self-employment and paid-employment. |
| Keywords: | self-employment, segmented labor markets, earnings risk, income process |
| JEL: | J24 J31 J41 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11136&r= |
| By: | Pana Alves (Banco de España); Carmen Broto (Banco de España); María Gil (Banco de España); Matías Lamas (Banco de España) |
| Abstract: | The residential real estate market has a significant weight in the Spanish economy and its performance is closely linked to that of the financial cycle. In addition, as evidenced by the real estate crisis that began in Spain in 2008, the risks generated in this sector have important implications for financial stability. The development of a framework for the early identification of risks in this market is therefore key. This article presents two complementary tools to meet this objective. The first is a heat map that provides a visual interpretation of risk levels in this market for a wide selection of individual indicators. The second is a synthetic indicator that summarizes the information provided by the individual indicators. This index complements the information of the heat map, since it measures both the intensity of the risks in each period and their composition. Both the heat map and the synthetic indicator suggest that, in recent months, the vulnerabilities that had been accumulating in the housing market since 2021 have somewhat reverted. |
| Keywords: | housing market, early warning indicators, heat map, synthetic index |
| JEL: | R30 R31 G21 G51 C43 |
| Date: | 2023–07 |
| URL: | http://d.repec.org/n?u=RePEc:bde:opaper:2314e&r= |
| By: | Heitor Almeida; Murillo Campello; Luciano I. de Castro; Antonio F. Galvao Jr |
| Abstract: | We develop a dynamic model of firm investment under uncertainty that captures firms’ risk attitude using quantile preferences. The firm maximizes its present value, defined as current profits and investment plus the discounted value of the τ-quantile of its value next period. In our framework, τ ∈ (0, 1) parametrizes the firm’s attitude toward downside risk. The model implies that the firm’s investment policy equates the marginal cost of capital with the τ-quantile of the discounted present value of future marginal profits — investment depends directly on the firm’s risk attitude. We further integrate our model into a “q-theory” of investment. Numerical solutions show how heterogeneity across τ-quantiles impacts the value of the firm and investment decisions. Empirical estimations of the quantile investment model show that the strength of the relation between investment and Tobin’s q increases as downside risk aversion decreases. Estimates of firms’ risk attitude reveal evidence of high levels of downside risk aversion. |
| JEL: | D21 D22 D25 E22 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:32498&r= |
| By: | Hakan Yilmazkuday (Department of Economics, Florida International University) |
| Abstract: | This paper investigates the effects of global geopolitical risk on stock prices of 29 economies by using the local projections method for the monthly period between 1985M1-2023M9. The results show that a positive unit shock of global geopolitical risk (normalized to one standard deviation) reduces stock prices (normalized to one standard deviation) in a statistically significant way by 0.80 in Latvia, 0.71 in China, 0.62 in the Euro Area, 0.50 in Sweden, 0.42 in the United Kingdom, 0.39 in the United States, 0.38 in Switzerland, 0.34 in Israel, 0.28 in Canada, and 0.21 in Denmark in a year following the shock, whereas it increases those only in Iceland by 0.28 that can be used to hedge against any geopolitical risk. Subsample analyses further suggest that the negative effects of the same shock exist in several economies (including the United States, China and Euro Area) during the first half of the sample period that coincides with the geopolitical events that the United States is involved with, whereas they only exist in Russia, Poland, Euro Area and the United Kingdom for the second half of the sample period, suggesting that the Russo-Ukrainian War has mostly affected the stock prices in these nearby economies. It is implied that the geographical location of geopolitical events as well as the countries involved are important indicators to understand the effects of any global geopolitical risk on stock prices. |
| Keywords: | Geopolitical Risk, Stock Prices, Local Projections Method |
| JEL: | G15 G41 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:fiu:wpaper:2407&r= |
| By: | Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample pe- riod from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability, but hardly evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregres- sive (HAR) RV model. We also study an extended model that features the RVs of energy commodities and precious metals. |
| Keywords: | Agricultural commodities, Realized volatility, Multi-task forecasting |
| JEL: | C22 C32 C53 Q11 |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202423&r= |
| By: | Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies, Tokyo, Japan; The Rimini Centre for Economic Analysis); Blessings Majon (National Graduate Institute for Policy Studies, Tokyo, 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 mixtures 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. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:ngi:dpaper:24-03&r= |
| By: | Zongxia Liang; Qi Ye |
| Abstract: | This paper diverges from previous literature by considering the utility maximization problem in the context of investors having the freedom to actively acquire additional information to mitigate estimation risk. We derive closed-form value functions using CARA and CRRA utility functions and establish a criterion for valuing extra information through certainty equivalence, while also formulating its associated acquisition cost. By strategically employing variational methods, we explore the optimal acquisition of information, taking into account the trade-off between its value and cost. Our findings indicate that acquiring earlier information holds greater worth in eliminating estimation risk and achieving higher utility. Furthermore, we observe that investors with lower risk aversion are more inclined to pursue information acquisition. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.09339&r= |
| By: | Yury Lebedev; Arunava Banerjee |
| Abstract: | Binomial trees are widely used in the financial sector for valuing securities with early exercise characteristics, such as American stock options. However, while effective in many scenarios, pricing options with CRR binomial trees are limited. Major limitations are volatility estimation, constant volatility assumption, subjectivity in parameter choices, and impracticality of instantaneous delta hedging. This paper presents a novel tree: Gaussian Recombining Split Tree (GRST), which is recombining and does not need log-normality or normality market assumption. GRST generates a discrete probability mass function of market data distribution, which approximates a Gaussian distribution with known parameters at any chosen time interval. GRST Mixture builds upon the GRST concept while being flexible to fit a large class of market distributions and when given a 1-D time series data and moments of distributions at each time interval, fits a Gaussian mixture with the same mixture component probabilities applied at each time interval. Gaussian Recombining Split Tre Mixture comprises several GRST tied using Gaussian mixture component probabilities at the first node. Our extensive empirical analysis shows that the option prices from the GRST align closely with the market. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.16333&r= |
| By: | Roberto Leon-Gonzalez (National Graduate Institute for Policy Studies, Tokyo, Japan; The Rimini Centre for Economic Analysis); Blessings Majoni (National Graduate Institute for Policy Studies, Tokyo, 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. |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:ngi:dpaper:23-07&r= |
| By: | Henri Fraisse; Christophe Hurlin |
| Abstract: | This note outlines the issues, risks and benefits of machine learning models for the design of internal credit risk assessment models used by banking institutions for the calculation of their own funds requirement ("Credit IRB Approach"). The use of ML models in IRB models is currently marginal. However, it could improve the predictive quality of models and in some cases lead to a reduction in capital requirements. However, ML models face a lack of interpretability that substitution or local approximation methods do not solve. <p> Cette note expose les enjeux, les risques et les avantages des modèles d’apprentissage automatique (« Machine Learning ») pour la conception des modèles internes d’évaluation de risque de crédit utilisés par les établissements bancaires dans le cadre du calcul de leur exigence en fond propre (« Approche IRB Crédit »). L’utilisation des modèles ML dans le cadre des modèles IRB est pour l’instant marginale. Elle pourrait pourtant permettre d’améliorer la qualité prédictive des modèles et dans certains cas conduire à une réduction des exigences de fonds propres. Toutefois les modèles ML se heurtent à un déficit d’interprétabilité que les méthodes par substitution ou d’approximation locale ne résolvent pas. |
| Keywords: | Machine Learning; banking prudential regulation; internal models; regulatory capital; Machine Learning ; réglementation prudentielle bancaire ; modèles internes ; capital réglementaire |
| JEL: | G21 G29 C10 C38 C55 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:decfin:44&r= |
| By: | Roger J. A. Laeven; Emanuela Rosazza Gianin; Marco Zullino |
| Abstract: | We introduce and develop the concepts of Geometric Backward Stochastic Differential Equations (GBSDEs, for short) and two-driver BSDEs. We demonstrate their natural suitability for modeling dynamic return risk measures. We characterize a broad spectrum of associated BSDEs with drivers exhibiting growth rates involving terms of the form $y|\ln(y)|+|z|^2/y$. We investigate the existence, regularity, uniqueness, and stability of solutions for these BSDEs and related two-driver BSDEs, considering both bounded and unbounded coefficients and terminal conditions. Furthermore, we present a GBSDE framework for representing the dynamics of (robust) $L^{p}$-norms and related risk measures. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.09260&r= |
| By: | Samuel Forbes |
| Abstract: | Empirical evidence shows stock returns are often heavy-tailed rather than normally distributed. The $\kappa$-generalised distribution, originated in the context of statistical physics by Kaniadakis, is characterised by the $\kappa$-exponential function that is asymptotically exponential for small values and asymptotically power law for large values. This proves to be a useful property and makes it a good candidate distribution for many types of quantities. In this paper we focus on fitting historic daily stock returns for the FTSE 100 and the top 100 Nasdaq stocks. Using a Monte-Carlo goodness of fit test there is evidence that the $\kappa$-generalised distribution is a good fit for a significant proportion of the 200 stock returns analysed. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.09929&r= |
| By: | Shumiao Ouyang; Hayong Yun; Xingjian Zheng |
| Abstract: | This study explores the risk preferences of Large Language Models (LLMs) and how the process of aligning them with human ethical standards influences their economic decision-making. By analyzing 30 LLMs, we uncover a broad range of inherent risk profiles ranging from risk-averse to risk-seeking. We then explore how different types of AI alignment, a process that ensures models act according to human values and that focuses on harmlessness, helpfulness, and honesty, alter these base risk preferences. Alignment significantly shifts LLMs towards risk aversion, with models that incorporate all three ethical dimensions exhibiting the most conservative investment behavior. Replicating a prior study that used LLMs to predict corporate investments from company earnings call transcripts, we demonstrate that although some alignment can improve the accuracy of investment forecasts, excessive alignment results in overly cautious predictions. These findings suggest that deploying excessively aligned LLMs in financial decision-making could lead to severe underinvestment. We underline the need for a nuanced approach that carefully balances the degree of ethical alignment with the specific requirements of economic domains when leveraging LLMs within finance. |
| Date: | 2024–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2406.01168&r= |
| By: | Joebges, Heike; Herr, Hansjörg; Kellermann, Christian |
| Abstract: | Crypto assets' partial money-like use promotes toxic developments in the financial system. Even though crypto assets might be regarded as close substitutes to traditional money, we show that they lack important functions of money. Traditional fiat money requires several interacting institutions to stabilize its value and regulate its use. In our analysis, we elaborate on the risks associated with the difficulty of setting up regulatory institutions in the crypto sphere and the likelihood of periods of high volatility as well as their repercussions on the traditional financial system due to reciprocal integration. The shift of banking functions into the unregulated area of decentralized finance triggers a new quality of instability in the global financial system with an increasing probability of effects on the real economy. Regulation of crypto assets remains an urgent issue. |
| Keywords: | crypto assets, Bitcoin, stablecoins, financial crisis |
| JEL: | E42 G01 G23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:ipewps:296490&r= |
| By: | Wu, Keyu (University of Zurich); Fehr, Ernst (University of Zurich); Hofland, Sean (Lucerne School of Business); Schonger, Martin (Lucerne School of Business) |
| Abstract: | Ambiguous prospects are ubiquitous in social and economic life, but the psychological foundations of behavior under ambiguity are still not well understood. One of the most robust empirical regularities is the strong correlation between attitudes towards ambiguity and compound risk which suggests that compound risk aversion may provide a psychological foundation for ambiguity aversion. However, compound risk aversion and ambiguity aversion may also be independent psychological phenomena, but what would then explain their strong correlation? We tackle these questions by training a treatment group’s ability to reduce compound to simple risks, and analyzing how this affects their compound risk and ambiguity attitudes in comparison to a control group who is taught something unrelated to reducing compound risk. We find that aversion to compound risk disappears almost entirely in the treatment group, while the aversion towards both artificial and natural sources of ambiguity remain high and are basically unaffected by the teaching of how to reduce compound lotteries. Moreover, similar to previous studies, we observe a strong correlation between compound risk aversion and ambiguity aversion, but this correlation only exists in the control group while in the treatment group it is rather low and insignificant. These findings suggest that ambiguity attitudes are not a psychological relative, and derived from, attitudes towards compound risk, i.e., compound risk aversion and ambiguity aversion do not share the same psychological foundations. While compound risk aversion is primarily driven by a form of bounded rationality – the inability to reduce compound lotteries – ambiguity aversion is unrelated to this inability, suggesting that ambiguity aversion may be a genuine preference in its own right. |
| Keywords: | ambiguity aversion, compound risk aversion, bounded rationality, reduction of compound lotteries |
| JEL: | C91 D01 D91 |
| Date: | 2024–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17032&r= |
| By: | Skjold, Benjamin; Steinkamp, Simon Richard; Connaughton, Colm; Hulme, Oliver J; Peters, Ole |
| Abstract: | Decision theories commonly assume that risk preferences can be expressed as utility functions, which vary from person to person but are stable over time. A recent model from ergodicity economics reveals that if people want their wealth to grow at the fastest rate they need to adjust their utility functions depending on the dynamics of their wealth. Here, we ask whether humans make such adjustments by exposing them to different wealth dynamics. We carried out an experiment in which participants made consequential risky decisions under two different conditions, additive and multiplicative wealth dynamics. We estimated risk aversion parameters separately in the two conditions for each participant, fitting isoelastic functions via hierarchical Bayesian models. In our pre-registered analyses, we found strong evidence for a change in utility function, namely an increase in the risk aversion parameter under the multiplicative condition, as predicted by ergodicity economics. Apart from evidence for a large effect of wealth dynamics, we also recover trait-like differences between participants that persist across the two conditions. Our study introduces a new experimental design and contains two independent replications between pilot data and a larger cohort. Together, these results provide evidence that human risk-taking behaviour is sensitive to the dynamical context in which decisions are made and that long-term wealth maximization is an important explanatory principle. |
| Date: | 2024–05–28 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:c96yd&r= |
| By: | Anish Rai; Buddha Nath Sharma; Salam Rabindrajit Luwang; Md. Nurujjaman; Sushovan Majhi |
| Abstract: | This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices. The TDA analysis shows that $L^1$, $L^2$ norms and Wasserstein distance ($W_D$) of the world leading indices rise abruptly during the crashes, surpassing a threshold of $\mu+4*\sigma$ where $\mu$ and $\sigma$ are the mean and the standard deviation of norm or $W_D$, respectively. Our study identified the stock index crashes of the 2008 financial crisis and the COVID-19 pandemic across continents as EEs. Given that different sectors in an index behave differently, a sector-wise analysis was conducted during the COVID-19 pandemic for the Indian stock market. The sector-wise results show that after the occurrence of EE, we have observed strong crashes surpassing $\mu+2*\sigma$ for an extended period for the banking sector. While for the pharmaceutical sector, no significant spikes were noted. Hence, TDA also proves successful in identifying the duration of shocks after the occurrence of EEs. This also indicates that the Banking sector continued to face stress and remained volatile even after the crash. This study gives us the applicability of TDA as a powerful analytical tool to study EEs in various fields. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.16052&r= |
| By: | Accominotti, Olivier; Albers, Thilo; Oosterlinck, Kim |
| Abstract: | This paper explores how selective default expectations affect the pricing of sovereign bonds in a historical laboratory: the German default of the 1930s. We analyze yield differentials between identical government bonds traded across various creditor countries before and after bond market segmentation. We show that, when secondary debt markets are segmented, a large selective default probability can be priced in bond yield spreads. Selective default risk accounted for one third of the yield spread of German external bonds over the risk-free rate during the 1930s. Selective default expectations arose from differences in the creditor countries’ economic power over the debtor. |
| Keywords: | sovereign risk; debt default; secondary markets; creditor discrimination; The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/Under REA grant agreement 608129; OUP deal |
| JEL: | F34 G12 G15 H63 N24 N44 |
| Date: | 2023–12–04 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:120657&r= |