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on Risk Management |
| By: | Andrea Teruzzi |
| Abstract: | The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.17239&r=rmg |
| By: | Shiva Zamani; Alireza Moslemi Haghighi; Hamid Arian |
| Abstract: | This study delves into the intricate realm of risk evaluation within the domain of specific financial derivatives, notably options. Unlike other financial instruments, like bonds, options are susceptible to broader risks. A distinctive trait characterizing this category of instruments is their non-linear price behavior relative to their pricing parameters. Consequently, evaluating the risk of these securities is notably more intricate when juxtaposed with analogous scenarios involving fixed-income instruments, such as debt securities. A paramount facet in options risk assessment is the inherent uncertainty stemming from first-order fluctuations in the underlying asset's volatility. The dynamic patterns of volatility fluctuations manifest striking resemblances to the interest rate risk associated with zero-coupon bonds. However, it is imperative to bestow heightened attention on this risk category due to its dependence on a more extensive array of variables and the temporal variability inherent in these variables. This study scrutinizes the methodological approach to risk assessment by leveraging the implied volatility surface as a foundational component, thereby diverging from the reliance on a singular estimate of the underlying asset's volatility. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14985&r=rmg |
| By: | Jana Hlavinova; Birgit Rudloff; Alexander Smirnow |
| Abstract: | In recent years, it has become apparent that an isolated microprudential approach to capital adequacy requirements of individual institutions is insufficient. It can increase the homogeneity of the financial system and ultimately the cost to society. For this reason, the focus of the financial and mathematical literature has shifted towards the macroprudential regulation of the financial network as a whole. In particular, systemic risk measures have been discussed as a risk measurement and mitigation tool. In this spirit, we adopt a general approach of multivariate, set-valued risk measures and combine it with the notion of intrinsic risk measures. In order to define the risk of a financial position, intrinsic risk measures utilise only internal capital, which is received when part of the currently held assets are sold, instead of relying on external capital. We translate this methodology into the systemic framework and show that systemic intrinsic risk measures have desirable properties such as the set-valued equivalents of monotonicity and quasi-convexity. Furthermore, for convex acceptance sets we derive a dual representation of the systemic intrinsic risk measure. We apply our methodology to a modified Eisenberg-Noe network of banks and discuss the appeal of this approach from a regulatory perspective, as it does not elevate the financial system with external capital. We show evidence that this approach allows to mitigate systemic risk by moving the network towards more stable assets. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14588&r=rmg |
| By: | Haufler, Andreas; Luelfesmann, Christoph |
| JEL: | G38 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc23:277623&r=rmg |
| By: | St\'ephane Cr\'epey; Noufel Frikha; Azar Louzi; Gilles Pag\`es |
| Abstract: | This article is a follow up to Cr\'epey, Frikha, and Louzi (2023), where we introduced a nested stochastic approximation algorithm and its multilevel acceleration for computing the value-at-risk and expected shortfall of a random financial loss. We establish central limit theorems for the renormalized errors associated with both algorithms and their averaged variations. Our findings are substantiated through numerical examples. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15333&r=rmg |
| By: | Ruslan Tepelyan; Achintya Gopal |
| Abstract: | The use of machine learning to generate synthetic data has grown in popularity with the proliferation of text-to-image models and especially large language models. The core methodology these models use is to learn the distribution of the underlying data, similar to the classical methods common in finance of fitting statistical models to data. In this work, we explore the efficacy of using modern machine learning methods, specifically conditional importance weighted autoencoders (a variant of variational autoencoders) and conditional normalizing flows, for the task of modeling the returns of equities. The main problem we work to address is modeling the joint distribution of all the members of the S&P 500, or, in other words, learning a 500-dimensional joint distribution. We show that this generative model has a broad range of applications in finance, including generating realistic synthetic data, volatility and correlation estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14735&r=rmg |
| By: | Jingyi Tian; Jun Nagayasu |
| Abstract: | As an important domain of information technology development, artificial intelligence (AI) has garnered significant popularity in the financial sector. While AI offers numerous advantages, investigating potential risks associated with the widespread use of AI has become a critical point for researchers. We examine the impact of AI technologies on systemic risk within China’s financial industry. Our findings suggest that AI helps mitigate the increase of systemic risk. However, the impact of AI differs across different financial sectors and is more pronounced during crisis periods. Our study also suggests that AI can decrease systemic risk by enhancing the human capital of financial firms. Moreover, the theoretical framework presented in this paper provides insights into the notion that imprudent allocation of AI-related investment could potentially contribute to an increase in systemic risk. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:toh:tupdaa:44&r=rmg |
| By: | Reis Castigo Intupo |
| Abstract: | In Mozambique there is no evidence of a bankruptcy prediction model developed in the national economic context, yet, back in 2016, the national banking sector suffered a financial shock that resulted in Mozambique Central Bank intervention in two banks (Moza Banco, S.A. and Nosso Banco, S.A.). This was a result of the deterioration of their financial and prudential indicators, although Mozambique had been adhering to the Basel Accords since 1994. The Basel Accords provides recommendations on banking sector supervision worldwide with the aim to enhance financial system stability. While it does not predict bankruptcy, the prediction model can be used as an auxiliary tool to manage that risk, but this has to be built in the national economic context. This paper develops for Mozambique banking sector a bankruptcy prediction model in the Mozambican context through the linear discriminant analyses method, following two assumptions: (i) composition of the sample and (ii) robustness of the financial prediction indicators (the capital structure, profitability asset concentration and asset quality) from 2012 to 2020. The developed model attained an accuracy level of 84% one year before Central Bank intervention (2015) with the entire population of 19 banks of the sector, which makes it recommendable as a risk management tool for this sector. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.16705&r=rmg |
| By: | Mourahib, Anas (Université catholique de Louvain, LIDAM/ISBA, Belgium); Kiriliouk, Anna (UNamur); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | When modeling a vector of risk variables, extreme scenarios are often of special interest. The peaks-over-thresholds method hinges on the notion that, asymptotically, the excesses over a vector of high thresholds follow a multivariate generalized Pareto distribution. However, existing literature has primarily concentrated on the setting when all risk variables are always large simultaneously. In reality, this assumption is often not met, especially in high dimensions. In response to this limitation, we study scenarios where distinct groups of risk variables may exhibit joint extremes while others do not. These discernible groups are derived from the angular measure inherent in the corresponding max-stable distribution, whence the term extreme direction. We explore such extreme directions within the framework of multivariate generalized Pareto distributions, with a focus on their probability density functions in relation to an appropriate dominating measure. Furthermore, we provide a stochastic construction that allows any prespecified set of risk groups to constitute the distribution’s extreme directions. This construction takes the form of a smoothed max-linear model and accommodates the full spectrum of conceivable max-stable dependence structures. Additionally, we introduce a generic simulation algorithm tailored for multivariate generalized Pareto distributions, offering specific implementations for extensions of the logistic and Hüsler–Reiss families capable of carrying arbitrary extreme directions. |
| Date: | 2023–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:aiz:louvad:2023034&r=rmg |
| By: | Afees A. Salisu (Centre for Econometrics & Applied Research, Ibadan, Nigeria; Department of Economics & Department of Economics, University of Pretoria, Private, South Africa); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | This study examines the out-of-sample predictability of expected skewness of oil price returns for stock returns of 10 (8 advanced plus 2 emerging) countries using long-range monthly data of over a century for each country. Using a distributed lag predictive econometric model, which controls for endogeneity, persistence, and conditional heteroscedasticity, we provide evidence of the strong statistical significance of the predictive impact of the third moment of oil price returns for equity returns for all the countries across various forecast horizons and length of out-of-sample periods. These findings also continue to hold for the shorter sample periods of 3 other emerging markets: Brazil, China and Russia. Our findings have important implications for academics, investors and policymakers. |
| Keywords: | Stock returns, expected skewness of oil returns, forecasting, advanced and emerging equity markets |
| JEL: | C22 G15 G17 Q02 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202339&r=rmg |
| By: | Philippe Goulet Coulombe; Mikael Frenette; Karin Klieber |
| Abstract: | We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network which accommodates for various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that breaks mean/variance indeterminacy in this class of overparametrized nonlinear models. Third, we conduct a blocked out-of-bag reality check to curb overfitting in both conditional moments. Fourth, the algorithm utilizes standard deep learning software and thus handles large data sets - both computationally and statistically. Ergo, our Hemisphere Neural Network (HNN) provides proactive volatility forecasts based on leading indicators when it can, and reactive volatility based on the magnitude of previous prediction errors when it must. We evaluate point and density forecasts with an extensive out-of-sample experiment and benchmark against a suite of models ranging from classics to more modern machine learning-based offerings. In all cases, HNN fares well by consistently providing accurate mean/variance forecasts for all targets and horizons. Studying the resulting volatility paths reveals its versatility, while probabilistic forecasting evaluation metrics showcase its enviable reliability. Finally, we also demonstrate how this machinery can be merged with other structured deep learning models by revisiting Goulet Coulombe (2022)'s Neural Phillips Curve. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.16333&r=rmg |
| By: | Paul Labonne; Leif Anders Thorsrud |
| Abstract: | The nonlinear nexus between financial conditions indicators and the conditional distribution of GDP growth has recently been challenged. We show how one can use textual economic news combined with a shallow Neural Network to construct an alternative financial indicator based on word embeddings. By design the index associates growth-at-risk to news about credit, leverage and funding, and we document that the proposed indicator is particularly informative about the lower left tail of the GDP distribution and delivers significantly better out-of-sample density forecasts than commonly used alternatives. Speaking to theories on endogenous information choice and credit-market sentiment we further document that the news-based index likely carries information about beliefs rather than fundamentals. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0125&r=rmg |
| By: | Robert Stok; Paul Bilokon |
| Abstract: | Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06256&r=rmg |
| By: | Eva L\"utkebohmert; Julian Sester; Hongyi Shen |
| Abstract: | Sovereign loan portfolios of Multilateral Development Banks (MDBs) typically consist of only a small number of borrowers and hence are heavily exposed to single name concentration risk. Based on realistic MDB portfolios constructed from publicly available data, this paper quantifies the magnitude of the exposure to name concentration risk using exact Monte Carlo simulations. In comparing the exact adjustment for name concentration risk to its analytic approximation as currently applied by the major rating agency Standard & Poor's, we further investigate whether current capital adequacy frameworks for MDBs are overly conservative. Finally, we discuss the choice of appropriate model parameters and their impact on measures of name concentration risk. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.13802&r=rmg |
| By: | Yan Dolinsky |
| Abstract: | In this work we study the continuous time exponential utility maximization problem in the framework of semi-static hedging. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.17270&r=rmg |
| By: | Emile A. Marin; Sanjay R. Singh |
| Abstract: | Classical contributions in international macroeconomics rely on goods-market mechanisms to reconcile the cyclicality of real exchange rates when financial markets are incomplete. However, cross-border trade in one domestic and one foreign-currency-denominated risk-free asset prohibits these mechanisms from breaking the pattern consistent with complete markets. In this paper, we characterize how goods markets drive exchange rate cyclicality, taking into account trade in risk-free and/or risky assets. We show that goods-market mechanisms come back into play, even when there is cross-border trade in two risk-free assets, as long as we allow for empirically plausible heterogeneity in the stochastic discount factors of domestic marginal investors. |
| Keywords: | risk sharing; incomplete markets; exchange rates |
| JEL: | E32 F31 F44 G15 |
| Date: | 2023–11–30 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedfwp:97453&r=rmg |
| By: | Savvakis C. Savvides (Visiting Lecturer, John Deutsch International Executive Programs, Queens University, Canada.) |
| Abstract: | There are four key areas in a capital investment evaluation that make for a good and sound appraisal of risk and return. The methodology of cost benefit analysis for capital investment projects is of course a prerequisite for a sound appraisal. There is general consensus regarding the practice and application of cost benefit analysis in the appraisal of capital investment projects and in determining economic viability. Although the correct methodology is necessary, it is not always sufficient to facilitate the decision of whether to invest.For a sound investment appraisal, one further needs to use a sound and robust integrated financial model which correctly and prudently applies the methodology of Cost-Benefit Analysis. Secondly, it is also fundamental to structure the projection parts of such a model only after first doing the serious homework on the market and competitive analysis data to be projected in the appraisal. This phase which unfortunately is not given enough attention in practice is essential to reveal the driving parameters and to be projecting growth patterns for key variables in a consistent and coherent manner. A good and thoughtful marketing analysis is also key when subjecting the financial projections to risk analysis using Monte Carlo Simulation. Last but not least, the above analysis should lead to the derivation of the project’s risk profile and how it may impact the various stakeholders and financiers of the project. This facilitates an appropriate agreement for a financing structure and for sharing of the risks among all stakeholders. Each of these aspects of investment appraisal are addressed in this manuscript. |
| Keywords: | cost-benefit analysis, development bank, risk analysis, project evaluation. |
| JEL: | D61 |
| Date: | 2023–12–12 |
| URL: | http://d.repec.org/n?u=RePEc:qed:dpaper:4612&r=rmg |
| By: | Boyang Yu |
| Abstract: | The impact of non-deterministic outputs from Large Language Models (LLMs) is not well examined for financial text understanding tasks. Through a compelling case study on investing in the US equity market via news sentiment analysis, we uncover substantial variability in sentence-level sentiment classification results, underscoring the innate volatility of LLM outputs. These uncertainties cascade downstream, leading to more significant variations in portfolio construction and return. While tweaking the temperature parameter in the language model decoder presents a potential remedy, it comes at the expense of stifled creativity. Similarly, while ensembling multiple outputs mitigates the effect of volatile outputs, it demands a notable computational investment. This work furnishes practitioners with invaluable insights for adeptly navigating uncertainty in the integration of LLMs into financial decision-making, particularly in scenarios dictated by non-deterministic information. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15180&r=rmg |