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on Risk Management |
| By: | Peng Liu; Andreas Tsanakas; Yunran Wei |
| Abstract: | In this paper, we study the risk sharing problem among multiple agents using Lambda value at risk as their preferences under heterogenous beliefs, where the beliefs are represented by several probability measures. We obtain semi-explicit formulas for the inf-convolution of multiple Lambda value at risk under heterogenous beliefs and the explicit forms of the corresponding optimal allocations. To show the interplay among the beliefs, we consider three cases: homogeneous beliefs, conditional beliefs and absolutely continuous beliefs. For those cases, we find more explicit expressions for the inf-convolution, showing the influence of the relation of the beliefs on the inf-convolution. Moreover, we consider the inf-convolution of one Lambda value at risk and a general risk measure, including expected utility, distortion risk measures and Lambda value at risk as special cases, with different beliefs. The expression of the inf-convolution and the form of the optimal allocation are obtained. Finally, we discuss the risk sharing for another definition of Lambda value at risk. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.03147 |
| By: | Ravi Kashyap |
| Abstract: | We have developed a novel risk management measure called the concentration risk indicator (CRI). The CRI has been created to address drawbacks with prevailing methodologies and to supplement existing methods. Modified and adapted from the Herfindahl-Hirschman (HH) index, the CRI can give a single numeric score that can be helpful to evaluate the extent of risks that arise from holding concentrated portfolios. We discuss how the CRI can become an indicator of financial stability at any desired aggregation unit: regional, national or international level. We show how the CRI can be easily applied to insurance risk and to any product portfolio mix. The CRI is particularly applicable to the current facet of the decentralized terrain, wherein the majority of the wealth is restricted to a small number of tokens. We calculate and report the CRI -- along with other risk metrics -- for individual assets and portfolios of crypto assets using a daily data sample from January 01, 2019 until August 10, 2022. The CRI is an example of developing metrics that can useful for sending concise yet powerful messages to the relevant audience. This tactic -- which can be described as marketing the benefits of any product or service by using concepts from multiple disciplines -- of creating new metrics goes further beyond the use of metrics to evaluate marketing efficacy. The simplicity of our metric -- and the intuitive explanations we have provided for the CRI -- makes it straightforward to properly articulate a strong -- clear and positive -- signal as part of marketing campaigns. The development -- and implementation -- of new risk management metrics will have greater impact when a wider rigorous risk management process has been established. We discuss several topics related to bringing about more improved risk management across all types of institutions and assets. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.07271 |
| By: | Covi, Giovanni (Bank of England); Huser, Anne-Caroline (Bank of England) |
| Abstract: | How do interdependent economic shocks impact the financial system and reverberate within it? To model the financial system, we start with a two-sector microstructural model of the financial system that includes banks and insurers. We develop a stress testing methodology that stochastically computes economic profits and losses at banks and insurers following correlated corporate default shocks. Taking into account the feedback and amplification of the initial shock though the financial system, we quantify its impact on firms’ capital positions. This methodology is applied to a very rich panel data set of UK banks and insurers. Our approach enables us to distil the contribution of initial economic shocks and the feedback and amplification mechanisms to extreme tail events. Overall, we find that, since the Covid pandemic (2020–21), the UK financial system has experienced an improvement in both profit expectations and tail losses. Comparing sectoral losses in an extreme stress scenario, we find that insurers are more affected than banks by economic credit and traded risk losses, while fire sale losses affect banks more than insurers. |
| Keywords: | Credit risk portfolio; systemic risk; financial contagion; financial network; system‑wide stress testing |
| JEL: | D85 G21 G32 L14 |
| Date: | 2024–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1081 |
| By: | Sebastien Gallet; Antje Hendricks; Julja Prodani |
| Abstract: | This paper introduces a new framework for integrating dependence on nature (ecosystem services) and the degree of nature degradation in estimations of credit risk-related losses for banks. The framework brings the field of nature-related financial risks forward by proposing a capital-based sensitivity indicator to nature degradation, thereby moving from an “exposure†approach to a “financial risk†approach. This ecosystem service degradation sensitivity indicator (EDSI) shows how much of a bank’s available capital buffer on top of its minimum requirements is lost due to a shock on nature. It enables cross-bank and cross-country comparison of potential financial losses related to nature degradation. Our results indicate that incorporating nature degradation into financial risk estimates adds an important - and currently missing - layer of risk and offers additional differentiation in capital impact among banks and countries. While in this paper the framework uses hypothetical shocks on nature and can therefore only produce comparative sensitivity indicators, upon calibrating a shock on different ecosystem services the framework can be used to stress-test financial institutions’ solvency position. |
| Keywords: | nature degradation; ecosystem services; biodiversity loss; dependence score;financial stability; risk; credit risk losses; Merton model |
| JEL: | G21 G28 Q57 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:814 |
| By: | Michael B. Gordy; Alexander J. McNeil |
| Abstract: | In the spectral backtesting framework of Gordy and McNeil (JBF, 2020) a probability measure on the unit interval is used to weight the quantiles of greatest interest in the validation of forecast models using probability-integral transform (PIT) data. We extend this framework to allow general Lebesgue-Stieltjes kernel measures with unbounded distribution functions, which brings powerful new tests based on truncated location-scale families into the spectral class. Moreover, by considering uniform distribution preserving transformations of PIT values the test framework is generalized to allow tests that are focused on both tails of the forecast distribution. |
| Keywords: | Backtesting; Volatility; Risk management |
| JEL: | C52 G21 G32 |
| Date: | 2024–08–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2024-60 |
| By: | Eguren-Martin, Fernando (SPX Capital); Kösem, Sevim (Bank of England); Maia, Guido (Centre for Macroeconomics and London School of Economics); Sokol, Andrej (Bloomberg LP) |
| Abstract: | We propose a novel approach to extract factors from large data sets that maximise covariation with the quantiles of a target distribution of interest. From the data underlying the Chicago Fed’s National Financial Conditions Index, we build targeted financial conditions indices for the quantiles of future US GDP growth. We show that our indices yield considerably better out-of-sample density forecasts than competing models, as well as insights on the importance of individual financial series for different quantiles. Notably, leverage indicators appear to co-move more with the median of the predictive distribution, while credit and risk indicators are more informative about downside risks. |
| Keywords: | Quantile regression; factor analysis; financial conditions indices; GDP-at-risk |
| JEL: | C32 C38 C53 C58 E37 E44 |
| Date: | 2024–08–06 |
| URL: | https://d.repec.org/n?u=RePEc:boe:boeewp:1084 |
| By: | Achintya Gopal |
| Abstract: | The use of machine learning for statistical modeling (and thus, generative modeling) has grown in popularity with the proliferation of time series models, text-to-image models, and especially large language models. Fundamentally, the goal of classical factor modeling is statistical modeling of stock returns, and in this work, we explore using deep generative modeling to enhance classical factor models. Prior work has explored the use of deep generative models in order to model hundreds of stocks, leading to accurate risk forecasting and alpha portfolio construction; however, that specific model does not allow for easy factor modeling interpretation in that the factor exposures cannot be deduced. In this work, we introduce NeuralFactors, a novel machine-learning based approach to factor analysis where a neural network outputs factor exposures and factor returns, trained using the same methodology as variational autoencoders. We show that this model outperforms prior approaches both in terms of log-likelihood performance and computational efficiency. Further, we show that this method is competitive to prior work in generating realistic synthetic data, covariance estimation, risk analysis (e.g., value at risk, or VaR, of portfolios), and portfolio optimization. Finally, due to the connection to classical factor analysis, we analyze how the factors our model learns cluster together and show that the factor exposures could be used for embedding stocks. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.01499 |
| By: | Nguyen, Huyen; Uzonwanne, Sochima |
| Abstract: | We investigate whether lenders employ sustainability pricing provisions to manage borrowers' environmental risk. Using unexpected negative environmental incidents of borrowers as exogenous shocks that reveal information on environmental risk, we find that lenders manage borrowers' environmental risk by conventional tools such as imposing higher interest rates, utilizing financial and net worth covenants, showing reluctance to refinance, and demanding increased collateral. In contrast, the inclusion of sustainability pricing provisions in loan agreements for high environmental risk borrowers is reduced by 11 percentage points. Our study suggests that sustainability pricing provisions may not primarily serve as risk management tools but rather as instruments to attract demand from institutional investors and facilitate secondary market transactions. |
| Keywords: | bank monitoring, environmental risk, institutional investors, sustainability pricing provisions |
| JEL: | G21 G28 K21 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:iwhdps:301152 |
| By: | Thordur Jonasson; Sheheryar Malik; Kay Chung; Mr. Michael G. Papaioannou |
| Abstract: | This paper presents some sound practices for foreign-currency risk management in developing countries and outlines instruments for managing sovereign debt portfolio currency exposures. Adoption of a debt management strategy with well-defined targets for foreign exchange risk is a critical element of public debt risk management. To this end, public debt managers often need to face with complex strategic and operational matters related to public debt hedging practices, including the use of derivatives. In this context, we highlight the main institutional challenges in the management of foreign exchange risk in sovereign debt portfolios and discuss the overall implementation of a foreign exchange risk-management strategy. |
| Keywords: | exchange rate risk risk management; debt portfolio; public debt manager; currency exposure; risk-management strategy; Government debt management; Debt management; Currencies; Exchange rate risk; Foreign currency debt; Asia and Pacific; Sub-Saharan Africa; Middle East; North Africa; Central Asia; Africa |
| Date: | 2024–08–02 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/167 |
| By: | Gita Gopinath; Josefin Meyer; Carmen Reinhart; Christoph Trebesch |
| Abstract: | Theory suggests that corporate and sovereign bonds are fundamentally different, also because sovereign debt has no bankruptcy mechanism and is hard to enforce. We show empirically that the two assets are more similar than you think, at least when it comes to high-yield bonds over the past 20 years. Based on rich new data we compare risky US corporate bonds (“junk” bonds) to risky emerging market sovereign bonds 2002-2021 (EMBI bonds). Investor experiences in these two asset classes were surprisingly aligned, with (i) similar average excess returns, (ii) similar average risk-return patterns (Sharpe ratios), (iii) a similar default frequency, and (iv) comparable haircuts. A notable difference is that the average default duration is higher for sovereigns. Furthermore, the time profile of bond returns and default events differs. One explanation is that the two markets co-move differently with domestic and global factors. US “junk” bond yields are more closely linked to US market conditions such as US stock market returns, US stock price volatility (VIX), US industrial production, or US monetary policy. |
| Keywords: | Sovereign debt and default, Default Risk, corporate bonds, corporate default, junkbonds, Chapter 11, crisis resolution |
| JEL: | G1 G3 H6 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwwpp:dp2097 |
| By: | Dhiti Osatakul; Shuanming Li; Xueyuan Wu |
| Abstract: | Our paper explores a discrete-time risk model with time-varying premiums, investigating two types of correlated claims: main claims and by-claims. Settlement of the by-claims can be delayed for one time period, representing real-world insurance practices. We examine two premium principles based on reported and settled claims, using recursively computable finite-time ruin probabilities to evaluate the performance of time-varying premiums. Our findings suggest that, under specific assumptions, a higher probability of by-claim settlement delays leads to lower ruin probabilities. Moreover, a stronger correlation between main claims and their associated by-claims results in higher ruin probabilities. Lastly, the premium adjustment principles based on settled claims experience contribute to higher ruin probabilities compared to those based on reported claims experience, assuming all other factors remain constant. Notably, this difference becomes more pronounced when there is a high likelihood of by-claim delays. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.00003 |
| By: | Jon Danielsson; Andreas Uthemann |
| Abstract: | The rapid adoption of artificial intelligence (AI) is transforming the financial industry. AI will either increase systemic financial risk or act to stabilise the system, depending on endogenous responses, strategic complementarities, the severity of events it faces and the objectives it is given. AI's ability to master complexity and respond rapidly to shocks means future crises will likely be more intense than those we have seen so far. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.17048 |
| By: | Patrick Kuiper; Ali Hasan; Wenhao Yang; Yuting Ng; Hoda Bidkhori; Jose Blanchet; Vahid Tarokh |
| Abstract: | The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, these models are only asymptotically valid for large samples. However, since extreme data is by definition scarce, the potential for model misspecification error is inherent to these applications, thus DRO estimators are natural. In order to mitigate over-conservative estimates while enhancing out-of-sample performance, we study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. We study both tractable convex formulations for some problems of interest (e.g. CVaR) and more general neural network based estimators. Both approaches are validated using synthetically generated data, recovering prescribed characteristics, and verifying the efficacy of the proposed techniques. Additionally, the proposed method is applied to a real data set of financial returns for comparison to a previous analysis. We established the proposed model as a novel formulation in the multivariate EVT domain, and innovative with respect to performance when compared to relevant alternate proposals. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.00131 |
| By: | de Bresser, J.;; Knoef, M.;; van Ooijen, R.; |
| Abstract: | There is rising interest in combined insurance products to finance long-term care (LTC) and retirement income. We analyze the market for life care annuities, which combine life annuities and LTC insurance, and examine how reverse mortgages can extend accessibility. These combined retirement finance products offer several benefits, such as reducing adverse selection, enabling consumption smoothing, and enhancing financial well-being at advantaged ages while keeping housing as a savings commitment. Using a discrete choice experiment conducted in a large representative panel in the Netherlands among individuals aged 40 to 66 reveals that 40% would opt for LTC-only annuities – which pay out between 500 and 1250 euros per month when having LTC needs – at market prices regardless of whether the payment mode is a monthly premium or a reverse mortgage. Reverse mortgages as a payment mode increase the demand for more expensive life care annuities by 8%-points. Further, the results show that a well-designed small menu of life care annuities could serve most individuals, with accessibility significantly extended when using reverse mortgages as a funding source. |
| Keywords: | long-term care; life care annuities; reverse mortgages; discrete choice experiment; saving motives; health expectations; |
| JEL: | D14 I13 J14 J18 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:yor:hectdg:24/11 |
| By: | Jamel Saadaoui (University of Paris 8) |
| Abstract: | This note explores the impact of geopolitical relationships between the US and China on the oil price. Using time-varying local projections, my analysis reveals that these dynamic effects are unstable over time. Indeed, these effects have been more observable since the global financial crisis, with China being increasingly perceived as a threat in the U.S. Instability is detected around the onset of the COVID-19 pandemic. During this period, geopolitical risks and political tensions influence oil price at different time horizons. |
| Keywords: | Time-Varying Local Projections, China, Oil Price, Political Relations, Geopolitical Risks, Global Financial Crisis, COVID-19 pandemic |
| JEL: | Q |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:inf:wpaper:2024.13 |