Potential Future Exposure Research Articles

Jump-at-Default Exposure Modeling with Physical Collateral

Jump-at-default exposure modeling seeks to capture the impact of hard (or systemic) wrong-way risk on counterparty credit exposure. In this work, the authors extend the jump-at-default framework to deal with embedded collateral optionality. In order to ensure a consistent treatment of the value jumps that may affect portfolio and/or collateral basket during close-out, Puetter and Renzitti propose a simple regression-based Monte Carlo approach to project these jumps. They illustrate its impact on potential future exposure and selected valuation adjustments through basic examples.

Measuring systemic risk contribution: A higher-order moment augmented approach

Marginal contributions of individual institutions to the systemic risk contain predictive power for potential future exposures and provide early warning signals to regulators and the public. In this paper, the higher-order co-skewness and co-kurtosis are used to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

P25-18: Precision Farming – Visions for a potential future exposure assessment

P25-18: Precision Farming – Visions for a potential future exposure assessment

Potential Future Exposure of Target Redemption Forwards

We calculate the PFE of TARFs using both a smile aware Heston model and a smile unaware Garman-Kohlhagen (GK) model. Results show that the FX Heston model tends to produce significantly different PFEs than the GK model, highlighting the importance for a dealer's global simulation model to accurately capture 1) the distribution of the underlying spot rate, and 2) the joint distribution of the underlying and its implied volatility surface.

Evaluating risk and stress-testing challenges for central counterparties

This paper discusses the types of challenges that stakeholders face in evaluating risk and margin models for central counterparties (CCP). Two important tools to evaluate the adequacy of CCP resources to cover potential future exposures are back-testing and stress-testing. The paper discusses challenges when designing and analysing back-tests, including the use of unit tests and the inclusion of add-ons. The design and specificities of supervisory stress-testing are covered with a particular focus on liquidity and concentration risk.

Pengaruh faktor kerentanan fisik dan sosial akibat erupsi gunung merapi terhadap ketercapaian sdgs dan irbi

The purpose of this study was to identify the influence of physical and social vulnerability factors due to the eruption of Mount Merapi in Yogyakarta. Furthermore, this study identifies the significance of physical and social vulnerability to the achievement of the SDGs in Yogyakarta. The achievement of SDGs number 11 is supported by indicators of the percentage of the number of dead or missing victims to the total number of disaster events, disaster risk index, regional resilience index, number of resilient villages/kelurahan that have been formed, integrated early warning system, and availability of contingency plans. The achievement of SDGs number 13 is further explained using a simple indicator, namely greenhouse gas emissions. In this case, the research contribution is expected to add an indicator explaining the achievement of SDGs number 13 in addition to climate change. This study uses IRBI (Indonesian Disaster Risk Index) data to indicate the achievement of disaster risk reduction in the research area. The achievement of the SDGs, which has increased every year, certainly has positive implications for handling vulnerable groups. The location of this research study is the District of Turi. Turi sub-district is important to be studied considering the current conditions and potential future exposure to the eruption of Mount Merapi. This study utilizes the Analytical Hierarchy Process (AHP) technique to determine the weight of the indicators used in vulnerability modeling. The research data were tested using statistical analysis of data normality, probabilistic Poison, and the two-way ANOVA effect test. The results showed that there was no influence of the variables of physical vulnerability and social vulnerability on the existing SDGs. The conclusion of the research on the effect of physical and social vulnerability factors on the eruption of Mount Merapi Volcano shows that there is no influence of the value of physical and social vulnerability in Turi District on the achievement of SDGs in the form of the DIY Disaster Risk Index

Quantum computing for financial risk measurement

Quantum computing allows a significant speed-up over traditional CPU- and GPU-based algorithms when applied to particular mathematical challenges such as optimisation and simulation. Despite promising advances and extensive research in hard- and software developments, currently available quantum systems are still largely limited in their capability. In line with this, practical applications in quantitative finance are still in their infancy. This paper analyses requirements and concrete approaches for the application to risk management in a financial institution. On the examples of Value-at-Risk for market risk and Potential Future Exposure for counterparty credit risk, the main contribution lies in going beyond textbook illustrations and instead exploring must-have model features and their quantum implementations. While conceptual solutions and small-scale circuits are feasible at this stage, the leap needed for real-life applications is still significant. In order to build a usable risk measurement system, the hardware capacity—measured in number of qubits—would need to increase by several magnitudes from their current value of about 10^2. Quantum noise poses an additional challenge, and research into its control and mitigation would need to advance in order to render risk measurement applications deployable in practice. Overall, given the maturity of established classical simulation-based approaches that allow risk computations in reasonable time and with sufficient accuracy, the business case for a move to quantum solutions is not very strong at this point.

Fast Calculation of Potential Future Exposure and XVA Sensitivities Using Fourier Series Expansion

Fast Calculation of Potential Future Exposure and XVA Sensitivities Using Fourier Series Expansion

Potential Future Exposure of Target Redemption Forwards

Potential Future Exposure of Target Redemption Forwards

The joint roadmap for radiation protection research: outreach and future

During the last decade there has been remarkable integration of radiation protection research in Europe, driven by six research platforms. The platforms are associations of research centres, university research groups and funding bodies in Member States that are dedicated to specialised areas of research in radiation protection, such as health risks (MELODI), radioecology (ALLIANCE), radiological emergencies (NERIS), dosimetry (EURADOS), medical use of radiation (EURAMED) and societal aspects (SHARE). Recently these platforms established an umbrella organisation MEENAS, to endorse further integration and joint activities in research, education and training, and infrastructures. A milestone in this process of integration and priority setting was achieved in 2020 when the first edition of the joint roadmap for radiation protection research was finalised. In this paper we describe the various roles for research and development in the radiation protection context, ranging from basic scientific knowledge underpinning the system of protection to research supporting the development and application of international standards and research and development activities needed to ensure safety in radiation practices and in potential exposure scenarios. We describe the process of how the joint roadmap has been developed and how it could be implemented. Finally, we address the need to anticipate potential future exposure scenarios and to systematically consider the impact of emerging technologies and global challenges in the context of radiation protection. The joint roadmap is a living document that needs to be regularly updated to cover both current and potential exposures of humans and the environment.

A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options

In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm (DOS) proposed by Becker, Cheridito, and Jentzen (2019), which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors. Cashflow paths are then created by applying the learned stopping strategy on a new set of realizations of the risk factors. Furthermore, in a second phase the cashflow paths are projected onto the risk factors to obtain approximations of pathwise option values. The regression step is carried out by ordinary least squares as well as neural networks, and it is shown that the latter results in more accurate approximations.The expected exposure is formulated, both in terms of the cashflow paths and in terms of the pathwise option values and it is shown that a simple Monte-Carlo average yields accurate approximations in both cases. The potential future exposure is estimated by the empirical α-percentile.Finally, it is shown that the expected exposures, as well as the potential future exposures can be computed under either, the risk neutral measure, or the real world measure, without having to re-train the neural networks.

Using conditional asymmetry to predict commodity futures prices

Despite decades of studies, there is still no consensus on what type of serial dependence, if any, might be present in risky asset returns. This manuscript provides an empirical study of the prices of energy commodities, gold and copper in the futures markets and demonstrates that, for these assets, the level of asymmetry of asset returns varies through time and can be forecast using past returns. A regime switching model is used to construct a managed futures trading strategy that provides returns that are statistically significant. It is also demonstrated how such model can be used to make probabilistic predictions of commodity prices in futures markets, which can be used to drive value-at-risk and potential future exposure metrics or guide dynamic hedging strategies of commodity price risk.

Total value adjustment for a stochastic volatility model. A comparison with the Black–Scholes model

Since the 2007/2008 financial crisis, the total value adjustment (XVA) should be included when pricing financial derivatives. In the present paper, the derivative values of European and American options have been priced where we take into account counterparty risk. Whereas European and American options considering counterparty risk have already been priced under Black-Scholes dynamics in [1], here the novel contribution is the introduction of stochastic volatility resulting in a Heston stochastic volatility type partial differential equation to be solved. We derive the partial differential equation modeling the XVA when stochastic volatility is assumed. For both European and American options, a linear and a nonlinear problem have been deduced. In order to obtain a numerical solution, suitable and appropriate boundary conditions have been considered. In addition, a method of characteristics for the time discretization combined with a finite element method in the spatial discretization has been implemented. The expected exposure and potential future exposure are also computed to compare the current model with the associated Black–Scholes model.

Speed-up credit exposure calculations for pricing and risk management

We introduce a new method to calculate the credit exposure of European and path-dependent options. The proposed method is able to calculate accurate expected exposure and potential future exposure profiles under the risk-neutral and the real-world measure. A key advantage is that it delivers an accuracy comparable to a full re-evaluation and at the same time it is faster than a regression-based method. The core of the approach is solving a dynamic programming problem by function approximation. This yields a closed-form approximation along the paths together with the option's delta and gamma. The simple structure allows for highly efficient evaluation of the exposures, even for a large number of simulated paths. The approach is flexible in the model choice, payoff profiles and asset classes. We validate the accuracy of the method numerically for three different equity products and a Bermudan interest rate swaption. Benchmarking against the popular least-squares Monte Carlo approach shows that our method is able to deliver a higher accuracy in a faster runtime.

Bias-corrected estimators for the Vasicek model: an application in risk measure estimation

We evaluate the usefulness of bias-correction methods in enhancing the Vasicek model for market risk and counterparty risk management practices. The naive bias-corrected estimator, the Tang and Chen bias-corrected estimator and the Bao et al bias-corrected estimator are selected to be compared against the benchmark least squares (LS) estimator. Our Monte Carlo experiment shows that the bias-corrected estimators substantially reduce the small sample bias of the LS estimator for the Vasicek model and project much more accurate value-at-risk and potential future exposure estimations. Even if the sample length is as long as 30 years, the improvements are still significant, especially for the cases where the mean-reversion parameter is close to zero. The applications to real data further demonstrate that the small sample bias of the LS estimator cannot be ignored and one should consider bias-corrected estimators for the Vasicek model.

Procyclicality of CCP Margin Models: Systemic Problems Need Systemic Approaches

Margin requirements protect a central counterparty (CCP) and its users against potential losses generated by the default of any of its members. They have several components, one of them being the initial margin requirement, which is typically calculated using a market risk model to estimate the potential future exposure of each member's portfolio. By definition, market risk models - whether for centrally cleared or bilateral cleared trades - have to be sensitive to changes in market risk and, as a consequence, when market risk increases, initial margin requirements will tend to increase. After the 2008 crisis, regulators had concerns about this becoming procyclical, in the non-technical sense of amplifying financial stress. As a result, CCPs have put in place different procyclicality mitigation tools. But when the markets are stressed and participants face larger margin calls, like in the recent events of March 2020, interest on the procyclicality of initial margin models seems renewed. In this paper we argue that the focus on initial margin models is misplaced. First, margin calls are largely driven by variation margin, not initial margin. Second, the inherent risk sensitivity of margin models, the stochastic nature of the problem, and the different trade-offs involved, constrain what can be achieved with model calibration. We illustrate why this is the case by empirically testing the performance of standard initial margin models during the recent March 2020 events and quantifying the different trade-offs. Therefore, if procyclicality of IM has been mitigated to the limit of what is practical and prudent, but fragilities in the system persist, how should these be addressed and who is responsible for addressing them? We argue that, since the ultimate objective is to minimize systemic propensities to adverse feedback loops, these questions demand a systemic perspective, focusing on the interactions between participants rather than on a single node.

Hepatitis B Knowledge, Testing, and Vaccination History among Undergraduate Public Health Students in Ghana.

Background Hepatitis B virus (HBV) infection is a serious public health problem in many parts of the world. The risk of acquiring the infection through exposure to blood, semen, and other bodily fluids is highest among health care workers (HCW) including trainees. Ghana is considered a high risk country for HBV; however little is known about the knowledge and prevention practices of the infection in the country. This study assessed the knowledge, testing, and vaccination history of HBV and their related factors among undergraduate public health students of University of Health and Allied Sciences in Ghana. Methods A cross-sectional study was conducted among 226 students using a pretested questionnaire to assess Hepatitis B knowledge, testing, and vaccination history of the students. We performed logistic regression analysis to examine the relationship between Hepatitis B testing and vaccination history and participants' characteristics. Data was analysed using Stata Version 12. Results Majority 169 (73.9%) of the 226 participants studied had moderate knowledge regarding HBV infection. About half 114 (50.4%) of them had never been tested for HBV infection, and 100 (44.2%) had received at least a single dose of Hepatitis B vaccine. The completed vaccination rate among the students was 30.5%. Students in their 2nd year (Adjusted Odds Ratio [AOR]: 3.13; 95% Confidence Interval [CI]: 1.13, 7.52; p<0.011) and those with moderate (AOR: 4.76; 95% CI; 1.35, 16.82; P=0.015) and good (AOR: 5.40; 95% CI: 1.31, 22.36; P=0.020) level of knowledge were more likely to be tested for HBV. With regard to vaccination, females (AOR: 1.85; 95%CI: 1.04-3.29; P=0.037) and regular students (AOR: 0.37; 95%CI: 0.19, 0.70; p=0.002) were associated with receiving the full dose of Hepatitis B vaccine. Conclusion This study highlights the urgent need for continued health education on HBV infection and strategies that ensure that health trainees are screened and fully vaccinated against the infection to prevent potential future exposure to the virus. The students' representative council can organize free HBV testing and vaccination for all fresh students.

Leveraging digital pathology for molecular testing.

Leveraging digital pathology for molecular testing.

A Regional Scale Approach to Assessing Current and Potential Future Exposure to Tidal Inundation in Different Types of Estuaries

Broad scale assessments of impacts associated with sea level rise have mainly been undertaken using ocean water level data from tide gauges located in harbours and ports assuming that these can be applied directly in mapping inundation throughout estuaries. On many coasts, however, exposure to sea level rise comes about through inundation adjacent to rivers and estuaries, in many instances far from the ocean. In this study, we examine the potential impacts of sea level rise within the diverse estuaries of South East Australia. We use an extensive and long-term water level data set, which show that water levels within the different types of estuaries vary from ocean water levels. We map potential inundation scenarios for each estuary using an approach which improves on the commonly used bath tub method by allowing for variation in tidal processes both between and along estuaries. We identify considerable exposure to future sea level rise, and variable suitability of the bath tub method within different estuaries. Exposure is particularly high around tidal lake systems, where reduced tidal ranges have allowed development to occur in relative proximity to present sea level, and around larger coastal rivers, which feature extensive low-lying plains exposed to potential inundation.

Potential future exposure of European land transport infrastructure to rainfall-induced landslides throughout the 21st century

Abstract. In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. In this study, a climate index estimating rainfall patterns which trigger landslides in central Europe is analysed until the end of this century and compared to present-day conditions. The analysis of the potential future development of landslide risk is based on an ensemble of dynamically downscaled climate projections which are driven by the SRES A1B socio-economic scenario. Resulting regional-scale climate change projections across central Europe are concatenated with Europe's road and railway network. Results indicate overall increases of landslide occurrence. While flat terrain at low altitudes exhibits an increase of about 1 more potentially landslide-inducing rainfall period per year until the end of this century, higher elevated regions are more affected and show increases of up to 14 additional periods. This general spatial distribution emerges in the near future (2021–2050) but becomes more pronounced in the remote future (2071–2100). Since largest increases are to be found in Alsace, potential impacts of an increasing amount of landslides are discussed using the example of a case study covering the Black Forest mountain range in Baden-Württemberg by further enriching the climate information with additional geodata. The findings derived are suitable to support political decision makers and European authorities in transport, freight and logistics by offering detailed information on which parts of Europe's ground transport network are at particularly high risk concerning landslide activity.