A dynamic method-of-moments copula model approach for market risk estimates

Participatory Integrated Assessment (PIA) has become a vital tool for decision-making for sustainable development, but it faces significant challenges due to the inherent uncertainty of socio-ecological systems. Uncertainty arises from multiple sources, such as incomplete data, knowledge gaps, and unpredictable events, which can lead to inadequate risk estimations and potentially undermine the effectiveness of environmental planning efforts. To address these challenges, this study proposes a qualitative modeling approach for risk estimation in PIA. The approach employs Decision Making under Deep Uncertainty (DMDU) to combine qualitative insights and information from stakeholders with available quantitative data. It allows for the exploration of alternative future states of the world and the identification of robust scenarios that promote sustainable development. The effectiveness of the proposed approach is demonstrated through the Ecological Ordinance of Yucatán, Mexico, a policy-making tool for multi-sectoral environmental planning. The study shows how qualitative DMDU can identify critical uncertainties and provide insights into regional management strategies. It also emphasizes the importance of stakeholder engagement and transparency in the decision-making process. Overall, this study presents a promising approach for addressing multiple forms of uncertainty in PIA and improving ecological risk estimation for decision-making in complex socio-ecological systems.

The impact of psoriasis on 10-year Framingham risk

Market risk is an important type of financial risk that is usually caused by price fluctuations in financial markets. One determinant of market risk comprises Value at Risk (VaR), which is defined as the maximum loss that can be achieved within a certain time horizon and at a given reliability level. The aim of the article is to determine the importance of selecting conditional volatility model within the parametric and semi-parametric approach for VaR estimation. The results ascertained show that the application of these models tends to provide more accurate predictions of actual losses as compared to traditional approaches to VaR estimates. Overall, the application of conditional volatility models ensures that VaR estimates are more flexible to adapt to changing market conditions – especially in the periods associated with higher return volatility. Furthermore, the results show that the differences between individual models of contingent volatility are primarily determined by selecting the specific distribution of the standardized residue series

Banks typically attempt to quantify climate-related risks, whether physical or transition ones, by adopting a top-down or a bottom-up analytical approach for the risk estimation of their borrowers. The two analytical approaches for risk estimation are regarded as mutually exclusive, when, in reality, they can complement each other in a mutually beneficial way. We discuss the challenges and opportunities of both analytical approaches with a focus on their applicability for commercial banks’ loans, and highlight directions for future research.

As the uncertainty is widely existent in the engineering structure, it is necessary to study the finite element (FE) modeling and updating in consideration of the uncertainty. A FE model updating approach in structural dynamics with interval uncertain parameters is proposed in this work. Firstly, the mathematical relationship between the updating parameters and the output interesting qualities is created based on the copula approach and the vast samples of inputs and outputs are obtained by the Monte Carlo (MC) sampling technology according to the copula model. Secondly, the samples of updating parameters are rechosen by combining the copula model and the experiment intervals of the interesting qualities. Next, 95% confidence intervals of updating parameters are calculated by the nonparameter kernel density estimation (KDE) approach, which is regarded as the intervals of updating parameters. Lastly, the proposed approach is validated in a two degree‐of‐freedom mass‐spring system, simple plates, and the transport mirror system. The updating results evidently demonstrate the feasibility and reliability of this approach.

Probabilistic risk assessments are enjoying increasing popularity as a tool to characterize the health hazards associated with exposure to chemicals in the environment. Because probabilistic analyses provide much more information to the risk manager than standard "point" risk estimates, this approach has generally been heralded as one which could significantly improve the conduct of health risk assessments. The primary obstacles to replacing point estimates with probabilistic techniques include a general lack of familiarity with the approach and a lack of regulatory policy and guidance. This paper discusses some of the advantages and disadvantages of the point estimate vs. probabilistic approach. Three case studies are presented which contrast and compare the results of each. The first addresses the risks associated with household exposure to volatile chemicals in tapwater. The second evaluates airborne dioxin emissions which can enter the food-chain. The third illustrates how to derive health-based cleanup levels for dioxin in soil. It is shown that, based on the results of Monte Carlo analyses of probability density functions (PDFs), the point estimate approach required by most regulatory agencies will nearly always overpredict the risk for the 95th percentile person by a factor of up to 5. When the assessment requires consideration of 10 or more exposure variables, the point estimate approach will often predict risks representative of the 99.9th percentile person rather than the 50th or 95th percentile person. This paper recommends a number of data distributions for various exposure variables that we believe are now sufficiently well understood to be used with confidence in most exposure assessments. A list of exposure variables that may require additional research before adequate data distributions can be developed are also discussed.

The impact of joint events on oil price volatility: Evidence from a dynamic graphical news analysis model

This chapter discusses the empirical management of risks in the banking firm. In terms of methodology, it broaches the assumptions, estimation approaches, and issues of the value at risk and credit-risk measurement models. It also discusses evidence on default or credit risk, liquidity risk, and market risk. Banks should keep sufficient capital as a buffer against unexpected losses. This capital management reflects both economic capital (what banks would voluntarily hold) and regulatory capital (as required by regulators). Financial institutions decide on their on-balance- and off-balance-sheet activities. The off-balance-sheet management is also important for banks as often assets and liabilities are contingent, implying that many risks are not visible on the books but are latently present off the books.

This paper presents some conventional and new measures of market, credit, and liquidity risks for government bonds. These measures are analyzed from the perspective of a sovereign''s debt manager. In particular, it examines duration, convexity, M-square, skewness, kurtosis, and VaR statistics as measures of interest rate exposure; a VaR statistic as the prominent measure of exchange rate exposure; the balance sheet approach (or contingent claims approach), and its consequent probability of default as the most promising measure of credit risk exposure; and an elasticity approach and a VaR statistic to measure liquidity risk. Along with the formulas for the various statistics proposed, we provide simple examples of their application to some common risk valuation cases. Finally, we present an integrated approach for the simultaneous estimation of a portfolio''s interest rate and exchange rate risk using the VaR methodology. The integrated approach is then extended to also include N risk factors. This approach allows us to measure the total risk of a portfolio, provided that the volatilities and correlations among the risk factors can be estimated

Yellow fever is a vector-borne disease endemic in tropical regions of Africa, where 90% of the global burden occurs, and Latin America. It is notoriously under-reported with uncertainty arising from a complex transmission cycle including a sylvatic reservoir and non-specific symptom set. Resulting estimates of burden, particularly in Africa, are highly uncertain. We examine two established models of yellow fever transmission within a Bayesian model averaging framework in order to assess the relative evidence for each model’s assumptions and to highlight possible data gaps. Our models assume contrasting scenarios of the yellow fever transmission cycle in Africa. The first takes the force of infection in each province to be static across the observation period; this is synonymous with a constant infection pressure from the sylvatic reservoir. The second model assumes the majority of transmission results from the urban cycle; in this case, the force of infection is dynamic and defined through a fixed value of R0 in each province. Both models are coupled to a generalised linear model of yellow fever occurrence which uses environmental covariates to allow us to estimate transmission intensity in areas where data is sparse. We compare these contrasting descriptions of transmission through a Bayesian framework and trans-dimensional Markov chain Monte Carlo sampling in order to assess each model’s evidence given the range of uncertainty in parameter values. The resulting estimates allow us to produce Bayesian model averaged predictions of yellow fever burden across the African endemic region. We find strong support for the static force of infection model which suggests a higher proportion of yellow fever transmission occurs as a result of infection from an external source such as the sylvatic reservoir. However, the model comparison highlights key data gaps in serological surveys across the African endemic region. As such, conclusions concerning the most prevalent transmission routes for yellow fever will be limited by the sparsity of data which is particularly evident in the areas with highest predicted transmission intensity. Our model and estimation approach provides a robust framework for model comparison and predicting yellow fever burden in Africa. However, key data gaps increase uncertainty surrounding estimates of model parameters and evidence. As more mathematical models are developed to address new research questions, it is increasingly important to compare them with established modelling approaches to highlight uncertainty in structures and data.

This study aimed to investigate whether the progression risk score (PRS) developed from cytoplasmic immunohistochemistry (IHC) biomarkers is available and applicable for assessing risk and prognosis in oral cancer patients. Participants in this retrospective case-control study were diagnosed between 2012 and 2014 and subsequently underwent surgical intervention. The specimens from surgery were stained by IHC for 16 cytoplasmic target markers. We evaluated the results of IHC staining, clinical and pathological features, progression-free survival (PFS), and overall survival (OS) of 102 oral cancer patients using a novel estimation approach with unsupervised hierarchical clustering analysis. Patients were stratified into high-risk (52) and low-risk (50) groups, according to their PRS; a metric consisting of cytoplasmic PLK1, PhosphoMet, SGK2, and SHC1 expression. Moreover, PRS could be extended for use in the Cox proportional hazard regression model to estimate survival outcomes with associated clinical parameters. Our study findings revealed that the high-risk patients had a significantly increased risk in cancer progression compared with low-risk patients (hazard ratio (HR) = 2.20, 95% confidence interval (CI) = 1.10–2.42, p = 0.026). After considering the influences of demographics, risk behaviors, and tumor characteristics, risk estimation with PRS provided distinct PFS groups for patients with oral cancer (p = 0.017, p = 0.019, and p = 0.020). Our findings support that PRS could serve as an ideal biomarker for clinical use in risk stratification and progression assessment in oral cancer.

Market risk analysis and estimation are presentedin T+ transactionsas they are used within the Moscow Exchange. There is a need to do so as a result of the cut-off of a new REPO product with Central Counterpartner (CCP). Here repurchase agreement goes through the National Clearing Center (NCC), the last being a bank and a clearing structure within the Moscow Exchange group.NCC actsas an intermediary (so called “Central Counterpartner”) between trading participants.REPOs with CCP raisecontractor claims and commitments to the CCP which takes the risk of default on commitments from unfair contract side. The REPO with CCP cut-off made ready a technological platform to implement T+2 trades at the Moscow Exchange. As a result of it there appeared the possibility to enter security purchase/sell contracts partially collateralized. All these transactions (the REPO with CCP, T+) made it a must determining security market risks. The paper is aimed at presenting VaR-like risk estimates. The methods used are from the computer fi nance. Unusual TS rate of return indicator is proposed and applied to find optimal portfolios under the Markowitz approach and their VaRs (losses) forecasts given the real “big” share price data and various horizons. Portfolio extreme rate and loss forecasting is our goal. To this end the forecasts are computed for three horizons (2, 5 and 10 days) and for three significance levels.There were developed R-, Excel- and Bloomberg-basedsoftware tools as needed. The whole range of proposed computing steps and the tables with charts may be considered as candidates to be included in the future market risk standards.Paper results permit capital market participants to choose the correct (as to the required risk level) common stocks.

Market risk model selection and medium-term risk with limited data: Application to ocean tanker freight markets

The value-at-risk (Va) method in market risk management is becoming a benchmark for measuring “market risk” for any financial instrument. The present study aims at examining which VaR model best describes the risk arising out of the Indian equity market (Bombay Stock Exchange (BSE) Sensex). Using data from 2006 to 2015, the VaR figures associated with parametric (variance–covariance, Exponentially Weighted Moving Average, Generalized Autoregressive Conditional Heteroskedasticity) and non-parametric (historical simulation and Monte Carlo simulation) methods have been calculated. The study concludes that VaR models based on the assumption of normality underestimate the risk when returns are non-normally distributed. Models that capture fat-tailed behaviour of financial returns (historical simulation) are better able to capture the risk arising out of the financial instrument.

We present a new estimation approach that allows us to extract from spreads in synthetic credit markets the contribution of systematic and idiosyncratic default risk to total default risk. Using an extensive dataset of 90,600 credit default swap and collateralized debt obligation (CDO) tranche spreads on the North American Investment Grade CDX index, we conduct an empirical analysis of an intensity-based model for correlated defaults. Our results show that systematic default risk is an explosive process with low volatility, while idiosyncratic default risk is more volatile but less explosive. Also, we find that the model is able to capture both the level and time series dynamics of CDO tranche spreads. Copyright The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.