Portfolio Risk Models Research Articles

A Decision Support System for Project Portfolio Management in Construction Companies

Project portfolio management requires a systematic process that comprises assessment of portfolio risk and expected profitability, as well as strategic fit of individual projects with company objectives. After a needs analysis based on literature findings and surveys with experts, in this study, a process model and a tool, COPPMAN (COnstruction Project Portfolio MANagement), were developed to support project portfolio decisions in construction companies. COPPMAN was developed in collaboration with construction professionals. Different from previous studies, it incorporates a portfolio risk and strategic fit assessment model considering project dependencies and integrates knowledge of previous, on-going and potential projects to estimate value of alternative project portfolios. COPPMAN was implemented in a construction company and evaluated as a useful tool due to its features such as knowledge integration, forecasting of portfolio profitability and recommendation of strategies as well as its visualization features. Research design and findings can be used for development of similar tools in other project-based industries.

Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach.

This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasonable level of significance, even when periods of stress are included. Numerical simulations corroborate the empirical results.

A Method for Off-Site Evaluation of the Insurance Sector

This work aims at offering a parsimonious off-site evaluation approach for insurance sector. In a way, it computes an index for systemic risk of the whole insurance industry. Methodology consists the adaption of two well-known credit portfolio risk models: CreditMetrics and CreditRisk+. Main frame is built on CreditRisk+ single sector model and is enhanced by simulations following the CreditMetrics simulation based approach. It is shown that implementing the methodology developed at this paper, authority is able to follow the level of risk associated with the financial status of the sector and order the firms in terms of their riskiness. Moreover, methodology also allows users to compute the individual risk contribution of each insurance company.

Portfolio value-at-risk with two-sided Weibull distribution: Evidence from cryptocurrency markets

This paper extends the univariate two-sided Weibull distribution to a multivariate case for portfolio-value-at-risk estimation. This method allows to capture the stylized facts of the time series of cryptocurrencies, such as extreme volatility, volatility clustering, very heavy tails, and skewness. This new portfolio risk model is applied to a cryptocurrency portfolio consisting of four major coins: Bitcoin, Litecoin, Ripple, and Dash. The predictive performance of the proposed model is compared with several widely used models. We find that the portfolio value-at-risk with two-sided Weibull distribution outperforms the other models.

Global risk aversion and emerging market return comovements

Utilizing the recently developed measure of global risk aversion by Xu (2017), we show that global risk aversion is a significant determinant of international equity correlations, consistently across all emerging markets examined. The positive effect of risk aversion on emerging market comovements is particularly strong for South Africa and Turkey and is consistent with contagion effects. The results underscore the importance of non-cash flow shocks in models of contagion and portfolio risk.

A High-Moment Trapezoidal Fuzzy Random Portfolio Model with Background Risk

AbstractIn most exiting portfolio selection models, security returns are assumed to have random or fuzzy distributions. However, uncertainties exist in actual financial markets. Markets are associated not only with inherent risk, but also with background risk that results from the differences among individual investors. This paper investigated the compliance of stock yields to the fuzzy-natured high-order moments of random numbers in order to develop a high-moment trapezoidal fuzzy random portfolio risk model based on variance, skewness, and kurtosis. Data obtained from the Shanghai Stock Exchange and Shenzhen Stock Exchange was used to assess the influence on the proposed model of both background risk and the maximum level of satisfaction of the portfolio. The empirical results demonstrated that the differences between the maximum and minimum variance, skewness, and kurtosis values of the portfolio were positively correlated with the variance of the background risk.

Multiscale dependence analysis and portfolio risk modeling for precious metal markets

In this paper, we propose a new Bivariate EMD copula based approach to analyze and model the multiscale dependence structure in the precious metal markets. The proposed model constructs the Copula based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD) transformed multiscale domain. We further propose the BEMD Copula based Portfolio Value at Risk (PVaR) model to estimate the precious metal market risk measure. Empirical studies in the typical precious metal markets have been conducted. We found the evidence of multiscale structure of the time varying dependence structure among precious metal markets. We show that significantly improved portfolio risk forecasting performance could be achieved with the proposed model when the multiscale dependence structure is taken into account during the modeling process.

Estimating credit risk parameters using ensemble learning methods: an empirical study on loss given default

In credit risk modeling, banks and insurance companies routinely use a single model for estimating key risk parameters. Combining several models to make a final prediction is not often considered. Using an ensemble or a collection of models rather than a single model can improve the accuracy and robustness of prediction results. In this study, we investigate two well-established ensemble learning methods (stochastic gradient boosting and random forest) and propose two new ensembles (ensemble by partial least squares and bag-boosting) in the application of predicting the loss given default. We demonstrate that an ensemble approach significantly increases the discriminatory power of the model compared with a single decision tree. In addition, the ensemble learning methods can be applied directly to predicting the exposure at default and probability of default with some simple modifications. The proposed approaches introduce a novel modeling framework that banks and other financial institutions can use to estimate and validate credit risk parameters based on the internal data of different portfolios. Moreover, the proposed approaches can be readily extended to general portfolio risk modeling in the areas of regulatory capital and economic capital management, loss forecasting, stress testing and pre-provision net revenue projections.

Application of Vine Copulas to Credit Portfolio Risk Modeling

In this paper, we demonstrate the superiority of vine copulas over conventional copulas when modeling the dependence structure of a credit portfolio. We show statistical and economic implications of replacing conventional copulas by vine copulas for a subportfolio of the Euro Stoxx 50 and the S&P 500 companies, respectively. Our study includes D-vines and R-vines where the bivariate building blocks are chosen from the Gaussian, the t and the Clayton family. Our findings are (i) the conventional Gauss copula is deficient in modeling the dependence structure of a credit portfolio and economic capital is seriously underestimated; (ii) D-vine structures offer a better statistical fit to the data than classical copulas, but underestimate economic capital compared to R-vines; (iii) when mixing different copula families in an R-vine structure, the best statistical fit to the data can be achieved which corresponds to the most reliable estimate for economic capital.

Global financial crisis and spillover effects among the U.S. and BRICS stock markets

This article examines the spillover effect between the U.S. market and five of the most important emerging stock markets namely those of the BRICS (Brazil, Russia, India, China and South Africa), and draws implications for portfolio risk modeling and forecasting. It gives consideration to periods before and after the recent global financial crisis (GFC). To this end, the bivariate DCC-FIAPARCH model, the modified ICSS algorithm and the Value-at-Risk (VaR) are employed to capture volatility spillovers, detect potential structural breaks and assess the portfolio market risks. Using the U.S. and the BRICS daily spot market indices for the period from September 1997 to October 2013, our empirical results show strong evidence of asymmetry and long memory in the conditional volatility and significant dynamic correlations between the U.S. and the BRICS stock markets. Moreover, we find several sudden changes in these markets with a common break date centered on September 15, 2008 which corresponds to the Lehman Brothers collapse. The Brazil, India, China and South Africa markets are strongly affected by the GFC, supporting the hypothesis of recoupling (with increased linkages). In contrast, the hypothesis of decoupling is supported for the Russian stock markets only. Finally, the skewed Student-t FIAPARCH models outperform and provide more accurate in-sample estimates and out-of-sample forecasts of VaR than the normal and Student-t FIAPARCH models in almost all cases. These results provide helpful information to financial risk managers, regulators and portfolio investors to determine the diversification benefits among these markets.

Single cell cloning and recombinant monoclonal antibodies generation from RA synovial B cells reveal frequent targeting of citrullinated histones of NETs.

ObjectivesRheumatoid arthritis (RA) is characterised by breach of self-tolerance towards citrullinated antigens with generation of anti-citrullinated peptide/proteins antibodies (ACPA). Currently, the nature and source of citrullinated antigens driving the humoral autoimmune response within synovial ectopic lymphoid structures (ELS) is a crucial unknown aspect of RA pathogenesis. Here we characterised the autoreactive B-cell response of lesional B cells isolated from ELS+RA synovium.MethodsSingle synovial tissue CD19+cells were Fluorescence Activated Cell Sorting (FACS)-sorted and VH/VL Ig genes cloned to generate recombinant monoclonal antibodies (rmAbs) from patients with ELS+/ACPA+RA.ResultsRA-rmAbs immunoreactivity analysis provided the following key findings: (1) in a chIP-based array containing 300 autoantigens and in a ‘citrullinome’ multiplex assay, a strong reactivity against citrullinated histones H2A/H2B (citH2A/H2B) was observed in ∼40% of RA-rmAbs, followed by cit-fibrinogen and cit-vimentin; (2) anti-citH2A/H2B-reactive RA-rmAbs (but not anti-citH2A/H2B negative) selectively recognised neutrophil extracellular traps (NETs) from peripheral blood and/or RA joint neutrophils; (3) anti-citH2A/citH2B and anti-NET immunobinding was dependent on affinity maturation and was completely abrogated following reversion of hypermutated IgVH/VL genes to germline sequences; (4) ELS+ (not ELS−) RA synovial tissues engrafted into Severe Combined ImmunoDeficiency (SCID) mice released human anti-citH2A/citH2B and anti-NET antibodies in association with the intra-graft expression of CXCL13 and lymphotoxin (LT)-β, two master regulators of ELS.ConclusionWe provided novel evidence that B cells differentiated within synovial ELS in the RA joints frequent target deiminated proteins which could be generated during NETosis of RA synovial neutrophils including histones. Thus, NETs could represent a source of citrullinated antigens fuelling the ACPA autoimmune response within the RA synovium.

Measuring Total Mortgage Market Credit Risk

This paper proposes two measures of credit risk for the population of outstanding mortgages. The first uses an average ex ante default probability to characterize risk, the second uses the unexpected loss generated by the asymptotic single factor risk (ASFR) model, a probabilistic model of portfolio risk. Both approaches show that average market–wide expected default rate and the unexpected loss per dollar of outstanding mortgage balances were roughly constant during the 2002-2006 boom in US house prices.

Accuracy of mortgage portfolio risk forecasts during financial crises

This paper explores whether factor based credit portfolio risk models are able to predict losses in severe economic downturns such as the recent Global Financial Crisis (GFC) within standard confidence levels. The paper analyzes (i) the accuracy of default rate forecasts, and (ii) whether forecast downturn percentiles (Value-at-Risk, VaR) are sufficient to cover default rate outcomes over a quarterly and an annual forecast horizon. Uninformative maximum likelihood and informative Bayesian techniques are compared as they imply different degrees of uncertainty.We find that quarterly VaR estimates are generally sufficient but annual VaR estimates may be insufficient during economic downturns. In addition, the paper develops and analyzes models based on auto-regressive adjustments of scores, which provide a higher forecast accuracy. The consideration of parameter uncertainty and auto-regressive error terms mitigates the shortfall.

Modeling portfolio risk by risk discriminatory trees and random forests

Common tree-splitting strategies involve minimizing a criterion function for minimum impurity (ie, difference) within child nodes. In this paper, we propose an approach based on maximizing a discriminatory criterion for maximum risk difference between child nodes. Maximum discriminatory separation based on risk is expected in credit risk scoring and rating. The search algorithm for an optimal split, proposed in this paper, is efficient and simple, just a scan through the data set. Choices of different trees, with options either more or less aggressive in variable splitting, are made possible. Two special cases are shown to relate to the Kolmogorov-Smirnov and the intracluster correlation statistics. As a validation of the proposed approaches, we estimate the exposure at default for a commercial portfolio. Results show that the risk discriminatory trees, constructed and selected using the bagging and random forest techniques, are robust. It is expected that the tools presented in this paper will add value to general portfolio risk modeling.

Granularity adjustment for mark-to-market credit risk models

The impact of undiversified idiosyncratic risk on value-at-risk and expected shortfall can be approximated analytically via a methodology known as granularity adjustment (GA). In principle, the GA methodology can be applied to any risk-factor model of portfolio risk. Thus far, however, analytical results have been derived only for simple models of actuarial loss, i.e., credit loss due to default. We demonstrate that the GA is entirely tractable for single-factor versions of a large class of models that includes all the commonly used mark-to-market approaches. Our approach covers both finite ratings-based models and models with a continuum of obligor states. We apply our methodology to CreditMetrics and KMV Portfolio Manager, as these are benchmark models for the finite and continuous classes, respectively. Comparative statics of the GA reveal striking and counterintuitive patterns. We explain these relationships with a stylized model of portfolio risk.

Portfolio Risk and Dependence Modeling: Application of Factor and Copula Models

We consider portfolio credit risk modeling with a focus on two approaches, the factor model, and the copula model. While other models have received greater scrutiny, both factor and cupola models have received little attention although these are appropriate for rating-based portfolio risk analysis. We review the two models with emphasis on the joint default probability. The copula function describes the dependence structure of a multivariate random variable. In this paper, it is used as a practical to simulation of generate portfolio with different copula, we only use Gaussian and t-copula case. And we generate portfolio default distributions and study the sensitivity of commonly used risk measures with respect to the approach in modeling the dependence structure of the portfolio.

Empirical Performance of LGD Prediction Models

The Global Financial Crisis highlighted that default and recovery rates of multiple borrowers generally deteriorate jointly during economic downturns. The vast majority of the literature, as well as many industry credit portfolio risk models ignore this and analyze default probabilities and recoveries in the event of default separately. As a result, the models project losses which are too low in economic downturns such as the recent financial crisis. Nevertheless, alternatives of incorporating the dependence between probabilities of default and recovery rates have been proposed. This paper is the first of its kind to assess the performance of these structurally different approaches. Four banks using different estimation procedures are compared. We use RMSE and RAE to measure the predictive accuracy of each procedure. The results show, that indeed models accounting for the correlation of default and recovery perform better than models ignoring it.

Granularity Adjustment for Mark-to-Market Credit Risk Models

The impact of undiversified idiosyncratic risk on value-at-risk and expected shortfall can be approximated analytically via a methodology known as granularity adjustment (GA). In principle, the GA methodology can be applied to any risk-factor model of portfolio risk. Thus far, however, analytical results have been derived only for simple models of actuarial loss, i.e., credit loss due to default. We demonstrate that the GA is entirely tractable for single-factor versions of a large class of models that includes all the commonly used mark-to-market approaches. Our approach covers both finite ratings-based models and models with a continuum of obligor states. We apply our methodology to CreditMetrics and KMV Portfolio Manager, as these are benchmark models for the finite and continuous classes, respectively. Comparative statics of the GA with respect to model parameters in CreditMetrics reveal striking and counterintuitive patterns. We explain these relationships with a stylized model of portfolio risk.

Genetic Variation in Financial Decision‐Making

ABSTRACTIndividuals differ in how they construct their investment portfolios, yet empirical models of portfolio risk typically account only for a small portion of the cross‐sectional variance. This paper asks whether genetic variation can explain some of these individual differences. Following a major pension reform Swedish adults had to form a portfolio from a large menu of funds. We match data on these investment decisions with the Swedish Twin Registry and find that approximately 25% of individual variation in portfolio risk is due to genetic variation. We also find that these results extend to several other aspects of financial decision‐making.

Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

We provide a sequential Monte Carlo method for estimating rare-event probabilities in dynamic, intensity-based point process models of portfolio credit risk. The method is based on a change of measure and involves a resampling mechanism. We propose resampling weights that lead, under technical conditions, to a logarithmically efficient simulation estimator of the probability of large portfolio losses. A numerical analysis illustrates the features of the method, and contrasts it with other rare-event schemes recently developed for portfolio credit risk, including an interacting particle scheme and an importance sampling scheme.