{"title":"A Sensitivity Based Approach for CVA Computation","authors":"A. Reghai, Othmane Kettani","doi":"10.2139/ssrn.2576846","DOIUrl":"https://doi.org/10.2139/ssrn.2576846","url":null,"abstract":"Since the sub-prime crisis, counterparty credit risk and Wrong Way Risk are a crucial issue in connection with valuation and risk management of credit derivatives. For portfolios comprising other than simple vanilla products, calculating CVA could be a rather daunting task. In this work, we present a comprehensive approach to model CVA and Wrong Way Risk for equity portfolios. This approach is based on a first order approximation of exposures that allows us to derive closed-form formulas for CVA. Wrong Way risk is modeled through a Gaussian copula that connects default of the counterparty to the underlying portfolio.We test our model on two portfolios in two different credit risk scenarios and analyze the results obtained. In particular, we show how we retrieve the theoretical CVA in a computationally appealing manner. Some interesting properties of the Gaussian Copula related to Wrong Way Risk modeling are also discussed in the paper.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125773460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Diversified Equity Portfolios: The Case to Use Risk-Based Weights to Construct an Investable Equity Index","authors":"A. Sotolongo","doi":"10.2139/ssrn.2573859","DOIUrl":"https://doi.org/10.2139/ssrn.2573859","url":null,"abstract":"When making a passive equity investment investors typically use indexes that are weighted by the market capitalization of the constituents. Using S&P 500 US equity sectors I will show that market cap weights provide inefficient diversification. As an alternative to the market cap weighted S&P 500 index I will introduce an investable risk-based index using SPDR sector ETFs. The risk-based weights are calculated using a risk parity methodology and over the life of the portfolio (03-26-1999 to 02-28-2015) create a cumulative return net of estimated trading costs of 79.5% compared to the S&P 500 return of 55.9%1. In addition to its higher returns, the sector equal risk contribution portfolio (ERP) has lower risk measured by standard deviation, conditional value at risk, and drawdowns.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126821231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Demir, D. Bryde, Damian J. Fearon, Professor Edward Ochieng
{"title":"Three Dimensional Stakeholder Analysis – 3dSA: Adding the Risk Dimension for Stakeholder Analysis","authors":"S. Demir, D. Bryde, Damian J. Fearon, Professor Edward Ochieng","doi":"10.2139/ssrn.2622155","DOIUrl":"https://doi.org/10.2139/ssrn.2622155","url":null,"abstract":"There is a need for better integration of stakeholder analysis and risk management because there are risks which can arise from the actions of stakeholders which can impact on the project aims and objectives. To meet this need the authors propose that stakeholders need to be analysed in three dimensions. This gives higher transparency to a stakeholder’s characteristics and creates a stronger link to risk management. In addition to a stakeholder’s power and interest, a third dimension of ‘attitude’ is developed. Hence, a power-interest-attitude matrix is generated and applied to a real case construction project in Germany. This application to a real project scenario demonstrates how stakeholder analysis can be enhanced over the commonly used two dimensional matrices, to better integrate stakeholder analysis with risk management.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130792652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Dynamic Valuation of Delinquent Credit-Card Accounts","authors":"Naveed Chehrazi, Thomas A. Weber","doi":"10.2139/ssrn.1868581","DOIUrl":"https://doi.org/10.2139/ssrn.1868581","url":null,"abstract":"This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) that estimates the account-specific probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account-specific, and time-varying macroeconomic covariates, as well as scheduled account-treatment actions. Two model-identification methods are examined, based on maximum-likelihood estimation and the generalized method of moments. The latter allows for an operational-statistics approach, combining model estimation and performance optimization by tailoring the estimation error to business-relevant loss functions. The DCS framework is applied to a large set of account-level repayment data. The improvements in classification and prediction performance compared to standard bank-internal scoring methods are found to be significant. This paper was accepted by Noah Gans, stochastic models and simulation .","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129492643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bank and Sovereign Risk Feedback Loops","authors":"Aitor Erce","doi":"10.24149/gwp227","DOIUrl":"https://doi.org/10.24149/gwp227","url":null,"abstract":"Measures of Sovereign and Bank Risk show occasional bouts of increased correlation, setting the stage for vicious and virtuous feedback loops. This paper models the macroeconomic phenomena underlying such bouts using CDS data for 10 euro-area countries. The results show that Sovereign Risk feeds back into Bank Risk more strongly than vice versa. Countries with sovereigns that are more indebted or where banks have a larger exposure to their own sovereign, suffer larger feedback loop effects from Sovereign Risk into Bank Risk. In the opposite direction, in countries where banks fund their activities with more foreign credit and support larger levels of non-performing loans, the feedback from Bank Risk into Sovereign Risk is stronger. According to model estimates, financial rescue operations can increase feedback effects from bank risk into sovereign risk. These results can be useful for the official sector when deciding on the form of financial rescues.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130730694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Political Connections, Discriminatory Credit Constraint and Business Cycle","authors":"Yuchao Peng, Lili Yan","doi":"10.2139/ssrn.2558459","DOIUrl":"https://doi.org/10.2139/ssrn.2558459","url":null,"abstract":"This paper builds a banking DSGE model based on endogenous loan to value ratios, taking the different relationship between different types of enterprises and banks into account. Due to the political connections between the bank and enterprises, loan to value ratio for favored enterprises (e.g. state-owned enterprises) is endogenously higher than that for non-favored enterprises (e.g. private enterprises), which is called discriminatory credit constraint in this paper. Compared to non-discriminatory credit constraint, we find that discriminatory credit constraint can further amplify the impact of negative technology shocks on output, and reduce the effectiveness of expansionary monetary policy. Empirical evidence from China industrial firms’ data supports our conclusion.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116747226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Options for Modelling the Financial Viability of Sofix Companies in the Post-Crisis Years","authors":"G. Angelov","doi":"10.2139/ssrn.2814333","DOIUrl":"https://doi.org/10.2139/ssrn.2814333","url":null,"abstract":"A financial crisis undoubtedly had the enormous negative operating on the real sector in a national and global scale. A grate number of stopping of companies, business restructuring, decrease of production, and staff surplus. Therefore it is vitally important to estimate financial steady development of the Bulgarian companies. Primary objective of such estimation to identify accessible possibilities for the acceptance of the adequate, self-weighted decisions, to support companies in the process of adaptation to replacement of market requirements. Aim of the article to foresee main financial pressures, using models for the estimation of authenticity of bankruptcy of companies and to offer the choice of decisions for overcoming these difficulties. An aim was arrived at through the empiric test of existent models in terms of open corporations of index of SOFIX during four years, from 2011 to 2014. Results from this test then drawn on as a benchmark test in the process of decision-making.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"86 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120877735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Oil Volatility Risk and Expected Stock Returns","authors":"Peter F. Christoffersen, Xuhui (Nick) Pan","doi":"10.2139/ssrn.2399677","DOIUrl":"https://doi.org/10.2139/ssrn.2399677","url":null,"abstract":"After the financialization of commodity futures markets in 2004-05 oil volatility has become a strong predictor of returns and volatility of the overall stock market. Furthermore, stocks' exposure to oil volatility risk now drives the cross-section of expected returns. The difference in average return between the quintile of stocks with low exposure and high exposure to oil volatility is significant at 0.66% per month, and oil volatility risk carries a significant risk premium of -0.60% per month. In the post-financialization period, oil volatility risk is strongly related with various measures of funding liquidity constraints suggesting an economic channel for the effect.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124600412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analyst Price Target Expected Returns and Option Implied Risk","authors":"Turan G. Bali, Jianfeng Hu, Scott Murray","doi":"10.2139/ssrn.2516937","DOIUrl":"https://doi.org/10.2139/ssrn.2516937","url":null,"abstract":"Motivated by the nature of asset pricing models, we investigate the cross-sectional relation between the market's ex-ante view of a stock's risk and the stock's ex-ante expected return. We demonstrate that an ex-ante measure of expected returns based on analyst price targets is highly related to the market's required rate of return. Using this measure, we show that ex-ante measures of volatility, skewness, and kurtosis derived from option prices are positively related to ex-ante expected returns. We then decompose the risk measures into systematic and unsystematic components and find that while expected returns are related to both systematic and unsystematic variance risk, only the unsystematic components of skewness and kurtosis are important for explaining the cross-section of expected stock returns. The results are consistent using two different approaches to measuring ex-ante risk and robust to controls for other variables related to stock returns and analyst bias.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123274999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Initial Approach to Risk Management of Funding Costs","authors":"D. Brigo, Cyril Durand","doi":"10.2139/ssrn.2507447","DOIUrl":"https://doi.org/10.2139/ssrn.2507447","url":null,"abstract":"In this note we sketch an initial tentative approach to funding costs analysis and management for contracts with bilateral counterparty risk in a simplified setting. We depart from the existing literature by analyzing the issue of funding costs and benefits under the assumption that the associated risks cannot be hedged properly. We also model the treasury funding spread by means of a stochastic Weighted Cost of Funding Spread (WCFS) which helps describing more realistic financing policies of a financial institution. We elaborate on some limitations in replication-based Funding / Credit Valuation Adjustments we worked on ourselves in the past, namely CVA, DVA, FVA and related quantities as generally discussed in the industry. We advocate as a different possibility, when replication is not possible, the analysis of the funding profit and loss distribution and explain how long term funding spreads, wrong way risk and systemic risk are generally overlooked in most of the current literature on risk measurement of funding costs. As a matter of initial illustration, we discuss in detail the funding management of interest rate swaps with bilateral counterparty risk in the simplified setup of our framework through numerical examples and via a few simplified assumptions.","PeriodicalId":187811,"journal":{"name":"ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127082764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}