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Academic Affairs - One of the best experts on this subject based on the ideXlab platform.
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Appendix A1 credit hour worksheet and courses with 6 or more credit hours
2016Co-Authors: Academic AffairsAbstract:Appendix A: Supplement A1 includes two documents: (1) Credit hour worksheet and (2) Courses 6 or more credit hour information.
Gerald Schernewski - One of the best experts on this subject based on the ideXlab platform.
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implementation of european marine policy new water quality targets for german baltic waters
Marine Policy, 2015Co-Authors: Marina Carstens, Ulrike Hirt, Wera Leujak, Gunther Nausch, René Friedland, Gerald Schernewski, Thomas Neumann, Thorkild PetenatiAbstract:A full re-calculation of Water Framework Directive reference and target concentrations for German coastal waters and the western Baltic Sea is presented, which includes a harmonization with HELCOM Baltic Sea Action Plan (BSAP) targets. Further, maximum allowable nutrient inputs (MAI) and target concentrations in rivers for the German Baltic catchments are suggested. For this purpose a spatially coupled, large scale and integrative modeling approach is used, which links the river basin flux model MONERIS to ERGOM-MOM, a three-dimensional ecosystem model of the Baltic Sea. The years around 1880 are considered as reference conditions reflecting a high ecological status and are reconstructed and simulated with the model system. Alternative approaches are briefly described, as well. For every WFD water body and the open sea, target concentrations for nitrogen and phosphorus compounds as well as chlorophyll a are provided by adding 50% to the reference concentrations. In general, the targets are less strict for coastal waters and slightly stricter for the sea (e.g. 1.2mg/m³ chl.a summer average for the Bay of Mecklenburg), compared to current values. By taking into account the specifics of every water body, this approach overcomes the inconsistencies of earlier approaches. Our targets are well in agreement with the BSAP targets, but provide spatially refined and extended results. The full data are presented in Appendix A1 and A2.
Peter N Posch - One of the best experts on this subject based on the ideXlab platform.
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credit risk modeling using excel and vba
2011Co-Authors: Gunter Loffler, Peter N PoschAbstract:Preface. Some Hints for Troubleshooting. 1 Estimating Credit Scores with Logit. Linking scores, default probabilities and observed default behavior. Estimating logit coefficients in Excel. Computing statistics after model estimation. Interpreting regression statistics. Prediction and scenario analysis. Treating outliers in input variables. Choosing the functional relationship between the score and explanatory variables. Concluding remarks. Appendix. Notes and literature. 2 The Structural Approach to Default Prediction and Valuation. Default and valuation in a structural model. Implementing the Merton model with a one-year horizon. The iterative approach. A solution using equity values and equity volatilities. Comparing different approaches. Implementing the Merton model with a T-year horizon. Credit spreads. Notes and literature. 3 Transition Matrices. Cohort approach. Multi-period transitions. Hazard rate approach. Obtaining a generator matrix from a given transition matrix. Confidence intervals with the Binomial distribution. Bootstrapped confidence intervals for the hazard approach. Notes and literature. Appendix. 4 Prediction of Default and Transition Rates. Candidate variables for prediction. Predicting investment-grade default rates with linear regression. Predicting investment-grade default rates with Poisson regression. Backtesting the prediction models. Predicting transition matrices. Adjusting transition matrices. Representing transition matrices with a single parameter. Shifting the transition matrix. Backtesting the transition forecasts. Scope of application. Notes and literature. Appendix. 5 Modeling and Estimating Default Correlations with the Asset Value Approach. Default correlation, joint default probabilities and the asset value approach. Calibrating the asset value approach to default experience: the method of moments. Estimating asset correlation with maximum likelihood. Exploring the reliability of estimators with a Monte Carlo study. Concluding remarks. Notes and literature. 6 Measuring Credit Portfolio Risk with the Asset Value Approach. A default mode model implemented in the spreadsheet. VBA implementation of a default-mode model. Importance sampling. Quasi Monte Carlo. Assessing simulation error. Exploiting portfolio structure in the VBA program. First extension: Multi-factor model. Second extension: t-distributed asset values. Third extension: Random LGDs. Fourth extension: Other risk measures. Fifth extension: Multi-state modeling. Notes and literature. 7 Validation of Rating Systems. Cumulative accuracy profile and accuracy ratios. Receiver operating characteristic (ROC). Bootstrapping confidence intervals for the accuracy ratio. Interpreting CAPs and ROCs. Brier Score. Testing the calibration of rating-specific default probabilities. Validation strategies. Notes and literature. 8 Validation of Credit Portfolio Models. Testing distributions with the Berkowitz test. Example implementation of the Berkowitz test. Representing the loss distribution. Simulating the critical chi-squared value. Testing modeling details: Berkowitz on subportfolios. Assessing power. Scope and limits of the test. Notes and literature. 9 Risk-Neutral Default Probabilities and Credit Default Swaps. Describing the term structure of default: PDs cumulative, marginal, and seen from today. From bond prices to risk-neutral default probabilities. Concepts and formulae. Implementation. Pricing a CDS. Refining the PD estimation. Notes and literature. 10 Risk Analysis of Structured Credit: CDOs and First-to-Default Swaps. Estimating CDO risk with Monte Carlo simulation. The large homogeneous portfolio (LHP) approximation. Systematic risk of CDO tranches. Default times for first-to-default swaps. Notes and literature. Appendix. 11 Basel II and Internal Ratings. Calculating capital requirements in the Internal Ratings-Based (IRB) approach. Assessing a given grading structure. Towards an optimal grading structure. Notes and literature. Appendix A1 Visual Basics for Applications (VBA). Appendix A2 Solver. Appendix A3 Maximum Likelihood Estimation and Newton's Method. Appendix A4 Testing and Goodness of Fit. Appendix A5 User-Defined Functions. Index.
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credit risk modeling using excel and vba with dvd
2007Co-Authors: Gunter Loffler, Peter N PoschAbstract:Preface to the 2nd edition. Preface to the 1st edition. Some Hints for Troubleshooting. 1 Estimating Credit Scores with Logit. Linking scores, default probabilities and observed default behavior. Estimating logit coefficients in Excel. Computing statistics after model estimation. Interpreting regression statistics. Prediction and scenario analysis. Treating outliers in input variables. Choosing the functional relationship between the score and explanatory variables. Concluding remarks. Appendix. Logit and probit. Marginal effects. Notes and literature. 2 The Structural Approach to Default Prediction and Valuation. Default and valuation in a structural model. Implementing the Merton model with a one-year horizon. The iterative approach. A solution using equity values and equity volatilities. Implementing the Merton model with a T -year horizon. Credit spreads. CreditGrades. Appendix. Notes and literature. Assumptions. Literature. 3 Transition Matrices. Cohort approach. Multi-period transitions. Hazard rate approach. Obtaining a generator matrix from a given transition matrix. Confidence intervals with the binomial distribution. Bootstrapped confidence intervals for the hazard approach. Notes and literature. Appendix. Matrix functions. 4 Prediction of Default and Transition Rates. Candidate variables for prediction. Predicting investment-grade default rates with linear regression. Predicting investment-grade default rates with Poisson regression. Backtesting the prediction models. Predicting transition matrices. Adjusting transition matrices. Representing transition matrices with a single parameter. Shifting the transition matrix. Backtesting the transition forecasts. Scope of application. Notes and literature. Appendix. 5 Prediction of Loss Given Default. Candidate variables for prediction. Instrument-related variables. Firm-specific variables. Macroeconomic variables. Industry variables. Creating a data set. Regression analysis of LGD. Backtesting predictions. Notes and literature. Appendix. 6 Modeling and Estimating Default Correlations with the Asset Value Approach. Default correlation, joint default probabilities and the asset value approach. Calibrating the asset value approach to default experience: the method of moments. Estimating asset correlation with maximum likelihood. Exploring the reliability of estimators with a Monte Carlo study. Concluding remarks. Notes and literature. 7 Measuring Credit Portfolio Risk with the Asset Value Approach. A default-mode model implemented in the spreadsheet. VBA implementation of a default-mode model. Importance sampling. Quasi Monte Carlo. Assessing Simulation Error. Exploiting portfolio structure in the VBA program. Dealing with parameter uncertainty. Extensions. First extension: Multi-factor model. Second extension: t-distributed asset values. Third extension: Random LGDs. Fourth extension: Other risk measures. Fifth extension: Multi-state modeling. Notes and literature. 8 Validation of Rating Systems. Cumulative accuracy profile and accuracy ratios. Receiver operating characteristic (ROC). Bootstrapping confidence intervals for the accuracy ratio. Interpreting caps and ROCs. Brier score. Testing the calibration of rating-specific default probabilities. Validation strategies. Testing for missing information. Notes and literature. 9 Validation of Credit Portfolio Models. Testing distributions with the Berkowitz test. Example implementation of the Berkowitz test Representing the loss distribution. Simulating the critical chi-square value. Testing modeling details: Berkowitz on subportfolios. Assessing power. Scope and limits of the test. Notes and literature. 10 Credit Default Swaps and Risk-Neutral Default Probabilities. Describing the term structure of default: PDs cumulative, marginal and seen from today. From bond prices to risk-neutral default probabilities. Concepts and formulae. Implementation. Pricing a CDS. Refining the PD estimation. Market values for a CDS. Example. Estimating upfront CDS and the 'Big Bang' protocol. Pricing of a pro-rata basket. Forward CDS spreads. Example. Pricing of swaptions. Notes and literature. Appendix. Deriving the hazard rate for a CDS. 11 Risk Analysis and Pricing of Structured Credit: CDOs and First-to-Default Swaps. Estimating CDO risk with Monte Carlo simulation. The large homogeneous portfolio (LHP) approximation. Systemic risk of CDO tranches. Default times for first-to-default swaps. CDO pricing in the LHP framework. Simulation-based CDO pricing. Notes and literature. Appendix. Closed-form solution for the LHP model. Cholesky decomposition. Estimating PD structure from a CDS. 12 Basel II and Internal Ratings. Calculating capital requirements in the Internal Ratings-Based (IRB) approach. Assessing a given grading structure. Towards an optimal grading structure. Notes and literature. Appendix A1 Visual Basics for Applications (VBA). Appendix A2 Solver. Appendix A3 Maximum Likelihood Estimation and Newton's Method. Appendix A4 Testing and Goodness of Fit. Appendix A5 User-defined Functions. Index.
Andre G Duarte - One of the best experts on this subject based on the ideXlab platform.
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plant carbon metabolism and climate change elevated co2 and temperature impacts on photosynthesis photorespiration and respiration
New Phytologist, 2019Co-Authors: Mirindi Eric Dusenge, Andre G DuarteAbstract:: Contents Summary 32 I. The importance of plant carbon metabolism for climate change 32 II. Rising atmospheric CO2 and carbon metabolism 33 III. Rising temperatures and carbon metabolism 37 IV. Thermal acclimation responses of carbon metabolic processes can be best understood when studied together 38 V. Will elevated CO2 offset warming-induced changes in carbon metabolism? 40 VI. No plant is an island: water and nutrient limitations define plant responses to climate drivers 41 VII. Conclusions 42 Acknowledgements 42 References 42 Appendix A1 48 SUMMARY: Plant carbon metabolism is impacted by rising CO2 concentrations and temperatures, but also feeds back onto the climate system to help determine the trajectory of future climate change. Here we review how photosynthesis, photorespiration and respiration are affected by increasing atmospheric CO2 concentrations and climate warming, both separately and in combination. We also compile data from the literature on plants grown at multiple temperatures, focusing on net CO2 assimilation rates and leaf dark respiration rates measured at the growth temperature (Agrowth and Rgrowth , respectively). Our analyses show that the ratio of Agrowth to Rgrowth is generally homeostatic across a wide range of species and growth temperatures, and that species that have reduced Agrowth at higher growth temperatures also tend to have reduced Rgrowth , while species that show stimulations in Agrowth under warming tend to have higher Rgrowth in the hotter environment. These results highlight the need to study these physiological processes together to better predict how vegetation carbon metabolism will respond to climate change.
Thorkild Petenati - One of the best experts on this subject based on the ideXlab platform.
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implementation of european marine policy new water quality targets for german baltic waters
Marine Policy, 2015Co-Authors: Marina Carstens, Ulrike Hirt, Wera Leujak, Gunther Nausch, René Friedland, Gerald Schernewski, Thomas Neumann, Thorkild PetenatiAbstract:A full re-calculation of Water Framework Directive reference and target concentrations for German coastal waters and the western Baltic Sea is presented, which includes a harmonization with HELCOM Baltic Sea Action Plan (BSAP) targets. Further, maximum allowable nutrient inputs (MAI) and target concentrations in rivers for the German Baltic catchments are suggested. For this purpose a spatially coupled, large scale and integrative modeling approach is used, which links the river basin flux model MONERIS to ERGOM-MOM, a three-dimensional ecosystem model of the Baltic Sea. The years around 1880 are considered as reference conditions reflecting a high ecological status and are reconstructed and simulated with the model system. Alternative approaches are briefly described, as well. For every WFD water body and the open sea, target concentrations for nitrogen and phosphorus compounds as well as chlorophyll a are provided by adding 50% to the reference concentrations. In general, the targets are less strict for coastal waters and slightly stricter for the sea (e.g. 1.2mg/m³ chl.a summer average for the Bay of Mecklenburg), compared to current values. By taking into account the specifics of every water body, this approach overcomes the inconsistencies of earlier approaches. Our targets are well in agreement with the BSAP targets, but provide spatially refined and extended results. The full data are presented in Appendix A1 and A2.