Credit Risk: Pricing, Measurement, and Management, by Darrell Duffie and Kenneth J Singleton, 2003, Princeton, NJ: Princeton University Press Credit risk is the major challenge for risk managers and market regulators International regulation of banks' credit risk was put in place in 1988 and since that time there has been no consensus on how to improve that regulatory framework Part of the explanation resides in the complexity of this risk Banks, regulators, and central banks do not agree on how to measure credit risk and, more particularly, on how to compute the optimal capital that is necessary for protecting the different partners that share this risk For example, what proportion of yield spreads on corporate bonds is explained by credit risk? Is it 30 percent, 50 percent, or even 90 percent? Is the credit risk proportion of the observed spreads solely a function of variations in the default probability or is it also explained by variations in the recovery rate over time or across cycles? Are macroeconomic cycles themselves or default risk premia, market liquidity, and even market risk significant determinants of yield spreads? These questions are important because some models such as CreditMetrics use the entire yield spread to compute the capital for credit risk If credit risk explains only a small fraction of yield spreads, these models compute too much capital for regulation and even for credit risk management (Dionne et al, 2004 and references therein) Asking banks to keep too much capital in reserve to cover credit risk can be a source of market distortion in risk management behavior (Allen and Gale, 2003; Dionne and Harchaoui, 2003) For example, it may generate some asset substitution activities that increase the risky position of banks, in order to set the level of risk at its optimal rather than regulatory level All these issues arise in part because credit risk is not well understood So the book by Duffie and Singleton will be welcomed by the academics, regulators, and practitioners who consult it The book has 13 chapters, three appendices (two on affine processes), a comprehensive list of references, and an index (authors and subjects) It covers all subjects related to credit risk It is designed for three broad audiences: academics and graduate students; those involved in the measurement and control of financial risks; and those involved in trading and marketing products with significant credit risk The main focus is modeling credit risk: measuring portfolio credit risk and pricing different securities exposed to credit risk The focus on credit risk management is less important in the book The introduction (indeed the entire book) is very well written and presents the subjects treated with clarity Credit risk is distinguished from other sources of risk such as market risk, liquidity risk, operational risk, systemic risk, and regulatory and legal risk The distinctions take many dimensions such as time horizon, liquidity, the parties implicated, methodology, and information asymmetries However, the authors insist on the fact that this does not mean that all these different risks should be managed separately These different risks may be correlated over time, so integrated frameworks for measuring and pricing them are necessary, particularly for market, credit, and liquidity risks For example, factors underlying changes in credit risk are often correlated with those underlying market risk and changes in liquidity risk can be viewed as a component of market risk and may generate credit risk The last chapter proposes an original way of integrating credit and market risks in a portfolio model The introduction also provides an overview of the book The chapters are organized to highlight the major topics related to credit risk, such as "Definition and Management" (Chapter 2), "Default and Transition" (Chapters 3 and 4), "Valuation" (including valuation of credit derivatives, Chapters 5-9), "Default Correlation" and "Portfolio Valuation" (Chapters 10 and 11), "Credit Risk in OTC Derivatives Positions" and "Portfolio Risk Measurement" (Chapters 12 and 13) …

Credit Risk: Pricing, Measurement, and Management

This paper aims at serving as a critical analysis of Real Options (RO) methodologies that have so far been applied to the flexible evaluation of smart grid developments and as a practical guide to understanding the benefits but more importantly the limitations of RO methodologies. Hence, future research could focus on developing more practical RO tools for application to the energy industry, thus making the utilization of powerful “real options thinking” for decision making under uncertainty more widespread. This is particularly important for applications in low carbon power and energy systems with increasing renewable and sustainable energy resources, given the different types of uncertainty they are facing in the transition towards a truly Smart Grid. In order to do so, and based on an extensive relevant literature review, the analogies with financial options are first presented, with various assumptions and their validity being clearly discussed in order to understand if, when, and how specific methods can be applied. It is then argued how option theory is in most cases not directly applicable to investment in energy systems but requires the consideration of their physical characteristics. The paper finally gives recommendations for building practical RO approaches to energy system (and potentially all engineering) project investments under uncertainty, regardless of the scale, time frame, or type of uncertainty involved.

A critical review of Real Options thinking for valuing investment flexibility in Smart Grids and low carbon energy systems

This paper demonstrates an innovative data analysis for weather using Cloud Computing, integrating both system and application Data Science services to investigate extreme weather events. Identifying five existing projects with ongoing challenges, our aim is to process, analyze and visualize collected data, study implications and report meaningful findings. We demonstrate the use of Cloud Computing technologies, MapReduce and optimization techniques to simulate temperature distributions and analyze weather data. Two major cases are presented. The first case is focused on forecasting temperatures based on studying trends from the historical data of Sydney, Singapore and London to compare the historical and forecasted temperatures. The second case is to use five-step MapReduce for numerical data analysis and eight-step process for visualization, which is used to analyze and visualize temperature distributions in the United States, before, during and after the time of experiencing polar vortex, as well as in the United Kingdom during and after the flood. Optimization was used in experiments involved up to 100 nodes between Cloud and non-Cloud and compared performance with and without optimization. There was an improvement in performance between 20% and 30% under 60 nodes in Cloud. Results, discussion and comparison were presented. We justify our research contributions and explain thoroughly in the paper how the three goals can be met: (1) forecasting temperatures of three cities based on evaluating the trends from the historical data; (2) using five-step MapReduce to achieve shorter execution time on Cloud and (3) using eight-step MapReduce with optimization to achieve data visualization for temperature distributions on US and UK maps.

/pdf/towards-data-analysis-for-weather-cloud-computing-53zjr3jujt.pdf

Towards data analysis for weather cloud computing

https://kar.kent.ac.uk/38763/1/ShaominWu.pdf

Construction of asymmetric copulas and its application in two-dimensional reliability modelling

Financing Renewable Energy Infrastructure: Formulation, Pricing and Impact of a Carbon Revenue Bond

In risk management it is desirable to grasp the essential statistical features of a time series representing a risk factor. This tutorial aims to introduce a number of different stochastic processes that can help in grasping the essential features of risk factors describing different asset classes or behaviors. This paper does not aim at being exhaustive, but gives examples and a feeling for practically implementable models allowing for stylised features in the data. The reader may also use these models as building blocks to build more complex models, although for a number of risk management applications the models developed here suffice for the first step in the quantitative analysis. The broad qualitative features addressed here are fat tails and mean reversion. We give some orientation on the initial choice of a suitable stochastic process and then explain how the process parameters can be estimated based on historical data. Once the process has been calibrated, typically through maximum likelihood estimation, one may simulate the risk factor and build future scenarios for the risky portfolio. On the terminal simulated distribution of the portfolio one may then single out several risk measures, although here we focus on the stochastic processes estimation preceding the simulation of the risk factors Finally, this first survey report focuses on single time series. Correlation or more generally dependence across risk factors, leading to multivariate processes modeling, will be addressed in future work.

/pdf/a-stochastic-processes-toolkit-for-risk-management-1xx7l1v1tr.pdf

A Stochastic Processes Toolkit for Risk Management

A stochastic processes toolkit for risk management: Geometric Brownian motion, jumps, GARCH and variance gamma models

A Stochastic Processes Toolkit for Risk Management. Mean reverting processes and jumps.

In risk management it is desirable to grasp the essential statistical features of a time series representing a risk factor. This tutorial aims to introduce a number of different stochastic processes that can help in grasping the essential features of risk factors describing different asset classes or behaviors. This paper does not aim at being exhaustive, but gives examples and a feeling for practically implementable models allowing for stylised features in the data. The reader may also use these models as building blocks to build more complex models, although for a number of risk management applications the models developed here suffice for the first step in the quantitative analysis. The broad qualitative features addressed here are {fat tails} and {mean reversion}. We give some orientation on the initial choice of a suitable stochastic process and then explain how the process parameters can be estimated based on historical data. Once the process has been calibrated, typically through maximum likelihood estimation, one may simulate the risk factor and build future scenarios for the risky portfolio. On the terminal simulated distribution of the portfolio one may then single out several risk measures, although here we focus on the stochastic processes estimation preceding the simulation of the risk factors Finally, this first survey report focuses on single time series. Correlation or more generally dependence across risk factors, leading to multivariate processes modeling, will be addressed in future work.

This paper presents a credit migration model that aims to consistently capture the point-in-time dynamics of the credit worthiness of debt issuers and their obligations, and a calibration routine that permits the model to effectively fit historical ratings data. Our approach is to view the rating migration matrices as the operator semi-group associated to an approximated parametric infinitesimal generators. Our credit model accounts not only for default risk dynamics but also for the entire transition among states of the rating migration matrix. This modeling feature is fundamental for an efficient risk management of credit derivatives and credit risk portfolios conditionally on a state of the economy or specific macro factors. We fit our model to the historical average rating migration matrices published by Moody’s Investors Service, focusing on the banking sector over the period 1920-2005. Our results show that the model can identify the through-the-cycle transition across rating scales and that the point-in-time migration probabilities are only generated by stressed economic conditions and can only be justified by the influence of macro factors on the through-the-cycle unconditional probability values. The great level of modeling details and the accuracy of the produced results is an improvement over those of other available models.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1969796_code705092.pdf?abstractid=1969796&mirid=1&type=2

Antonio Dalessandro

Papers

A Stochastic Processes Toolkit for Risk Management

A stochastic processes toolkit for risk management: Geometric Brownian motion, jumps, GARCH and variance gamma models

A Stochastic Processes Toolkit for Risk Management. Mean reverting processes and jumps.

A Stochastic Processes Toolkit for Risk Management

Credit Migration Risk and Point in Time Credit Dynamics: A New Perspective for Credit Risk Management