Weather risk assessment of Indian power sector: A conditional value-at-risk approach:
15 Nov 2019-Energy & Environment (SAGE PublicationsSage UK: London, England)-Vol. 30, Iss: 4, pp 641-661
TL;DR: In this paper, the authors assess the weather risk exposure of Indian power sector from both generation and demand sides, considering two representative firms, Damodar Valley Corporation (DVC) and Power Grid Corporation (PGC).
Abstract: This paper aims to assess the weather risk exposure of Indian power sector from both generation and demand sides. The study considers two representative firms – firstly, Damodar Valley Corporation ...
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TL;DR: In this paper, the authors present an excellent and complete introduction to applied statistics in the atmospheric sciences, including matrix algebra, multinomial distributions, principal components (called empirical orthogonal functions), canonical correlation analysis, discrimination and classification, and cluster analysis.
Abstract: Read any paper in statistical meteorology or climatology since 1995 and you are nearly certain to find a citation of the first edition of this book (Wilks 1995). This is for good reason. The book is an excellent and complete introduction to applied statistics in the atmospheric sciences. The examples are all current, and the explanations of methods are transparently clear. The original has been used successfully as a textbook. The new edition contains many new topics, including density estimation, the bootstrap, and numerical methods in parameter fitting. Many of the examples are new, and there are several new problems at the end of each chapter. The most substantial additions come in the new Section 3, “Multivariate Statistics.” All of the standard topics are covered: a review of matrix algebra, multinomial distributions, principal components (called “empirical orthogonal functions” in meteorology), canonical correlation analysis, discrimination and classification, and cluster analysis. A regular, applied multivariate course could be (and has been) taught with this part of the book. Multidimensional statistics and massive datasets are meteorology nowadays, and this is the only book that presents a complete summary of the methods in common use. What makes this book specific to meteorology, and not just to applied statistics, are its extensive examples and two chapters on statistical forecasting and forecast evaluation. Most weather forecasts start as output from dynamical models, which are essentially enormous sets of partial differential equations and parameterizations that describe the physics of the atmosphere. The models are fed initial conditions, which are observations that go through a process called analysis that synchronizes the observations and model physics. Then the models are integrated forward in time. What comes out is a rough prediction of the future. Statistical models take these rough guesses and make them better. Naturally, there are many ways to do this, and many ways yet to be discovered. Wilks does a good job explaining what is known and what is not known. The newest twist in the forecast process, and one that recognizes the chaotic nature of the atmosphere, is called ensemble forecasting. The initial conditions are not without uncertainty, and so they are perturbed (in another complicated process of analysis) in such a way as to represent this uncertainty, and the dynamical models are rerun many times, each time with different perturbed initial conditions. The resulting ensemble of forecasts must be statistically postprocessed to produce (and display) a usable forecast. How to best do this is an open question, but again the book lays out the common strategies now in use. Once the forecasts (of any type) are in hand, they must be evaluated for accuracy using statistical methods. How to do this for point forecasts is now fairly well understood; the concepts of skill, proper probability forecasts, economic value, and graphical methods are all given here. But another big open question is how to do evaluation for multidimensional multivariate forecasts, a problem that few have yet tried to tackle, although some progress is being made. Actually, meteorologists have led the way in the statistical evaluation of predictions, and it would be wise for statisticians to take notice of these methods and begin to apply them routinely to their own models. For example, in how many applied papers in, say, sociology journals, can you recall that the model touted by the authors was actually verified and evaluated or just taken as finally proved (with an acceptably low p value)?
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TL;DR: Fundamental properties of conditional value-at-risk are derived for loss distributions in finance that can involve discreetness and provides optimization shortcuts which, through linear programming techniques, make practical many large-scale calculations that could otherwise be out of reach.
Abstract: Fundamental properties of conditional value-at-risk (CVaR), as a measure of risk with significant advantages over value-at-risk (VaR), are derived for loss distributions in finance that can involve discreetness. Such distributions are of particular importance in applications because of the prevalence of models based on scenarios and finite sampling. CVaR is able to quantify dangers beyond VaR and moreover it is coherent. It provides optimization short-cuts which, through linear programming techniques, make practical many large-scale calculations that could otherwise be out of reach. The numerical efficiency and stability of such calculations, shown in several case studies, are illustrated further with an example of index tracking.
3,010 citations
"Weather risk assessment of Indian p..." refers background in this paper
...Such presence of a ‘fat tail’ in the distribution of weather data has also been mentioned by many in literature.(25,26) In the third section, the study generates two series of 8000 random numbers, one following Gamma, and the other Normal distribution....
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TL;DR: Conditional value-at-risk (CVAR) as mentioned in this paper is a measure of risk with significant advantages over VAR that can quantify dangers beyond VAR, and moreover it provides optimization shortcuts which can make practical many large-scale calculations that could otherwise be out of reach.
Abstract: Fundamental properties of conditional value-at-risk, as a measure of risk with significant advantages over value-at-risk, are derived for loss distributions in finance that can involve discreetness. Such distributions are of particular importance in applications because of the prevalence of models based on scenarios and finite sampling. Conditional value-at-risk is able to quantify dangers beyond value-at-risk, and moreover it is coherent. It provides optimization shortcuts which, through linear programming techniques, make practical many large-scale calculations that could otherwise be out of reach. The numerical efficiency and stability of such calculations, shown in several case studies, are illustrated further with an example of index tracking.
1,208 citations
TL;DR: A rapidly growing body of research applies panel methods to examine how temperature, precipitation, and windstorms influence economic outcomes as mentioned in this paper, including agricultural output, industrial output, labor productivity, energy demand, health, conflict, and economic growth.
Abstract: A rapidly growing body of research applies panel methods to examine how temperature, precipitation, and windstorms influence economic outcomes. These studies focus on changes in weather realizations over time within a given spatial area and demonstrate impacts on agricultural output, industrial output, labor productivity, energy demand, health, conflict, and economic growth, among other outcomes. By harnessing exogenous variation over time within a given spatial unit, these studies help credibly identify (i) the breadth of channels linking weather and the economy, (ii) heterogeneous treatment effects across different types of locations, and (iii) nonlinear effects of weather variables. This paper reviews the new literature with two purposes. First, we summarize recent work, providing a guide to its methodologies, datasets, and findings. Second, we consider applications of the new literature, including insights for the "damage function" within models that seek to assess the potential economic effects of future climate change. ( JEL C51, D72, O13, Q51, Q54)
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TL;DR: The Integrated Assessment Models (IAMs) as mentioned in this paper combine general circulation models of climate and computable general equilibrium economic models to determine the interrelationship between climate and economic activity and policies that affect both of them.
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477 citations
TL;DR: In this article, a model-based approach for analyzing the possible effects of global change on Europe's hydropower potential at a country scale is presented, by comparing current conditions of climate and water use with future scenarios, an overview is provided of today's potential for hydroelectricity generation and its mid- and long-term prospects.
329 citations