THE CASE FOR AND COMPONENTS OF A PROBABILISTIC AGRICULTURAL OUTLOOK PROGRAM

An operational program to develop and disseminate probabilistic outlook information for agricultural commodities would allow decision makers to better comprehend the degree of uncertainty associated with future prices. While there are psychological limitations to the estimation or probabilities, this is a skill that can be taught and developed, particularly among experienced forecasters such as outlook specialists. Techniques are available for eliciting probabilities, and weather forecasting experience demonstrates that experts can quantify probabilities in a reliable manner. The components of a program to develop and disseminate outlook probabilities should include a survey of user needs, training programs for participating outlook specialists, and user educational programs. Further research is needed to develop elicitation techniques, and to evaluate costs and benefits.Teaching/Communication/Extension/Profession,

Risk as a primitive: A survey of measures of perceived risk

The concept of risk is essential to many problems in economics and business. Usually, risk is treated in the traditional expected utility framework where it is defined only indirectly through the shape of the utility function. The purpose of utility functions, however, is to model preferences. In this paper, we review those approaches which directly model risk judgements. After a short review of naive risk measures used in earlier economic literature, we present recent theoretical and empirical development

The valuation of companies in emerging markets: a behavioural view with a private company perspective

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2015.Researchers have suggested that emerging markets’ activity is driven largely by unlisted companies. These companies are dynamic, and show a relatively equitable income distribution. However, they operate under severe challenges which can be a deterrent to their success. In spite of these difficulties, the companies form exceptional investment targets due to their innovative abilities, ability to customize products and formulate business models that reduce bottlenecks and input costs as well as take advantage of economies of scale and scope. Important risk factors such as: political, currency, corporate governance and information risks, amongst others, should be factored in during the valuation process of emerging market companies. In this paper, several criteria are used to assess thirteen popular emerging market valuation models’ ability to effectively incorporate these risks. Based on the outcomes of the assessment a best fit model is selected. However, none of the emerging market valuation models explicitly factor in irrationality of market participants. In order to address this, the study focuses on seven behavioural approaches to valuation under the assumption of investor rationality and managerial overconfidence and/or optimism, with a purpose to select one to include in the above mentioned “best fit” emerging market valuation models. Next, assessment mechanisms for adapting these two models for private company valuation were flagged by discussing approaches currently used in academia and corporate finance. Finally, possible means of combining the three objectives, and assessing the success of doing so, as an area for further research, were recommended. Key Words: emerging markets, valuation, risk premium, country risk, systematic risk, unsystematic risk, private companies, managerial overconfidence, managerial optimism, irrationality, efficient markets, capital asset pricing mode

Portfolio optimization with quantile-based risk measures

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 175-179).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.In this thesis we analyze Portfolio Optimization risk-reward theory, a generalization of the mean-variance theory, in the cases where the risk measures are quantile-based (such as the Value at Risk (V aR) and the shortfall). We show, using multicriteria theory arguments, that if the measure of risk is convex and the measure of reward concave with respect to the allocation vector, then the expected utility function is only a special case of the risk-reward framework. We introduce the concept of pseudo-coherency of risk measures, and analyze the mathematics of the Static Portfolio Optimization when the risk and reward measures of a portfolio satisfy the concepts of homogeneity and pseudo-coherency. We also implement and analyze a sub-optimal dynamic strategy using the concept of consistency which we introduce here, and achieve a better mean-V aR than with a traditional static strategy. We derive a formula to calculate the gradient of quantiles of linear combinations of random variables with respect to an allocation vector, and we propose the use of a nonparametric statistical technique (local polynomial regression - LPR) for the estimation of the gradient. This gradient has interesting financial applications where quantile-based risk measures like the V aR and the shortfall are used: it can be used to calculate a portfolio sensitivity or to numerically optimize a portfolio. In this analysis we compare our results with those produced by current methods. Using our newly developed numerical techniques, we create a series of examples showing the properties of efficient portfolios for pseudo-coherent risk measures. Based on these examples, we point out the danger for an investor of selecting the wrong risk measure and we show the weaknesses of the Expected Utility Theory.by Gerardo José Lemus Rodriguez.Ph.D

Extreme Rainfall Events: Incorporating Temporal and Spatial Dependence to Improve Statistical Models

The proper design of protective measurements against floods related to heavy precipitation has long been a question of interest in many fields of study. A crucial component for such design is the analysis of extreme historical rainfall using Extreme Value Theory (EVT) methods, which provide information about the frequency and magnitude of possible future events. Characterizing an entire basin or geographical catchment requires the extension of univariate EVT methods to capture the spatial variability of the data. This extension requires that the similarity of the data for nearby stations be included in the model, resulting in more efficient use of the data. This dissertation focuses on using statistical models incorporating spatial dependence for modeling annual rainfall maxima. Additionally, we present ways of adapting the models to capture the dependence between rainfall of different time scales. These models are used in order to pursue two aims. The first aim is to improve our understanding of the mechanisms that lead to dependence on extreme rainfall. The second aim is to improve the resulting estimates when incorporating the dependence into the models. Two published studies make up the main findings of this dissertation. The models used in both studies involve the use of Brown-Resnick max-stable processes, allowing the models to explicitly account for the dependence on either the temporal or the spatial domain. These conditional models are compared for both cases to a model that ignores the dependence, allowing us to determine the impact of the dependence in both situations. Contributions to three other studies using the concept of dependence are also summarized. In the first study, we assess the impact of including the dependence between rainfall series of different aggregation durations when estimating Intensity-Duration-Frequency curves. This assessment was done in a case study for the Wupper catchment in Germany. This study found that including the dependence in the model had a positive effect on the prediction accuracy when focusing on rainfall with short durations (d <= 10h) and large probabilities of non-exceedance. Therefore, we recommend using max-stable processes when a study focuses on short-duration rainfall. In the second study, we investigate how the spatial dependence of extreme rainfall in Berlin-Brandenburg changes seasonally and how this change could impact the estimates from a max-stable model that includes this dependence. The seasonality was determined by estimating the parameters of a summer and winter semi-annual block maxima model. The results from this study showed that, for the summer maxima, the dependence structure was adequately captured by an isotropic Brown-Resnick model. On the contrary, the same model performed poorly for the winter maxima, suggesting that a change in the assumptions is needed when dealing with typical winter events, typically frontal or stratiform for this region. These results show that accounting for the meteorological properties of the rainfall-generating processes can provide useful information for the design of the models. Overall, our findings show that including meteorological knowledge in statistical models can improve their resulting estimations. In particular, we find that, under certain conditions, using statistical dependence to incorporate knowledge about the differences in temporal and spatial scales of rainfall-generating mechanisms can lead to a positive impact in the models.Die richtige Auslegung von Schutzmaßnahmen gegen Überschwemmungen im Zusammenhang mit Starkniederschlägen ist seit langem eine Frage, die in vielen Studienbereichen von Interesse ist. Eine entscheidende Komponente für eine solche Planung ist die Analyse extremer historischer Niederschläge mit Methoden der Extremwertstatistik, die Informationen über die Häufigkeit und das Ausmaß möglicher künftiger Ereignisse liefern. Die Charakterisierung eines ganzen Einzugsgebiets oder einer geografischen Einheit erfordert die Erweiterung der univariaten Extremwerstatistik-Methoden, um die räumliche Variabilität der Daten zu erfassen. Diese Erweiterung erfordert, dass die Ähnlichkeit der Daten für nahe gelegene Stationen in das Modell einbezogen wird, was zu einer effizienteren Nutzung der Daten führt. Diese Dissertation konzentriert sich auf die Verwendung statistischer Modelle, die die räumliche Abhängigkeit bei der Modellierung von jährlichen Niederschlagsmaxima berücksichtigen. Darüber hinaus werden Möglichkeiten zur Anpassung der Modelle vorgestellt, um die Abhängigkeit zwischen Niederschlägen auf verschiedenen Zeitskalen zu erfassen. Diese Modelle werden zur Verfolgung zweier Ziele eingesetzt. Das erste Ziel besteht darin, unser Verständnis der Mechanismen zu verbessern, die zur Abhängigkeit von extremen Niederschlägen führen. Das zweite Ziel besteht darin, die resultierenden Schätzungen zu verbessern, wenn die Abhängigkeit in die Modelle einbezogen wird. Zwei veröffentlichte Studien bilden die wichtigsten Ergebnisse dieser Dissertation. Die in beiden Studien verwendeten Modelle basieren auf max-stabilen Brown-Resnick-Prozessen, die es den Modellen ermöglichen, die Abhängigkeit entweder auf der zeitlichen oder auf der räumlichen Ebene ausdrücklich zu berücksichtigen. Diese bedingten Modelle werden für beide Fälle mit einem Modell verglichen, das die Abhängigkeit ignoriert, so dass wir die Auswirkungen der Abhängigkeit in beiden Situationen bestimmen können. Es werden auch Beiträge zu drei anderen Studien zusammengefasst, die das Konzept der Abhängigkeit verwenden. In der ersten Studie bewerten wir die Auswirkungen der Einbeziehung der Abhängigkeit zwischen Niederschlagsreihen unterschiedlicher Aggregationsdauern bei der Schätzung von Intensitäts-Dauer-Frequenz-Kurven. Diese Bewertung wurde in einer Fallstudie für das Einzugsgebiet der Wupper in Deutschland durchgeführt. Diese Studie ergab, dass sich die Einbeziehung der Abhängigkeit in das Modell positiv auf die Vorhersagegenauigkeit auswirkt, wenn man sich auf Niederschläge mit kurzen Dauern (d <= 10 h) und großer Nichtüberschreitungwahrscheinlichkeit konzentriert. Daher empfehlen wir die Verwendung von max-stabilen Prozessen, wenn sich eine Studie auf Regenfälle von kurzer Dauer konzentriert. In der zweiten Studie untersuchen wir, wie sich die räumliche Abhängigkeit von Extremniederschlägen in Berlin-Brandenburg saisonal verändert und wie sich diese Veränderung auf die Schätzungen eines max-stabilen Modells auswirken könnte, das diese Abhängigkeit berücksichtigt. Die Saisonalität wurde durch die Schätzung der Parameter eines halbjährlichen Sommer- und Winter-Blockmaxima-Modells bestimmt. Die Ergebnisse dieser Studie zeigten, dass die Abhängigkeitsstruktur für die Sommermaxima durch ein isotropes Brown-Resnick-Modell angemessen erfasst wurde. Im Gegensatz dazu zeigte dasselbe Modell eine schlechte Leistung für die Wintermaxima, was darauf hindeutet, dass eine Änderung der Annahmen erforderlich ist, wenn es um typische Winterereignisse geht, die in dieser Region typischerweise frontal oder stratiförmig sind. Diese Ergebnisse zeigen, dass die Berücksichtigung der meteorologischen Eigenschaften der Niederschlagsprozesse nützliche Informationen für die Gestaltung der Modelle liefern kann. Insgesamt zeigen unsere Ergebnisse, dass die Einbeziehung von meteorologischem Wissen in statistische Modelle die daraus resultierenden Schätzungen verbessern kann. Insbesondere stellen wir fest, dass unter bestimmten Bedingungen die Nutzung der statistischen Abhängigkeit zur Einbeziehung von Wissen über die Unterschiede in den zeitlichen und räumlichen Skalen der regenerzeugenden Mechanismen zu einer positiven Wirkung in den Modellen führen kann

Project selection considering delayed acceptance of investment projects

M.S.Gunter P. Shar

Balancing Ecological and Economic Objectives in Land Use and Management: Modeling to Identify Sustainable Spatial Patterns.

Human-driven land-use/cover (LULC) changes threaten the integrity of ecosystems in many ways. To evaluate possible impacts of future changes in LULC on ecosystem services and support more sustainable environmental management, it is essential to understand how land-use patterns affect both ecological and economic outcomes, and how alternative spatial land-use and -management strategies may improve sustainability in land-use systems. I developed and tested a spatial simulation approach that can help improve our understanding of how human-driven landscape conditions at the watershed scale might reshape impacts on both water quality and economic performance in a Lake Erie watershed under a changing climate. The dissertation is organized into three chapters. The first chapter describes a study in which I evaluated sensitivity of a stochastic land-change model (LCM) to pixel versus polygonal land unit derived from parcel maps. Performance of pixel- and polygon-based simulations suggest that using polygonal unit is helpful with generating more realistic landscape patterns, but at the cost of spatial allocation accuracy. For the second chapter, I developed the first integrated modeling approach that compares the relative economic efficiency of alternative spatial land-use and -management strategies for addressing non-point source (NPS) nutrient pollution. Using the Soil Water Assessment Tool (SWAT) and data on crop costs and prices, I evaluated joint impacts on nutrient reduction and economic returns for optimized patterns of land-use changes (LUCs) versus conservation practices (CPs) at the field scale. Simulated results showed relying on CPs alone might not be sufficient to restore water quality in Lake Erie, and a combination strategy including both LUCs and CPs would be necessary and more efficient. Finally, I examined sensitivity of optimized spatial patterns of land-use and -management (CPs) approaches to climate change. I found optimal land-use and -management placement can be quite sensitive to change in climatic conditions. CP targeting was found to be more robust to climate change than land-use change, but integration of both strategies would be necessary to achieve high DRP reduction (>65%) targets. Results from this study highlight the need for future spatial optimization studies to consider adaptive capacity of conservation actions under a changing climate.PhDNatural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133330/1/xuhui_1.pd

The M-Scale Model: A Multi-Scale Model for Decision Support of On-Site Remediation.

Remedial decisions for sediment management involve knowledge of the biogeochemical processes affecting contaminant fate and transport in the sediment, as well as the spatial distribution of the contaminant. Spatial statistical models provide estimates of the spatial distribution, and the results depend on validity of the assumptions inherent to the selected statistical tools, and the appropriateness of these tools with respect to the objectives of the estimation. Most decision tools for site assessment depend on spatial estimation/simulation that either misinterpret the extent of exceedance, or assigns a single decision map corresponding to a given uncertainty criterion. The specific objectives of this work are (i) to provide a spatial statistical approach, the M-Scale model, for characterization of the spatial structure and spatial distribution of an attribute, such as contaminant concentration or microbiological parameters; (ii) to investigate the applicability of the developed model to field data relevant to contaminated sediments using various performance diagnostics; and (iii) to explore the sensitivity of the M-Scale model and other methods to the nugget effect (artificially induced error and micro-scale variability) using laboratory and field data from the Anacostia River (NJ). Results using artificial data indicate the developed model generates estimates that (i) reproduce spatial variability evident in the sample, with reasonable precision for classifying exceedance/non-exceedance of a design threshold, and (ii) reproduce the overall attribute value distribution. Cross-validation results using datasets from the Passaic River yield similar performance metrics for the M-Scale model relative to CK in the reproduction of the overall value distribution, and relative to OK in the precision of classification. Estimation results using samples at both the site-scale and the micro-scale from the Anacostia River further indicate the possibility of reducing the uncertainty associated with estimates by characterizing the actual micro-scale variability. Cross-validation results using the same datasets indicate that each data point in a small-size sample set is essential in the estimation process. The reproduction of spatial variability demonstrated in this dissertation indicates improvement of spatial estimation by characterizing multi-scale covariances of means. The model has broad applicability for situations where multi-scale characterization issues drive spatial management decisions.Ph.D.Environmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58473/1/mengyl_1.pd

Irrigation, water market and climate change: three essays

Irrigation water is vital for agriculture, but climate change presents substantial challenges to its management. This thesis comprises three studies that examine the multidimensional challenges of irrigation water governance, the functionality of water markets, and their potential contribution to mitigating the impacts of climate change. The studies also investigate the effects of climatic conditions on the trading behaviour of water market participants. The first study presents a comprehensive framework for assessing the ability of water governance to cope with climate change. My findings indicate an improvement in the economic efficiency of irrigation water use over the past few decades and the contribution of market-based instruments in managing the impact of climate change. The second study takes a closer look at the functionality and performance of the Murray-Darling Basin (MDB) water market in Australia. The study investigates several key market attributes across a number of trading zones in the sMDB. Overall, the findings document that water markets serve well their fundamental purpose in water resource management, and that various products available in the market enhance market efficiency. The third study uses a portfolio approach to analyse the impacts of climatic conditions, particularly water availability, on the optimal trading strategies of water market participants. The findings illustrate the benefits of portfolio management in improving returns, reducing risks and securing water supply, as opposed to the traditional ownership of a single type of water right. In summary, this thesis addresses the challenges that irrigation water governance confronts in the context of climate change and provides in-depth discussions about potential tools to deal with the challenges. My research highlights the crucial role of economic instruments, particularly water markets, in mitigating these challenges, based on empirical evidence and optimization results

Measuring the Risk of Shortfalls in Air Force Capabilities

The U.S. Air Force seeks to measure and prioritize risk as part of its Capabilities Review and Risk Assessment (CRRA) process. The goal of the CRRA is to identify capability shortfalls, and the risks associated with those shortfalls, to influence future systems acquisition. Many fields, including engineering, medicine and finance, seek to model and measure risks. This research utilizes various risk measurement approaches to propose appropriate risk measures for a military context. Specifically, risk is modeled as a non-negative random variable of severity. Four measures are examined: simple expectation, a risk-value measure, tail conditional expectation, and distorted expectation. Risk measures are subsequently used to weight the objective function coefficients in a system acquisition knapsack problem