DEALING WITH RISK IN AGRICULTURE: A CROP LEVEL ANALYSIS AND MANAGEMENT PROPOSAL FOR ITALIAN FARMS

Risk management plays a critical role in agriculture, which is particularly exposed to multiple and heterogeneous risk factors. In addition to the traditional basic risks that generally characterize any business venture, agriculture faces external factors, generally difficult to control and with a strong impact on farm profitability. These are firstly environmental (pests and diseases) and climatic conditions that affect the quantity and quality of agricultural production, but also the structural constraints of the agricultural market, which is characterised by a high degree of supply rigidity, price volatility and inelasticity of demand. This leads to the need to implement risk management tools, some of which aimed at income stabilization (already in place by many years in other countries, i.e. the USA and Canada) and requiring the active participation of the farmer on the one hand and of the institutional system on the other. In order to suggest risk management solutions to Italian farmers, this thesis makes efforts in simulating the feasibility of a risk management tool introduced in the EU with Regulation (EU) No 2017/2393 but not yet implemented: the sector-specific Income Stabilization Tool. This is based on a public-private partnership and is managed by a mutual fund steered by associated farmers. These latter pay an annual contribution to become eligible for receiving indemnities when experiencing a severe income drop. Unlike others that are limited to covering specific types of risk, this tool makes it possible to look at the farmer's entire income risk considering the correlation among several sources of risk (particularly between production level and prices). This thesis provides first a theoretical background on risk analysis and risk management in agriculture (concepts, classification, literature and methodology). Second, the role of policies within the European Union framework and, Italy, in particular, has been viewed by analysing the normative framework and the reference context of insurance instruments in agriculture. Subsequently, since assessing farm profitability and economic risk is important to support farmers’ decisions about investments and whether or not to join the insurance instruments, an explorative analysis on profitability and riskiness of a perennial crop in Italy, such as hazelnut, has been done. Finally, the implementation of a sector-specific 3 Income Stabilization Tool for the crop investigated has been suggested by following this structure: - assessment of the profitability and risk of hazelnut production, in the four main production areas in Italy; - assessment of the most important parameters generating risk; - simulation of the feasibility of using an income risk management tool to make supply and demand able to interact and its impact on the level and riskiness of farm income; - assessment of the geographical scale at which the Income Stabilization Tool scheme could be implemented. Using data from the Italian Farm Accountancy Data Network on hazelnut producing farms, a downside risk analysis showed that riskiness is distributed in different ways on the entire country with sensitivity on yield risk affecting farmers' income level and economic risk. The simulation implemented in this study demonstrates the tool could reduce substantially the risk faced by hazelnut farmers in Italy. The additional public support is essential in case of joining the tool. In addition, in view of the differences within the Italian territory, the farmers’ payments should be differentiated based on the requisites and the specific climatic and environmental characteristics of each region. Concurrently, recourse to a national mutual fund would make it possible to benefit from the principle of risk pooling

Harnessing innovation for open flood risk models and data

The harnessing of recent and future innovations in designing effective risk platforms, models, and data is essential to the reduction of flood risk. Doing so ensures those assets can gain long-term traction for disaster risk reduction, while realistically reflecting an uncertain world. Although the central concepts of flood risk analysis are well established, fragmentation around them is considerable in the data and models used in practice. The following discusses the reasons for this fragmentation and provides recommendations for addressing it

Examining the decision-relevance of climate model information for the insurance industry

The insurance industry is becoming increasingly exposed to the adverse impacts of climate variability and climate change. In developing policies and adapting strategies to better manage climate risk, insurers and reinsurers are therefore engaging directly with the climate modelling community to further understand the predictive capabilities of climate models and to develop techniques to utilise climate model output. With an inherent interest in the present and future frequency and magnitude of extreme climate-related loss events, insurers rely on the climate modelling community to provide informative model projections at the relevant spatial and temporal scales for insurance decisions. Furthermore, given the high economic stakes associated with enacting strategies to address climate change, it is essential that climate model experiments are designed to thoroughly explore the multiple sources of uncertainty. Determining the reliability of model based projections is a precursor to examining their relevance to the insurance industry and more widely to the climate change adaptation community. Designing experiments which adequately account for uncertainty therefore requires careful consideration of the nonlinear and chaotic properties of the climate system. Using the well developed concepts of dynamical systems theory, simple nonlinear chaotic systems are investigated to further understand what is meant by climate under climate change. The thesis questions the conventional paradigm in which long-term climate prediction is treated purely as a boundary value problem (predictability of the second kind). Using simple climate-like models to draw analogies to the climate system, results are presented which support the emerging view that climate prediction ought to be treated as both an initial value problem and a boundary condition problem on all time scales. The research also examines the application of the ergodic assumption in climate modelling and climate change adaptation decisions. By using idealised model experiments, situations in which the ergodic assumption breaks down are illustrated. Consideration is given to alternative model experimental designs which do not rely on the assumption of ergodicity. Experimental results are presented which support the view that large initial condition ensembles are required to detail the changing distribution of climate under altered forcing conditions. It is argued that the role of chaos and nonlinear dynamic behaviour ought to have more prominence in the discussion of the forecasting capabilities in climate prediction

Stochastic mortality in a complex world: methodologies and applications within the affine diffusion framework

In this Thesis, we address the modelling of stochastic mortality, a key issue for life insurance, pension funds, public policy and fiscal planning. Indeed, the prospective increase of longevity can be an advantage for individuals, but it represents also a relevant social achievement. The stability and consistency of social welfare systems are put in danger worldwide due to the combined phenomenon of the progressive increase in life expectancy, along with the reduction of birth-rates in industrialized Countries. This phenomenon needs to be interpreted in the context of the connected world in which we live, where the multiple networks arising from the globalization, the Internet communication and the global economic development propagate any event in a very short time, making risks more complex. Due to their very nature, insurance and reinsurance deal with several risks on their balance sheet and, when determining the total risk of a portfolio, they need to establish the rules for aggregating the various risks that compose it. The introduction of market-consistent accounting and risk-based solvency requirements has called for the integration of mortality risk analysis into stochastic valuation models; moreover mortality-linked securities have attracted the interest of capital market investors, who in turn demand transparent tools to price demographic and financial risks in an integrated fashion. Accordingly, a coherent mathematical framework for studying the changes in financial and demographic conditions over time, is suitable. The class of the affine processes has been used in a wide range of applications in financial and actuarial sciences, thanks to its computational tractability and flexibility. For instance, affine processes have been extensively used in modelling the term structure of interest rates, that underpin extensive literatures on the pricing of bonds and interest-rate derivatives and are also at the basis of many of the pricing systems used by the financial industry. Affine models for the force of mortality have been developed in the literature under the assumption of both dependence and independence between mortality and interest rate dynamics. The core of this Thesis are the affine models and their properties for modelling the evolution of mortality. We propose and discuss two contributions: (i) we fit and compare past mortality trends among different Countries under the mathematical framework of the Feller process; (ii) we design a multiplicative affine model for the future evolution of mortality, by combining two components: the forecast provided by any existing mortality model, representing the deterministic baseline, and an affine driving process that stochastically affects the baseline over the forecasting time horizon. The so structured model not only is affine, thus fitting well our targets, but, when assessing its forecasting performance, it proves to be parsimonious and to provide a more accurate forecast with respect to the baseline. Within such a model, the affine driving factor is tasked with describing the dynamics over time of a measure of the fitting error of the existing mortality model providing the baseline and it is stochastically described by a Cox-Ingersoll-Ross process. For our numerical application, we choose, as the existing mortality model giving the baseline, the Cairns-Blake-Dowd (or M5) model, that is combined with the CIR process describing the stochastic factor affecting the baseline in a multiplicative way. The resulting model is called mCBD. Using the Italian females mortality data, for fixed ages, and implementing the backtesting procedure, over both a static time horizon and fixed-length windows rolling one-year ahead through time, we empirically test the performance of the CBD and the mCBD models in forecasting death rates. On the basis of average measures of forecasting errors and information criteria, we demonstrate that the mCBD model is a parsimonious model providing better results in terms of predictive accuracy than the CBD model and showing a stronger potential to gain accuracy in the long-run when a rolling windows analysis (dynamic approach) is performed. To conclude, in the Thesis, we explore and test the properties and capabilities of some affine models in fitting and forecasting mortality data both by themselves and as dynamic driving processes multiplying a deterministic baseline. Combining models and mixing techniques prove to give satisfactory results and show a concrete potential to bring the research forward. Our future research is thus oriented to use approaches that combine Monte Carlo simulations and benefit from the synergy between different techniques

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change

This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies

The WWRP Polar Prediction Project (PPP)

Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

Enterprise risk management and firm performance: developing risk management measurement in accounting practice

The current extremely volatile business world requires firms to deal with a wide range of risks that pose threats to their organisations. The poor practices of risk management, based on Traditional Risk Management (TRM), was cited time and time again in the aftermath of the recent Global Crisis. Enterprise Risk Management (ERM) has been advocated as a solution to the problems of TRM. The aim is to centralise the management of risk within the organisation and ensure that the board deals with the risk. Hence strategic, external, internal, operational, compliance and reputational risk are dealt with jointly. In doing so, it is expected that ERM will bring value creation to firms. One of the main limitations facing researchers is the lack of a good standardised measurement of ERM implementation; therefore, it has not been possible to establish whether ERM does actually bring benefit to firms. In addition, many companies have set up ERM initiatives, but they lack a clear understanding of the factors that will lead to successful ERM implementation. The remaining unanswered problematic situation has led to two unanswered questions that will determine whether the solution to ERM implementation is avoiding potential pitfalls and improving business sustainability. Firstly, does ERM implementation have an impact on firm performance? And secondly, which is the firm-specific characteristic that leads to better ERM implementation level? This thesis answers the aforementioned questions by proposing a reliable ERM measurement method, and then testing whether firms that adopt ERM actually improve financial performance and determine the influential factor of ERM implementation. The proposed method for measuring ERM implementation is based on the components developed from the current ERM frameworks, where contribution scoring can be standardised to measure ERM implementation level. To demonstrate its viability, data was collected from publicly listed firms in Thailand and was then compared to three alternative methodologies: cluster analysis (CA), principal component analysis (PCA) and partial least squares (PLS). The results show that the proposed method did well compared to the alternatives, both statistically and in prediction performance. The relationship between the proposed ERM measurement and firm performance is then considered by taking appropriate control variables into account, such as the firm’s size and characteristics, industry effects, sales growth and the external environment: technology, market uncertainty, as well as economic factors. By using data from the Thailand Stock Exchange, it was found that implementing ERM could improve firm performance in term of Tobin's Q, ROE and ROA. The results show that ERM and firm performance are related. For the influential factor of ERM implementation, the empirical results show that a firm’s size and economic factors have a statistically positive relationship with a high level of ERM implementation, while lower ERM scores show more revenue volatility than those who have well-implemented ERMs. Furthermore, technology and growth are positively related to each ERM in the scoring system considered

Undergraduate and Graduate Course Descriptions, 2021 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2021

Undergraduate and Graduate Course Descriptions, 2021 Fall

Wright State University undergraduate and graduate course descriptions from Fall 2021