MANAGING PHOSPHOROUS SOIL DYNAMICS OVER SPACE AND TIME

Understanding the relationship between soil fertility dynamics and crop response is conceptually appealing. Even more appealing is comprehension of the spatial and temporal heterogeneity of these connections over a production surface and across seasons. Knowledge of these interactions is complicated because nutrient carryover dynamics and crop response to inputs are determined simultaneously on the one-hand, and sequentially on the other. A second problem enters when crops are rotated, for example, in the corn-soybean system commonly practiced in the Corn Belt. This paper examines the nutrient carryover-crop response nexus using data from a corn-soybean, variable-rate nitrogen (N) and phosphorous (P) experiment conducted over five years. Site-specific corn response to N and P and soybean response to P are simultaneously estimated with a P carryover equation. These estimates are used in a dynamic programming model to map site-specific optimal N and P fertilizer policies, soil P evolution, and profitability. The net present value of managing N and P site-specifically is compared to a strategy where these inputs are managed uniformly following extension guidelines. The results suggest that when P-carryover is managed, site-specific returns to the variable-rate strategies are higher than returns to a conventional, uniform strategy.Crop Production/Industries,

Export diversification and resource-based industrialization: the case of natural gas

For resource-rich economies, primary commodity specialization has often been considered to be detrimental to growth. Accordingly, export diversification policies centered on resource-based industries have long been advocated as effective ways to moderate the large variability of export revenues. This paper discusses the applicability of a mean-variance portfolio approach to design these strategies and proposes some modifications aimed at capturing the key features of resource processing industries (presence of scale economies and investment lumpiness). These modifications help make the approach more plausible for use in resource-rich countries. An application to the case of natural gas is then discussed using data obtained from Monte Carlo simulations of a calibrated empirical model. Lastly, the proposed framework is put to work to evaluate the performances of the diversification strategies implemented in a set of nine gas-rich economies. These results are then used to formulate some policy recommendations

Estimating the Resilience Value of Soil Biodiversity in Agriculture: A Stochastic Simulation Approach

Characteristic of ecosystems is that different organisms can have similar functions and hence provide similar ecosystem services. Consequently functional diversity can maintain the rate of services despite environmental fluctuations. In this paper we present a method for estimating the resilience value of biodiversity. Central to a resilience perspective on biological conservation is consideration of uncertainty about the future. To do this we propose stochastic simulation as a practical approach for valuing resilience due to the ease of incorporating uncertain variables. We demonstrate the approach by developing a stochastic simulation model for valuing soil biodiversity in agriculture. Our results indicate that the long time frames involved in soil processes create a significant incentive to perpetuate unsustainable farming systems and hence there might be a need for policy intervention. However we also show that investing in soil biodiversity conservation can provide significant risk diversification benefits that are not accounted for in a deterministic evaluation. These benefits can be estimated through stochastic simulation.Resource /Energy Economics and Policy,

Ambiguity in asset pricing and portfolio choice: a review of the literature

A growing body of empirical evidence suggests that investors’ behavior is not well described by the traditional paradigm of (subjective) expected utility maximization under rational expectations. A literature has arisen that models agents whose choices are consistent with models that are less restrictive than the standard subjective expected utility framework. In this paper we conduct a survey of the existing literature that has explored the implications of decision-making under ambiguity for financial market outcomes, such as portfolio choice and equilibrium asset prices. We conclude that the ambiguity literature has led to a number of significant advances in our ability to rationalize empirical features of asset returns and portfolio decisions, such as the empirical failure of the two-fund separation theorem in portfolio decisions, the modest exposure to risky securities observed for a majority of investors, the home equity preference in international portfolio diversification, the excess volatility of asset returns, the equity premium and the risk-free rate puzzles, and the occurrence of trading break-downs.Capital assets pricing model ; Investments

Antecipação na tomada de decisão com múltiplos critérios sob incerteza

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

The Introduction of the Euro and its Effects on Investment Decisions

In this paper we examine changes on investment decisions induced by the introduction of the Euro. There are two potential sources of portfolio reallocation. First, the introduction of the Euro diminished exchange rate risks within the EMU region, which relieved European investors from currency risk associated with intra-EMU investments. Second, monetary policy has been bundled within one single institution, which increased the correlation of different national stock and bond market returns. We test for structural breaks in the portfolio holdings of German investors and estimate a market model in the latter in order to account for the two described effects. We observe a significant decrease in national and an significant increase in intra-EMU as well as US investments. Therefore, the establishment of the EMU led to a decrease of investment home bias. --investment home bias,realized volatility,Euro introduction

Evaluating multivariate volatility forecasts

The performance of techniques for evaluating univariate volatility forecasts are well understood. In the multivariate setting however, the efficacy of the evaluation techniques is not developed. Multivariate forecasts are often evaluated within an economic application such as portfolio optimisation context. This paper aims to evaluate the efficacy of such techniques, along with traditional statistical based methods. It is found that utility based methods perform poorly in terms of identifying optimal forecasts whereas statistical methods are more effective.Multivariate volatility, forecasts, forecast evaluation, Model confidence set

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Dollarization Persistence and Individual Heterogeneity

The most salient feature of financial dollarization, and the one that causes more concern to policy makers, is its persistence: even after successful macroeconomic stabilizations, dollarization ratios often remain high. In this paper we claim that this persistence is connected to the fact that the participants in the dollar deposit market are fairly heterogenous, and so is the way they form their optimal currency portfolio.We develop as simple model when agents differ in their ability to process information, which turns out to be enough to generate persistence up on aggregation. We find empirical support for this claim with data from three Latin American countries and Poland.Dollarization, individual heterogeneity, persistence, aggregation

A Comparative Study of Portfolio Optimization Methods for the Indian Stock Market

This chapter presents a comparative study of the three portfolio optimization methods, MVP, HRP, and HERC, on the Indian stock market, particularly focusing on the stocks chosen from 15 sectors listed on the National Stock Exchange of India. The top stocks of each cluster are identified based on their free-float market capitalization from the report of the NSE published on July 1, 2022 (NSE Website). For each sector, three portfolios are designed on stock prices from July 1, 2019, to June 30, 2022, following three portfolio optimization approaches. The portfolios are tested over the period from July 1, 2022, to June 30, 2023. For the evaluation of the performances of the portfolios, three metrics are used. These three metrics are cumulative returns, annual volatilities, and Sharpe ratios. For each sector, the portfolios that yield the highest cumulative return, the lowest volatility, and the maximum Sharpe Ratio over the training and the test periods are identified.Comment: This is the draft version of the chapter that has been accepted for publication in the edited volume titled "Data Science: Theory and Practice". The volume is edited by Jaydip Sen and Sayantani Roy Choudury and will be published by IntechOpen, London, UK. The chapter is 74 pages long and it contains 32 tables and 62 figure