Organic food products are produced without synthetic chemicals, including herbicides, pesticides, and fertilizers. Food grown in organic systems that are certified organic by the United States Department of Agriculture command a price premium, whether it is direct to consumer via farmers markets or in conventional grocery stores. Organic food and food products are representing a relatively larger portion of overall food sales in recent years, and the demand for organic meat has also increased. However, there is a lack of available U.S.-grown organic grains and soybeans to feed the growing number of organic certified livestock to produce organic meat to meet this demand. This shortage results from many factors, yet is primarily due to organic production requirements for significantly more land and operating capital when compared to conventionally grown counterparts. There is a lack of information detailing the relative costs and returns of organic grain production, and, limited understanding of organic premiums. The overall goal of this study is to examine differences in price levels between organic and conventional corn, soybeans, wheat, oats, and barley between 2007 and 2015, as well as factors that may affect the organic premium. For organic grain and soybean producers, study findings reveal that the least risky organic commodities to grow include corn and soybeans, especially if sold in the cash market. However, the author suggests that growers may consider growing wheat, barley, and oats if they have a buyer willing to contract in advance to ensure a premium and reduce price risk. For purchasers of organic grains and soybeans, including major food companies as well as livestock producers, it is recommended they continue to study developments in organic grain supplies as producers continue to consider adoption of organic production methods. / Master of Science

Yes / This paper investigates the impact of investor sentiment on the mean-variance relationship in 14 European stock markets. Applying three approaches to define investors’ neutrality and determine high and low sentiment periods, we find that individual investors’ increased presence and trading over high-sentiment periods would undermine the risk-return tradeoff. More importantly, we report that investors’ optimism (pessimism) is more determined by their normal sentiment state, represented by the all-period average sentiment level, rather than the neutrality value set in sentiment surveys.

This empirical study uses stochastic coefficients econometric modeling to forecast real exchange rate volatility and examine how expected and unexpected volatility affect bilateral trade flows of certain steel products between Canada, Mexico and the United States using monthly data for the seven-year period 1996-2002. The results of the model indicate that the effects of exchange rate volatility on bilateral trade flows for this sector are relatively minor, where sustained changes in the spot exchange rate, sectoral economic growth, and the price of goods being traded all exert more significant influence on trade levels than exchange rate volatility. However, the model results also tend to indicate that as exchange rate volatility increases, the well-developed U.S.-Canadian forward currency exchange market may present economic agents with profit opportunities through risk-portfolio diversification, resulting in a positive correlation between volatility and trade. For the less-developed U.S.-Mexican forward currency market, the model results indicate that the relationship between trade and volatility, both expected and unexpected, is weak and predominantly negative. / Master of Arts

An initial public offering (IPO) represents a significant event in a firm’s lifecycle, marking the transition from being a privately held company to a publicly traded entity by offering its shares to the public for the first time. Several previous studies have shown that, from an investor point of view, IPOs posits the opportunity to earn substantial return, and that they also tend to underperform long-term. In recent years, stock market volatility has fluctuated considerably due to factors such as the global pandemic and geopolitical conflicts. These factors have led to varying stock market returns, affecting individuals' savings. Additionally, the number of investors in Sweden has grown substantially over the past decade. This, combined with the relatively unexplored nature of market volatility in IPO research, has laid the foundation for this study's focus. Therefore, the purpose of this study is to assess the impact market volatility has on the initial return and the long-term risk-adjusted return of IPOs in Sweden.  To fulfill this purpose, analyses have been undertaken to investigate the relationship between IPO short- and long-term returns and market volatility between 2019 and 2022. This timeframe encapsulates two years experiencing low market volatility (2019 and 2021), and two years experiencing higher market volatility (2020 and 2022). The data sample consists of 165 firms when measuring short-term returns, and 162 firms when measuring long-term returns, who have all had their IPO within this timeframe and are all listed on the Swedish stock market. To further contribute to the literature, the study incorporates the Efficient Market Hypothesis (EMH), Prospect Theory, and the Winner´s Curse Theory. These are three well-established and contrasting theories within IPO research which are introduced to see how well their perspectives align with the study's findings.  The empirical results from the statistical analyses showed varied outcomes. While a statistically significant difference could be identified between certain years, the majority did not. Since the majority of the tests conducted could not find a significant difference in return between high and low volatile years, market volatility at the time of an IPO does not significantly influence the return. Consequently, the findings suggest that employing an investment strategy that involves investing in IPOs based on market volatility levels is not superior to other strategies. These findings give investors deeper insights into how IPOs and their timing are influenced by market conditions and can therefore aid them in making more informed decisions.

Yes / It is believed that financial markets are integrated and sensitive to news – including political conflicts in some regions of the world. Furthermore, financial markets seem to react differently to information flows from one region to another. The purpose of this research is to discern the effects of the recent Middle East and North Africa (MENA) conflicts – commonly referred to as the Arab Spring – on the volatility of risks and returns of global and regional stock markets as well as Gold and Oil markets. To be more specific, we consider the main uprisings in Tunisia, Egypt, Libya and Yemen and their impact on financial markets – as measured by the volatility of their risks and returns. In sum, we cluster 53 stock markets into 6 regions; namely, developed, developing, MENA, Asia, Europe, and Latin America countries, and use T-GARCH to assess the reaction of these regions to each uprising event independently. In addition, we use GARCH-M to assess the reaction of these regions stock markets as well as Gold and Oil markets to the uprisings of MENA as a whole. Our empirical findings suggest that the uprising events of MENA have more impact on the volatility of risks and returns of developed, developing, and Europe regions than MENA itself. In addition, although the results show that the volatility of both risks and returns of both developed and MENA regions are significantly affected by general conflicts in MENA, the volatility of MENA is affected during all intervals and with higher significance level. Furthermore, while MENA uprisings as a whole impact on the volatility of risk of oil (after 5 days) and gold (immediately after entering news) significantly, the returns of these markets are not affected by conflicts.

<p>This doctoral thesis is comprised of four papers that all relate to the subject of Time Series Analysis.</p><p>The first paper of the thesis considers point estimation in a nonnegative, hence non-Gaussian, AR(1) model. The parameter estimation is carried out using a type of extreme value estimators (EVEs). A novel estimation method based on the EVEs is presented. The theoretical analysis is complemented with Monte Carlo simulation results and the paper is concluded by an empirical example.</p><p>The second paper extends the model of the first paper of the thesis and considers semiparametric, robust point estimation in a nonlinear nonnegative autoregression. The nonnegative AR(1) model of the first paper is extended in three important ways: First, we allow the errors to be serially correlated. Second, we allow for heteroskedasticity of unknown form. Third, we allow for a multi-variable mapping of previous observations. Once more, the EVEs used for parameter estimation are shown to be strongly consistent under very general conditions. The theoretical analysis is complemented with extensive Monte Carlo simulation studies that illustrate the asymptotic theory and indicate reasonable small sample properties of the proposed estimators.</p><p>In the third paper we construct a simple nonnegative time series model for realized volatility, use the results of the second paper to estimate the proposed model on S&P 500 monthly realized volatilities, and then use the estimated model to make one-month-ahead forecasts. The out-of-sample performance of the proposed model is evaluated against a number of standard models. Various tests and accuracy measures are utilized to evaluate the forecast performances. It is found that forecasts from the nonnegative model perform exceptionally well under the mean absolute error and the mean absolute percentage error forecast accuracy measures.</p><p>In the fourth and last paper of the thesis we construct a multivariate extension of the popular Diebold-Mariano test. Under the null hypothesis of equal predictive accuracy of three or more forecasting models, the proposed test statistic has an asymptotic Chi-squared distribution. To explore whether the behavior of the test in moderate-sized samples can be improved, we also provide a finite-sample correction. A small-scale Monte Carlo study indicates that the proposed test has reasonable size properties in large samples and that it benefits noticeably from the finite-sample correction, even in quite large samples. The paper is concluded by an empirical example that illustrates the practical use of the two tests.</p>

The present thesis focuses on exploration of the applicability of realized measures in volatility modeling and forecasting. We provide a first comprehensive study of jump variation impact on future volatility of Central and Eastern European stock markets. As a main workhorse, the recently proposed Realized Jump GARCH model, which enables a study of the impact of jump variation on future volatility forecasts, is used. In addition, we estimate Realized GARCH and heterogeneous autoregressive (HAR) models using one-minute and five-minute high frequency data. We find that jumps are important for future volatility, but only to a limited extent due to the high level of information aggregation within the stock market index. Moreover, Realized (Jump) GARCH models outperform the standard GARCH model in terms of data fit and forecasting performance. Comparison of forecasts with HAR models reveals that Realized (Jump) GARCH models capture higher portion of volatility variation. Eventually, Realized Jump GARCH compared to other Realized GARCH models provides comparable or even better forecasting performance.

Master of Science / Department of Horticulture, Forestry, and Recreation Resources / Steven Keeley / Common dandelion (Taraxacum officinale Weber) is an important perennial weed in turfgrass. Fall is considered the optimal time for postemergence herbicidal control of dandelions; however, applications in spring, when volatility damage to surrounding plants is an additional concern, are often needed. Therefore, we conducted research to determine the volatility of common broadleaf herbicides, and their efficacy when applied at spring and fall application timings. Volatility was determined by applying herbicides to turfgrass and using potted tomatoes as indicator plants. Tomatoes exposed to turfgrass treated with Trimec Classic, Confront, Surge, Escalade 2, and Imprelis exhibited little or no volatility damage, while exposure to Speedzone, 4 Speed XT, and Cool Power caused significant damage. In general, herbicides causing little or no damage were amine formulations. Two field studies determined the effect of spring and fall application timing on dandelion control with several herbicides. Herbicide applications in the spring coincided with dandelion anthesis stages: pre-bloom, peak bloom, and post-bloom. Results were dependent on dandelion pressure in the studies. In 2010, with lower pressure, there were no differences among herbicides at any spring timing when dandelion control was evaluated after one year; all herbicides gave ≥ 80% control. In 2011, with higher dandelion pressure, Imprelis SL and 4 Speed XT provided ≥ 96% dandelion control at the spring pre- and post-bloom timings, which was better than Surge, Escalade 2, Cool Power, and Confront. The best choices for spring efficacy combined with minimal to no volatility were Escalade 2 and Trimec Classic. Finally, because interest in organic dandelion control is increasing, we compared several organic weed control tactics with a conventional herbicide. In a two-year field study, the conventional herbicide gave much better control (> 96%) than any organic method. Horticultural vinegar corn gluten meal, and fertilizer-only gave < 25% control, while hand-weeding gave 58 to 71% control. While hand-weeding was the best of the organic tactics, the time required was considered prohibitive for turfgrass managers, unless initial weed levels were very low.

Cílem této doktorské práce je analýza chování vybraných českých otevřených podílových fondů a akcií. Podílové fondy si od druhé poloviny 90. let získávají v České republice stále větší oblibu. Do konce roku 2006 dosáhl objem investic do podílových fondů 150 miliard korun. Empirická studie se věnuje třem typům podílových fondů: akciovým, dluhopisovým a peněžním a akcie. Denní hodnoty cen byly získány z internetových stránek správců fondů a RM-systému. Sledované období začíná 1.1.2001 a končí 31.12.2005. Akcie a podílové listy mají odlišné principy formování ceny. Zatímco ceny akcií se vytváří interakcí nabídky a poptávky na akciovém trhu, u podílových listů je cena odvozena z celkové hodnoty aktiv fondu. Vliv trhu není u podílových fondů významný, protože nabídka podílo-vých listů je téměř neomezená. Navíc jsou aktiva podílového fondu tvořena řadou rozdílných investičních nástrojů jako jsou české a zahraniční akcie, dluhopisy, pokladniční poukázky, instrumenty peněžních trhů atd. Zjištění, zda časové řady fondů mají i za těchto předpokladů stejné vlastnosti jako řady akcií a zda je pro jejich modelování vhodné použít modely vytvo-řené pro akcie, burzovní indexy nebo směnné kurzy, je hlavním tématem této práce. Pozornost je věnována nepodmíněnému rozdělení výnosů logaritmů cen podílových listů. Metodou maximální věrohodnosti jsou odhadnuty parametry teoretických rozdělení a poté je testována jejich shoda s rozdělením výnosů. Další rozdělení zmiňovaná v souvislosti s nepodmíněným rozdělením finančních časových řad jsou zmíněna v teoretické části. K mo-delování podmíněné střední hodnoty je využito modelů typu AR, k modelování podmíněného rozptylu pak lineárních modelů ARCH, GARCH a GARCH-M a nelineárních modelů typu GRJ-GARCH a EGARCH. Další modely volatility jsou popsány v jedné z úvodních kapitol. Skupina nelineárních modelů je do analýzy zahrnuta za účelem hledání ?pákového efektu?. Lineární model GARCH-M popisuje přímé působení podmíněného rozptylu časové řady na její podmíněnou střední hodnotu. Vzhledem k prokázané nenormalitě rozdělení reziduí, ne-jsou splněny počáteční podmínky modelů časových řad. Vhodnější modely lze získat změnou předpokladu o rozdělení nesystematické složky na GED nebo Studentovo t rozdělení. Na zá-kladě porovnání prostřednictvím informačních kritérií a u příbuzných modelů testem věrohodnostním poměrem je pro každou časovou řadu nalezen nejvhodnější model, který slouží k popisu jejích vlastností a v praxi může být využit i k předpovědi dalšího vývoje, v analýze Value at Risk nebo k popisu vývoje rizikovosti fondu. V závěru jsou popsány zjiš-těné společné a rozdílné vlastnosti podílových fondů a akcií a doporučení pro modelování těchto časových řad.

自1980年代後期Hull & White、Wiggins、Johnson & Shanno等人相繼發表關於隨機波動度模型的文獻後，就有諸多的文獻對於在選擇權定價中考慮隨機波動度作更深入的分析與模型探討，然而關於隨機波動度的研究，在早期大多採用蒙地卡羅模擬法來分析選擇權的價格行為，但蒙地卡羅模擬法受限於運算效率不高與缺乏彈性，故在評價新奇選擇權，如美式選擇權、障礙選擇權時，並無法應用。故本文以Leisen（2000）的二元樹狀模型出發，探討在不同相關係數及參數設定下之各類選擇權的定價、避險參數及隱含波動度曲面模擬計算等主題。 最後我們得到下面幾點結論： 1.在收斂速度與運算效率方面，我們可以發現二元樹狀模型在分割期數n大於20時，計算價格與收斂價格的差距就非常微小，而若我們計算不同切割期數的最大價格差異也會發現其實都不到百分之一，因此整體而言，收斂速度是令人非常滿意的。 2.當期初波動度提高時，會縮小價外選擇權與B-S價格之間的價格誤差。當到期期限增加時，隱含波動度曲線會有整體提高的趨勢。 3.若提高波動係數σ為2.5時，則不論相關係數的正負情形，價內外的程度，皆會大幅提高選擇權的隱含波動度。而在相關係數為-0.5的時候，可以發現實證中常觀察到的隱含波動度微笑曲線，這可能代表著市場上的波動係數比我們預期中的都還來的高。 4.在進行不同相關係數及不同價內外程度下二元樹狀與單元樹狀模型的美式選擇權價格比較時，我們可以發現，若以二元樹狀模型為正確價格，當相關係數為負的時候，在價外的時候，單元樹狀模型有價格低估的現象，在價內的時候，則有價格高估的現象，而在相關係數為正的時候，則反之。 5.Leisen二元樹狀與封閉解的歐式向上出局賣權價格比較，在特定的參數設定之下，Leisen二元樹狀模型在評價歐式向上出局賣權的時候，當相關係數為負的時候，在價外的時候，模型價格會高於封閉解，在價內的時候，模型價格則會低於封閉解，而在相關係數為正的時候，則反之。