<p>  </p> <p>Since the commercialization of dicamba-resistant soybean and cotton, numerous instances of suspected off-target dicamba movement onto sensitive plant species have been reported. Further investigation into chemical and environmental factors that influence dicamba volatilization is warranted to better understand the mechanisms that lead to increased dicamba off-target movement via volatilization and plant response to dicamba vapor. The environmental impacts of dicamba must be minimized in order to ensure the sustainability and continued use of dicamba, which is an important herbicide for controlling broadleaf weeds in key cropping systems and non-crop sites. </p> <p>Controlled environment experiments were conducted to characterize the chemical interactions with dicamba volatility for three formulations of dicamba on glass slides. Dicamba volatility was similar for spray solution pH levels 4 to 8 for the diglycolamine (DGA) and the diglycolamine with VaporGrip® (DGA+VG) formulations. For the N,N-Bis-(3- aminpropyl) methylamine (BAPMA) formulation, dicamba volatility increased at a pH level of 5 with continued increases in volatility occurring as spray solution continued to decrease indicating that BAPMA formulation is more sensitive to pH fluctuations than the DGA and the DGA+VG formulations. While spray solution pH levels below 4 increased dicamba volatility for all three formulations compared to each formulation applied at a native pH level (5.53, 5.2, and 6.28 for the DGA, DGA+VG, and BAPMA formulations, respectively), the largest increase in dicamba volatility occurred when ammonium or iron was added to spray solution. Therefore, applicators should avoid mixing dicamba with other tank-mix partners that contain ammonium or iron to minimize the likelihood for dicamba volatilization. </p> <p>While extensive research exists documenting the process of dicamba volatilization, there has been little confirmation regarding how volatilized dicamba enters sensitive plants. Dicamba-sensitive (DS) soybean with different levels of canopy conductance, from different watering regimes and exposure time of day, were exposed to dicamba vapor. The DS soybean response was positively correlated with soybean canopy conductance during the dicamba vapor exposure suggesting that dicamba vapor route of entry into soybean is facilitated through the stomata. An additional experiment was conducted that exposed the single side of a hypostomatic leaf to dicamba vapor on different northern red oak trees. Northern red oak tree response was substantially greater when the abaxial leaf surface (high stomatal density) was exposed to dicamba vapor compared to when the adaxial leaf surface (no stomata) was exposed to dicamba vapor. Thus, dicamba vapor entry into plants is largely facilitated via stomata and secondly through re-deposition onto the leaf surface, where dicamba is absorbed through the plant cuticle, albeit minor. If dicamba vapor is redeposited onto leaf surfaces, dicamba acid absorption through the cuticle can be limited without the presence of a surfactant. Field and greenhouse experiments confirmed that the presence of surfactants from applications of other formulated herbicides can exacerbate soybean response to dicamba acid that was deposited on the leaf surface. </p> <p>In the midwestern United States, off-target dicamba movement to DS soybean has been problematic as DS soybean are extremely sensitive to very low concentrations of dicamba. Field and greenhouses studies confirmed that there are phenotypic differences amongst different soybean genotypes and their response to dicamba. Estimations of visual soybean injury was approximately 10% less for genotypes that were less sensitive to dicamba compared to genotypes with increased sensitivity. The future identification of the mechanisms that lead to decreased sensitivity to dicamba could be used to identify soybean cultivars that could mitigate the impacts of dicamba off-target movement to DS soybean. </p> <p>Lastly, a field experiment was conducted that investigated the influence of simulated dew on dicamba volatility from dicamba treated soybean leaves, in addition to soybean response in the presence of dicamba vapor. The results from a field experiment determined that consecutive simulated dew applications increase dicamba volatility from dicamba treated soybean. Furthermore, this is the only research demonstrating that DS-soybean response increases from dicamba vapor in the presence of dew. The results from this dissertation provide further insight into the chemical and environmental factors that influence dicamba volatility, the route of entry of dicamba vapor into plants, and soybean response to dicamba.</p>

This thesis studies three important topics in modeling financial volatility. First, the jump clustering in ex post variance and its implications on forecasting, second, the underlying distribution of stochastic volatility and third, the role of non-Gaussian multivariate return distribution combined with a realized GARCH framework. The first chapter is on variance jumps. Financial markets present unexpected and large jumps, due to unobserved news flow. I focus on modeling the ex post variance jumps, their time- dependent arrivals and their sizes. I use a discrete-time bivariate model, with two autoregressive components which capture the long and short-run memory of the ex post variance measures. I estimate contemporaneous and time-dependent jumps in the log-measures of realized variance and bipower variation. The results from S&P500 show that the variance jumps are frequent and persistent. I examine the ability of jumps to forecast returns and ex post variance densities over horizons of up to 50 days out-of-sample. Modeling jumps significantly improves ex post variance density forecasts for all horizons and improves forecasts of the returns density. In the second chapter I explore the empirical non-Gaussian features of stochastic volatility. The standard assumption in a stochastic volatility specification is typically a restrictive Gaussian AR(1) structure. I drop this assumption and instead I assume that latent log-volatility follows an infinite mixture of normals with a Dirichlet process prior. The ex post measure of realized variance is used as a source of information to help identify the unknown distribution of log- volatility. Results from major stock indices show strong evidence of non-Gaussian distributional behaviour of volatility. The proposed framework captures asymmetry and thick tails in returns as well as realized variance. In out-of-sample forecasting, the new model provides improved density forecasts for returns, negative returns and log-realized variance. In the third chapter a new approach for multivariate realized GARCH models is proposed. Two new extensions that have non-Gaussian innovations are developed. The first one is a parametric version, with multivariate-t innovations. The second one is a nonparametric approximation of the return distribution using an infinite mixture of multivariate normals given a Dirichlet process prior. The proposed models are based on the assumption that the realized covariance follows an Inverse Wishart distribution with conditional mean set to the conditional covariance of returns. The benefits of the proposed models are demonstrated from density forecasting and portfolio applications. Results from two equity datasets indicate that modeling the tail behaviour improves return density forecasting compared to the Gaussian assumption. The proposed models produce the least volatile global minimum variance portfolios out-of-sample and provide improved forecasts of Value-at-Risk and Expected Shortfall. / Thesis / Doctor of Business Administration (DBA)

Increased volatility of agricultural commodity prices as well as market linkages between the agricultural and energy markets expose producers to different types of systematic price risk. Producers that operate on margins involving both input and output price uncertainty are perhaps the most adversely affected by these volatility changes. The beef cattle feeding industry is one such example. This research focuses on how expected margins in the beef cattle backgrounding and finishing stages are affected by output and input price uncertainty.

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Under current regulations, gasoline engines are facing lubricity and wear challenges that need to be met by enhanced gasoline lubricity. Gasoline lubricity can be enhanced by lubricity improvers such as heavy fatty acid methyl esters. This thesis presents the ‘High Frequency Reciprocating Rig’ (HFRR) tests carried out on a standardized tribological test rig as per a modified version of ASTM D6079, to account for the effects of volatility of gasoline. Testing 5 gasoline types (gasolines A-E) blended with 2 lubricity improver types (LI1-2) at 2 concentrations, 250 and 500 ppm, provided insights on the changes in lubrication behavior with different gasoline composition, LI type, and concentration. The gasoline types with higher aromatic content and average carbon number (lower volatility) resulted in less wear and better lubricity regardless of LI concentration. The highly aromatic gasoline “A” performed better with the fatty acid-based LI1. Gasolines “B-E”, which are less aromatic, resulted in less wear with the ester-based LI2. The decrease in wear volumes with LI2 was more pronounced with the highly volatile gasolines B and E. These insights were mainly challenged by the failure of some tests due to the high volatility of gasoline. To mitigate this effect and confirm the findings, less volatile gasoline surrogates were designed to mimic the composition of the gasoline types on functional group basis, and were blended with the same lubricity improvers, and then tested using the same method. This improved the results and showed that high aromaticity enhanced the lubricity of the gasoline blends, especially with fatty-acid based LI1, but degraded it beyond 50% aromatic content. The enhancement of lubricity with higher average carbon number was also highlighted. To create deeper understanding of the lubrication mechanisms involved, it is recommended to study the rheological properties of the blends, analyze the chemical composition of the deposits on the wear tracks, and repeat the tests with continuous supply of lubricant to further decrease the effect of gasoline volatility

The focus of this dissertation is modeling volatility in option pricing by the Black-Scholes formula. A major drawback of the formula is that the returns from assets are assumed to have constant volatility over time. The empirical evidence is overwhelmingly against it. In this dissertation, we allow random volatility for estimating call option prices by Black-Scholes formula and by Monte Carlo simulation. The Black-Scholes formula follows from an assumption that assets evolve according to a Geometric Brownian Motion with constant volatility. This dissertation allows time-varying random volatility in the Geometric Brownian Motion to outline a proof of the formula, thus addressing this drawback. To estimate option prices with the Black-Scholes, the dissertation considers its expectation with respect to two potential probability models of random volatility. Unfortunately, a closed form expression of the expectation of the formula for computing the option prices is intractable. Then the dissertation settles with using an approximation which to its credit incorporates in it the kurtosis of the probability model of random volatility. To our knowledge, option pricing methods in literature do not incorporate kurtosis information. The option pricing with random volatility is pursued for two stochastic volatility models. One model is a member of generalized auto regressive conditional heteroscedasticity (GARCH). The second is a member of Stochastic Volatility models. For each model, estimation of their parameters is outlined. Two real financial series data are then used to illustrate estimation of the option prices, and compared them with those from the Black-Scholes formula with constant volatility. Motivated by a Monte Carlo procedure in the literature for option pricing when the volatility follows a GARCH model, this dissertation lays a foundation for future research to simulate option prices when the random volatility is assumed to follow a Stochastic Volatility model instead of GARCH. / Statistics

Sustainability has increasingly gained attention and importance for both companies andinvestors. This attention and importance will continue to grow. One way that companiesare measured for their sustainability is by the three pillars ethical, social and governance(ESG-scores) given by private rating agencies. A higher score means that the companiesare more sustainable, therefore, these measures are a good and simple way for privateinvestors to make responsible investment-decisions. Many investors are not onlysustainable, but most are also risk averse. Previous studies find that, ESG-scores seem tohave a negative relation to risk. According to theories such as Efficient Market Hypothesis(EMH) suggesting that these ESG-scores then should have the same relationship betweenall industries. On the other hand, the theory Herd Behavior could explain why there wouldbe a difference. Therefore, this study aims to investigate whether there exist anydifferences on this relationship over industries when looking at companies withheadquarters in Sweden. This study includes 58 companies and contains data over a five-year period that provides290 firm observations during the period of 2015-01-01 to 2019-12-31. The data wascollected mainly from Eikon Refinitiv, and some stock-prices that is not available throughEikon was obtained via Nasdaq. The risk measures used is beta (BETA) and volatility(VOL). A multiple regression analysis was applied to test the relationship between theESG-scores and risk and a comparison between the different industries in Sweden. The results display no significant result when investigating the relationship between thetwo risk measures BETA and VOL looking at the inclusion of all industries. When insteadtesting industries separately, the risk measure VOL did show significant results for two ofthe industries; “financials” and “industrials”. The results were a significant positiverelationship between VOL and ESG-score, which is the reverse compared to previousstudies. This result indicates that there is a relationship between risk measured by VOLand ESG-scores, but it does not add up to previous studies findings. On the one hand, noother significant results were obtained and therefore further conclusions with empiricalevidence could not be drawn. On the other hand, results indicate a difference betweenindustries in Sweden. Further research is suggested in order to investigate the possibledifferences in Sweden and the Nordic region. This study contributed new information andindications regarding the area of risk and ESG-scores for listed companies withheadquarters in Sweden. For the private investor this study has provided knowledge aboutthe risk of previous studies results not being appliable in Sweden, as well as the possibleindustry differences when looking at ESG-scores association with volatility and beta.Furthermore, the theory EMH was shown to most likely not hold, whereas herdingbehaviour instead could be the possible explanation.

This dissertation studies the relationship between output growth rate and its volatility. This study sheds light on International, Regional, and Development Economics literature. In the first chapter, we revisit the relationship between output growth rate and its volatility using cross-section techniques for our panel data set from 60 countries from 1970 to 2019. In addition to the conventional volatility measurement of the standard deviation, we incorporate the higher moments, such as skewness and kurtosis, as volatility measures. Higher moments further sharpen our understanding of the volatility and growth rate relationship. We also examine the role of the irreversibility of investment, a purported proximate factor for increased volatility in theory but not applied to empirical models, on the growth rate. We find that a higher level of the irreversibility of investment tends to reduce the growth rate. In the second chapter, we examine the growth-volatility relationship covering manufacturing activities at the two-digit level in 32 countries. In particular, we conduct a comprehensive analysis to reveal the long-term relationship between output growth rate and volatility over 1970 – 2019 within countries and across sectors. We have data for each manufacturing subsector for each country over a long period. We have redefined the growth rate and volatility measures with alternative definitions such as cross-country and cross-sector across time. This offers additional advantages from an econometric perspective, as the large cross-sectional dimension is beneficial when estimating the determinants of growth rate. Moreover, our study assesses the evolution of the long-term relationship between economic sectoral growth rate and sectoral volatility over time. Overall, we find that growth rate and volatility are negatively related, with a few exceptions. The third chapter investigates the relationship between regional growth rate and volatility in U.S. state regions. We use disaggregated data for manufacturing activities over the period 1977 – 2021. We find a significant positive relationship between sectoral volatility and GDP per worker growth rate across the U.S. states regions, meaning that manufacturing volatile sectors for the U.S. are growing faster. This finding is also robust in including additional control variables in the analysis, thus confirming that volatility does not capture the effect of other potential determinants of GDP growth in the manufacturing sectors. We further examine how policy structure and geographical similarity affect regional growth rates, in which we distinguish between the Democrat and Republican Parties and Coastline and Non-Coastline states. We find that the growth rate and volatility relationship has been weaker for Democrat-leading states and geographically more open states (states with a coastline). This suggests that the growth rate and volatility relationship can be altered by having a supporting fiscal policy or having a more open economy.

Master of Science / Department of Agricultural Economics / Glynn Tonsor / This decade has seen movements in commodity futures markets never seen before. There are many factors that have intensified price movements and volatility behavior. Those factors likely altering supply and demand include governmental policy within and outside of the U.S, weather shocks, geopolitical conflicts, food safety concerns etc. Whatever the reasons are for price movements it is clear that the volatility behavior in commodity markets constantly change, and risk managers need to use current and efficient tools to mitigate price risk. This study identified market structural breaks of realized volatility in corn, wheat, soybeans, live cattle, feeder cattle and lean hogs futures markets. Furthermore, this study analyzes the forecasting performance of implied volatility, historical volatility, a composite approach and a naïve approach as forecasters of realized volatility. The forecasting performance of these methods was analyzed in the full period of time of our weekly data from January 1995 to April 2014 and in each identified market regime for each commodity. Previous research has analyzed forecasting performance of implied volatility, a time series alternative and a composite method. However, to the best of my knowledge, they have not worried about market structural breaks in the data that might influence the performance of the mentioned forecasting methods in different periods of time. Overall, results indicate that indeed there are multiple market structural breaks present in the volatility datasets across all six commodities. We found differences in the forecasting performance of the analyzed methods when individual market regimes were analyzed. There seems to be evidence that corroborates the idea in the literature about the superiority of implied volatility over a historical volatility, a composite approach and a naïve approach. Additionally, implied volatility encompassed all the information contained in the historical volatility and the naïve measure across each identified market regime in all six commodities. Our results show that when both implied volatility and historical volatility are available, the benefit of combining those measures into a composite forecasting approach is very limited. Our results hold true for a short term 1 week ahead realized volatility forecast. It would be of interest to see how results vary for longer forecasting time horizons.

This thesis will investigate if adding an exogenous variable (implied volatility) to the variance equation will increase the performance for the GARCH(1,1) and EGARCH(1,1) models based on the OMXS30 index. These models are also compared with the implied volatility itself as a forecasting/modeling method. To evaluate the models the realized variance will be used as an unbiased estimator of the conditional variance. The findings suggest that adding implied volatility to the variance equation increase the overall performance.