Option market data are quoted in terms of option prices and are fragmented into over 100 individual contract files per day for each symbol. Traders and quantitative analysts compare values of options in terms of implied volatilities. The current project refactors fragmented option price data into implied volatility files organized by stock symbols and expiration dates. Each resulting file comprises the temporal evolution of daily volatility smile curves for every day prior to expiration. Possible analysis enabled by the refactored data is demonstrated.

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Dicamba resistant (DR) cropping technology has increased dicamba use, resulting in observation of dicamba off-target-movement (OTM). Volatility is one form of this movement. Tank mixtures and environmental conditions impact the volatile behavior of dicamba following application. Research was conducted in 2018, 2019, and 2020 to further assess and understand volatility mitigation by understanding tank-mix effects and utility of irrigation on volatility mitigation. Low tunnel and humidome methodology were used to analyze impact of tank mixtures and irrigation on dicamba volatility. Data suggest tank mixing encapsulated chloroacetamide formulations can mitigate volatility when comparing identical active ingredients formulated as emulsifiable concentrates. Tank-mixed glyphosate increases dicamba volatility regardless of salt form, with dimethylamine salt of glyphosate having the most volatile effect. Manipulation of environmental conditions can also assist in mitigation efforts when applicable through use of irrigation. Increasing amount of irrigation applied following dicamba application has a positive effect on mitigation.

This thesis studies realized volatility (RV), implied volatility (IV) and their applications in stochastic volatility models. The first essay uses both daytime and overnight high-frequency price data for equity index futures to estimate the RV of the S\&P500 and NASDAQ 100 indexes. Empirical results reveal strong inter-correlation between the regular-trading-time and after-hour RVs, as well as a significant predictive power of overnight RV on daytime RV and vice versa. We propose a new day-night realized stochastic volatility (DN-SV-RV) model, where the daytime and overnight returns are jointly modeled with their RVs, and their latent volatilities are correlated. The newly proposed DN-SV-RV model has the best out-of-sample return distribution forecasts among the models considered. The second essay extends the realized stochastic volatility model by jointly estimating return, RV and IV. We examine how RV and IV enhance the estimation of the latent volatility process for both the S\&P500 index and individual stocks. The third essay re-examines asymmetric stochastic volatility (ASV) models with different return-volatility correlation structures given RV and IV. We show by simulation that estimating the ASV models with return series alone may infer erroneous estimations of the correlation coefficients. The incorporation of volatility measures helps identify the true return-volatility correlation within the ASV framework. Empirical evidence on global equity market indices verifies that ASV models with additional volatility measures not only obtain significantly different estimations of the correlations compared to the benchmark ASV models, but also improve out-of-sample return forecasts. / Thesis / Doctor of Philosophy (PhD)

This study tests if historical volatility- and implied volatility has significant predictive power over future realized volatility and if so which one of the two is the superior predictor. The study is conducted by using historical volatility of the OMXS30 and implied volatility from OMXS30 call options during the period 2012-2023. Three regressions have been made to test the research questions, two simple linear regression and one multiple linear regression. The results of the study showed that both historical- and implied volatility had significant predictive power over future realized volatility with implied being the superior one with a higher correlation coefficient. The multiple regression showed that both the independent variables were important and both of them explained different parts of the data, which means that they have complementary abilities and that both should be used when assessing the forecast of realized volatility.

<p>Volatility forecast plays a central role in the financial decision making process. An intrinsic purpose of any investor is profit earning. For that purpose investors need to estimate the risk. One of the most efficient</p><p>methods to this end is the volatility estimation. In this theses I compare the CBOE Volatility Index, (VIX) with the actual volatility on an index at the Nordic Market. The actual volatility is defined as the one-day-ahead prediction as calculated by using the GARCH(1,1) model. By using the VIX model I performed consecutive predictions 30 days ahead between February the 2nd, 2007 to March</p><p>the 6th, 2007. These predictions were compared with the GARCH(1,1) one-day-ahead predictions for the same period. To my knowledge, such comparisons have not been performed earlier on the Nordic Market. The conclusion of the study was that the VIX predictions tends to higher values then the GARCH(1,1) predictions except for large prices upward jumps, which indicates that the VIX is not able to predict future shocks.</p><p>Except from these jumps, the VIX more often shows larger value than the GARCH(1,1). This is interpreted as an uncertainly of the prediction. However, the VIX predictions follows the actual volatility reasonable</p><p>well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.</p>

Volatility forecast plays a central role in the financial decision making process. An intrinsic purpose of any investor is profit earning. For that purpose investors need to estimate the risk. One of the most efficient methods to this end is the volatility estimation. In this theses I compare the CBOE Volatility Index, (VIX) with the actual volatility on an index at the Nordic Market. The actual volatility is defined as the one-day-ahead prediction as calculated by using the GARCH(1,1) model. By using the VIX model I performed consecutive predictions 30 days ahead between February the 2nd, 2007 to March the 6th, 2007. These predictions were compared with the GARCH(1,1) one-day-ahead predictions for the same period. To my knowledge, such comparisons have not been performed earlier on the Nordic Market. The conclusion of the study was that the VIX predictions tends to higher values then the GARCH(1,1) predictions except for large prices upward jumps, which indicates that the VIX is not able to predict future shocks. Except from these jumps, the VIX more often shows larger value than the GARCH(1,1). This is interpreted as an uncertainly of the prediction. However, the VIX predictions follows the actual volatility reasonable well. I conclude that the VIX estimation can be used as a reliable estimator of market volatility.

One basic assumption of the celebrated Black-Scholes-Merton PDE model for pricing derivatives is that the volatility is a constant. However, the implied volatility plot based on real data is not constant, but curved exhibiting patterns of volatility skews or smiles. Since the volatility is not observable, various stochastic volatility models have been proposed to overcome the problem of non-constant volatility. Although these methods are fairly successful in modeling volatilities, they still rely on the implied volatility approach for model implementation. To avoid such circular reasoning, we propose a new class of stochastic volatility models based on directly observable volatility proxies and derive the corresponding option pricing formulas. In addition, we propose a new GARCH (1,1) model, and show that this discrete-time stochastic volatility process converges weakly to Heston's continuous-time stochastic volatility model. Some Monte Carlo simulations and real data analysis are also conducted to demonstrate the performance of our methods.

This thesis tests the hypothesis that the qualities of different forecasts of exchange rate volatility depend on the underlying exchange rate regime. By examining the British pound during and after its withdrawal from the European Monetary System (EMS), this analysis compares "backward-looking" historical forecasts of future volatility with the "forward-looking" forecast of volatility reflected in current option prices. Because option implied volatility contains the market's most current expectations about future prices, theory and much previous evidence suggests this should be the superior predictor of future volatility. In contrast to previous research by findings, this study concludes that option implied volatility is not superior. During the time when the pound was in the EMS, implied volatility provided reasonably good forecasts of future volatility. However, after the pound withdrew from the EMS, various statistical measures of historical volatility are found to have greater informational content and predictive power about future actual volatility than implied volatility. In particular, a time series estimate, specifically a GARCH(1,1) model, had the most informational content and predictive power about realized pound volatility, especially in the period following sterling's withdrawal from the EMS. / Master of Arts

Thesis advisor: Pierluigi Balduzzi / Essay one of this dissertation investigates the relation between fundamental idiosyncratic volatility and stock returns idiosyncratic volatility using data from 56 countries over 1980-2014. I find a strong positive relation between fundamental idiosyncratic volatility and idiosyncratic volatility of returns. This association, however, seems to be entirely driven by the developed economies and I find no effect in the emerging markets. Specifically, fundamental idiosyncratic volatility does not lead to more idiosyncratic return volatility in countries with poor legal institutions and weak shareholder protection laws. The second essay examines the effect of accounting standards on return predictability by using a variance decomposition approach, and is joint work with Pierluigi Balduzzi, Gil Sadka, and Ronnie Sadka. We decompose returns into a cash-flow news component and a discount-rate news component, and investigate cross-sectional and time-series changes in the contribution of each component to return variations. We also decompose returns for 20 industries in three subsample periods to examine the effect of accounting standards on different industries over time. Our results contribute to our understanding of the effect of accounting practices on accounting variables and return predictability. The third essay studies the effect of short-selling on stock price informativeness. Morck, Yeung, and Yu (2000), in their pioneering study of international differences in stock price synchronicity, emphasize the effect of market development on the ability of investors to incorporate firm-specific information into prices. I use a unique institutional feature in the Hong Kong market to investigate one of the important tools investors use to incorporate information into prices and hence, reduce stock price synchronicity; short-selling. Examining the cross-sectional and time-series variation in short-sale constraints in the Hong Kong market, I find that following the removal of short-sale constraints, stock prices become more informative and move less in tandem with the market. My findings contribute to our understanding of the impact of short-sales constraints on stock price informativeness. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Carroll School of Management. / Discipline: Finance.