In this thesis, we aim to improve forecast accuracy of a heterogenous au- toregressive model (HAR) by including market sentiment indicators based on Google search volume and Twitter sentiment. We have analysed 30 com- panies of the Dow Jones index for a period of 15 months. We have performed out-of-sample forecast and compiled a ranking of the extended models based on their relative performance. We have identified three relevant variables: daily negative tweets, daily Google search volume and weekly Google search volume. These variables improve forecast accuracy of the HAR model se- parately or in a Twitter-Google combination. Some specifications improve forecast accuracy by up to 22% for particular stocks, others impair forecast accuracy by up to 24%. The combination of daily negative tweets and weekly search volume is a superior model to the basic HAR for 17 stocks according to RMSE and for 16 stocks according to MAE and MASE. The daily nega- tive tweets specification outperforms the basic HAR for 17 and 19 stocks, respectively. And, the combination of daily negative tweets and daily search volume outpaces the basic HAR for 15 and 18 stocks, respectively. Based on the average MASE improvement, the combination of daily negative tweets and weekly search volume is a clear winner as it lowers the...

Over the past couple of decades, the number of volatility indices has increased rapidly. These indices seek to represent the market’s expectation of realized volatility over the coming month, based on the prices of options traded on each underlying equity index. Although the dynamics of realized volatility spillover have been studied extensively, very few studies exists that examine the spillover between these volatility indices. By using DAG-based structural vector autoregression, this paper provides evidence that implied volatility spillover differs from realized volatility spillover. Through solving the well-known VAR identification problem for these indices, this paper finds that Asia, more specifically Hong Kong, plays a central role in implied volatility spillover during and after the 2008 financial crisis.

Dicamba is often tank mixed with glyphosate to increase herbicidal efficacy but may contribute to off-target movement (OTM). In recent years, volatilization has become problematic for dicamba-containing herbicides, resulting in increased regulatory requirements necessitating the use of volatility-reducing agents (VRA) for application. Research was conducted in 2021 and 2022 using low tunnels in a field environment and humidomes in a greenhouse environment to further assess how glyphosate salts and VRAs affect dicamba volatility. Our data indicate that the inclusion of glyphosate to dicamba can increase dicamba volatility, depending on the glyphosate salt used. The inclusion of the evaluated VRAs will decrease dicamba volatility when applied to a tank mixture of dicamba plus potassium salt of glyphosate.

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Volatility clustering is a stylized fact common in nance. Large changes in prices tend to cluster whereas small changes behave likewise. The higher the volatility of a market, the more risky it is said to be and vice versa . Below, we study volatility clustering using an agent-based model. This model looks at the reaction of agents as a result of the variation of asset prices. This is due to the irregular switching of agents between fundamentalist and chartist behaviors generating a time varying volatility. Switching depends on the performances of the various strategies. The expectations of the excess returns of the agents (fundamentalists and chartists) are heterogenous.

No description available.

No description available.

Abstract Microsoft’s Windows Operating System provides a logging service that collects, filters and stores event messages from the kernel and applications into log files (.evt and .evtx). Volatility, the leading open source advanced memory forensic suite, currently allows users to extract these events from memory dumps of Windows XP and Windows 2003 machines. Currently there is no support for users to extract the event logs (.evtx) from Windows Vista, Win7 or Win8 memory dumps, and Volatility users have to rely on outside software in order to do this. This thesis discusses a newly developed evtxlogs.py plugin for Volatility, which allows users the same functionality with Windows Vista, Win7 and Win8 that they had with Windows XP and Win 2003’s evtlogs.py plugin. The plugin is based on existing mechanisms for parsing Windows Vista-format event logs, but adds fully integrated support for these logs to Volatility.

Organic particulate matter represents a significant fraction of sub-micrometer atmospheric aerosol mass. However, organic aerosol (OA) consists of thousands of different organic compounds making the simulation of its concentration, chemical evolution, physical and chemical properties extremely challenging. The identity of the great majority of these compounds remains unknown. The volatility of atmospheric OA is one of its most important physical properties since it determines the partitioning of these organic compounds between the gas and particulate phases. The use of lumped compounds with averaged properties is a promising solution for the representation of OA in atmospheric chemical transport models. The two-dimensional volatility basis set (2D-VBS) is a proposed method used to describe OA distribution as a function of the volatility and oxygen content of the corresponding compounds. In the first part of the work we evaluate our ability to measure the OA volatility distribution using a thermodenuder (TD). We use a new method combining forward modeling, introduction of ‘experimental’ error and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model and show that it is suitable for studies of OA volatility in the lab and the field. Measurements combining a thermodenuder (TD) and a High Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) took place during summer and winter in Paris, France as part of the collaborative project MEGAPOLI and during the winter of 2013 in the city of Athens. The above volatility estimation method with the uncertainty estimation algorithm is applied to these datasets in order to estimate the volatility distribution for the organic aerosol (OA) and its components during the two campaigns. The concentrations of the OA components as a function of temperature were measured combining data from the thermodenuder and the aerosol mass spectrometer (AMS) with Positive Matrix Factorization (PMF) analysis. Combining the bulk average O:C ratios and volatility distributions of the various factors, our results are placed into the two-dimensional volatility basis set (2D-VBS) framework. The OA factors cover a broad spectrum of volatilities with no direct link between the average volatility and average O:C of the OA components. An intercomparison among the OA components of both campaigns and their physical properties is also presented. The approach combining thermodenuder and isothermal dilution measurements is tested in smog chamber experiments using OA produced during meat charbroiling. The OA mass fraction remaining is measured as a function of temperature in the TD and as a function of time in the isothermal dilution chamber. These two sets of measurements are used together to estimate the volatility distribution of the OA and its effective vaporization enthalpy and accommodation coefficient. In the isothermal dilution experiments approximately 20% of the OA evaporate within 15 min. In the TD almost all the OA evaporated at approximately 200oC. The resulting volatility distributions suggest that around 60-75% of the cooking OA (COA) at concentrations around 500 μg m-3 consists of low volatility organic compounds (LVOCs), 20-30% of semi-volatile organic compounds (SVOCs) and around 10% of intermediate volatility organic compounds (IVOCs). The estimated effective vaporization enthalpy of COA is 100 ± 20 kJ mol-1 and the effective accommodation coefficient is around 0.05. The characteristics of the COA factor from the Athens campaign are compared to those of the OA produced from meat charbroiling in these experiments. In the next step, different parameterizations of the organic aerosol (OA) formation and evolution in the two-dimensional Volatility Basis Set (2D-VBS) framework are evaluated using ground and airborne measurements collected in the 2012 Pan-European Gas AeroSOls-climate-interaction Study (PEGASOS) field campaign in the Po Valley, Italy. A number of chemical schemes are examined, taking into account various functionalization and fragmentation pathways for biogenic and anthropogenic OA components. Model predictions and measurements, both at the ground and aloft, indicate a relatively oxidized OA with little average diurnal variation. Total OA concentration and O:C ratios were reproduced within experimental error by a number of chemical aging schemes. Anthropogenic SOA is predicted to contribute 15-25% of the total OA, while SOA from intermediate volatility compounds oxidation another 20-35%. Biogenic SOA contributions varied from 15 to 45% depending on the modeling scheme. The average OA and O:C diurnal variation and their vertical profiles showed a surprisingly modest sensitivity to the assumed vaporization enthalpy for all aging schemes. This can be explained by the intricate interplay between the changes in partitioning of the semivolatile compounds and their gas-phase chemical aging reactions. The same set of different parameterizations of the organic aerosol (OA) formation and evolution in the two-dimensional Volatility Basis Set (2D-VBS) framework are evaluated using ground measurements collected in the 2013 PEGASOS field campaign in the boreal forest station of Hyytiälä in Southern Finland. The most successful is the simple functionalization scheme of Murphy et al. (2012) while all seven aging schemes have satisfactory results, consistent with the ground measurements. Despite their differences, these schemes predict similar contributions of the various OA sources and formation pathways. Anthropogenic SOA is predicted to contribute 11- 18% of the total OA, while SOA from intermediate volatility compounds oxidation another 18- 27%. The highest contribution comes from biogenic SOA, as expected contributing 40 to 63% depending on the modeling scheme. The primary OA contributes 4% while the SOA resulting from the oxidation of the evaporated POA varies between 4 to 6%. Finally, 5-6% is according to the model the results of long range transport from outside the modeling domain.

I examine the relation between a firm's dividend policy and its strategic tax decisions. I posit that the capital market pressure associated with paying a dividend leads dividend-paying firms to seek predictable cash flows. I specifically focus on the volatility of a firm's cash effective tax rate (ETR) due to the observability, large size, variability, and periodicity of cash tax payments. Consistent with dividend payments altering a firm's strategic tax preferences, I find that firms that pay a higher dividend exhibit more predictable cash ETRs. Further, I find that the predictability of a dividend-initiating (eliminating) firm's cash ETR subsequently increases (decreases). Additionally, I find that, consistent with prior research suggesting that financially constrained firms "borrow" cash from their tax account, financial constraint moderates the positive relation between the predictability of a firm's cash ETR and its dividend payments. Importantly, my results hold for firms initiating a dividend in response to the exogenous shock of the Bush tax cuts. Finally, I also examine specific tax strategies dividend-paying firms use to help increase the predictability of their cash tax payments. My results contribute to the academic literature by examining whether, and how, dividend-paying firms alter their strategic tax decisions. Additionally, I contribute to ongoing public policy debates over the value of dividend payments by demonstrating a positive relation between dividend payments and the predictability of a firm's cash tax payments.