Stochastic variability of output levels in a pulp mill

Quite often, in the construction of a pulp mill involves establishing the size of tanks which will accommodate the material from the various processes in which case estimating the right tank size a priori would be vital. Hence, simulation of the whole production process would be worthwhile. Therefore, there is need to develop mathematical models that would mimic the behavior of the output from the various production units of the pulp mill to work as simulators. Markov chain models, Autoregressive moving average (ARMA) model, Mean reversion models with ensemble interaction together with Markov regime switching models are proposed for that purpose.

Effects of a Financial Transaction Tax - Do Transaction Costs Lower Volatility?: A Literature Review

In this literature review the theorethical framework of Financial transaction taxes and their assumed effect on market volatility is assessed. The empirical evidence from various studies is compared against the theory and a simple empirical review of the Finnish stock market is conducted. The findings implicate that financial transaction taxes can not reduce volatility and their actual effect on markets is dependend by many other factors as well. Some evidence even suggests that transactions taxes may actually raise volatility.

Exchange rate volatility and the use of foreign currency derivatives: Impact of 2008 global financial crisis

This thesis examines the impact of foreign exchange rate volatility to the extent of use of foreign currency derivatives. Especially the focus is on the impacts of 2008 global financial crisis. The crisis increased risk level in the capital markets greatly. The change in the currency derivatives use is analyzed by comparing means between different periods and in addition, by linear regression that enables to analyze the explanatory power of the model. The research data consists of financial statements figures from fiscal years 2006-2011 published by firms operating in traditional Finnish industrial sectors. Volatilities of the chosen three currency pairs is calculated from the daily fixing rates of ECB. Based on the volatility the sample period is divided into three sub-periods. The results suggest that increased FX market volatility did not increase the use foreign currency derivatives. Furthermore, the increased foreign exchange rate volatility did not increase the power of linear regression model to estimate the use foreign currency derivatives compared to previous studies.

Idiosyncratic volatility around information release date

Several papers document idiosyncratic volatility is time-varying and many attempts have been made to reveal whether idiosyncratic risk is priced. This research studies behavior of idiosyncratic volatility around information release dates and also its relation with return after public announcement. The results indicate that when a company discloses specific information to the market, firm’s specific volatility level shifts and short-horizon event-induced volatility vary significantly however, the category to which the announcement belongs is not important in magnitude of change. This event-induced volatility is not small in size and should not be downplayed in event studies. Moreover, this study shows stocks with higher contemporaneous realized idiosyncratic volatility earn lower return after public announcement consistent with “divergence of opinion hypothesis”. While no significant relation is found between EGARCH estimated idiosyncratic volatility and return and also between one-month lagged idiosyncratic volatility and return presumably due to significant jump around public announcement both may provide some signals regarding future idiosyncratic volatility through their correlations with contemporaneous realized idiosyncratic volatility. Finally, the study show that positive relation between return and idiosyncratic volatility based on under-diversification is inadequate to explain all different scenarios and this negative relation after public announcement may provide a useful trading rule.

Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Market integration, return and volatility dynamics; empirical evidence from African stock markets

Frontier and Emerging economies have implemented policies with the objective of liberalizing their equity markets. Equity market liberalization opens the domestic equity market to foreign investors and as well paves the way for domestic investors to invest in foreign equity securities. Among other things, equity market liberalization results in diversification benefits. Moreover, equity market liberalization leads to low cost of equity capital resulting from the lower rate of return by investors. Additionally, foreign and local investors share any potential risks. Liberalized equity markets also become liquid considering that there are more investors to trade. Equity market liberalization results in financial integration which explains the movement of two markets. In crisis period, increased volatility and co-movement between two markets may result in what is termed contagion effects. In Africa, major moves toward financial liberalization generally started in the late 1980s with South Africa as the pioneer. Over the years, researchers have studied the impact of financial liberalization on Africa’s economic development with diverse results; some being positive, others negative and still others being mixed. The objective of this study is to establish whether African stock-markets are integrated into the United States (US) and World market. Furthermore, the study helps to see if there are international linkages between the Africa, US and the world markets. A Bivariate- VAR- GARCH- BEKK model is employed in the study. In the study, the effect of thin trading is removed through series of econometric data purification. This is because thin trading, also known as non-trading or inconsistency of trading, is a main feature of African markets and may trigger inconsistency and biased results. The study confirmed the widely established results that the South Africa and Egypt stock markets are highly integrated with the US and World market. Interestingly, the study adds to knowledge in this research area by establishing the fact that Kenya is very integrated with the US and World markets and that it receives and exports past innovations as well as shocks to and from the US and World market.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

Volatility of Different Formulations of Clomazone Herbicide

Volatilization represents an important process in the displacement of pesticides for the environment. The physicochemical properties of the clomazone molecule indicate its relative volatility. Therefore, this study was carried out to assess the volatilization of different clomazone herbicide formulations using bioindicator species. To that end, airtight glass boxes were used with the presence of different clomazone formulations and plant species. The formulations used were Gamit 360 CS(r), Gamit 500 EC(r) and Gamit Star(r). The plant species assessed were maize, sorghum and rice. With the results obtained it is possible to conclude that, among the formulations, Gamit 360 CS(r) has caused less phytotoxicity to the bioindicator species in comparison to the formulations of Gamit 500 EC(r) and Gamit Star(r) formulations. In general, The Gamit 500 EC(r) and Gamit Star(r) have not differed in the phytotoxicity potential for the bioindicator species.

Regime-Switching in the Impact of Oil Price Shocks on Stock Market Volatility: Evidence from Oil-Importing and Oil-Exporting Countries

Research has highlighted the adequacy of Markov regime-switching model to address dynamic behavior in long term stock market movements. Employing a purposed Extended regime-switching GARCH(1,1) model, this thesis further investigates the regime dependent nonlinear relationship between changes in oil price and stock market volatility in Saudi Arabia, Norway and Singapore for the period of 2001-2014. Market selection is prioritized to national dependency on oil export or import, which also rationalizes the fitness of implied bivariate volatility model. Among two regimes identified by the mean model, high stock market return-low volatility regime reflects the stable economic growth periods. The other regime characterized by low stock market return-high volatility coincides with episodes of recession and downturn. Moreover, results of volatility model provide the evidence that shocks in stock markets are less persistent during the high volatility regime. While accelerated oil price rises the stock market volatility during recessions, it reduces the stock market risk during normal growth periods in Singapore. In contrast, oil price showed no significant notable impact on stock market volatility of target oil-exporting countries in either of the volatility regime. In light to these results, international investors and policy makers could benefit the risk management in relation to oil price fluctuation.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.