The 1996 and 2002 Farm Bill changes in milk support price legislation deregulated the market and milk prices are more volatile than ever. The use of a mechanism to reduce farmers' exposure to volatile milk prices has therefore become essential. This study evaluates the impact of two hedging strategies, one conservative and the other an intermediate one (more sophisticated). Optimal parameters for the two strategies are searched over a period of 5 years. Then, the performance, in terms of increased profitability and reduced variation, is assessed and the best performer is chosen and applied to an out-of-sample dataset. With the in-sample data, both strategies generate higher mean monthly profits than with the no-hedging option. Comparison of both strategies indicates that the intermediate strategy outperforms the conservative one in terms of higher profitability and lower variance. Out-of-sample results confirm the findings of the in-sample results. The additional profits and the reduction in volatility can make the difference between keeping a farm profitable and bankruptcy. / Master of Science

No / Motivated by an extensive literature showing that government bond yields exhibit a strong non-Markov property, in the sense that moving averages of long-lagged yields significantly improve the predictability of excess bond returns. We then develop a systematic approach of constructing non-Markov Gaussian dynamic term structure models (GDTSMs) under the Heath-Jarrow-Morton (HJM) framework. Compared to the current literature, our approach is more flexible and parsimonious, enabling us to estimate an economically significant non-Markov effect that helps predict excess bond returns both in-sample and out-of-sample.

This thesis deals with trading using technical analysis. Mostly attention is paid shares traded on the Prague Stock Exchange. The practical part describes computer program, which gives recommendations for the purchase and sale of shares based on moving averages and methods of moving average convergence / divergence and relative strength index. The conclusion is stated financial comparison of the methods.

Classical time series analysis has well known methods for the study of seasonality. A more recent method of functional data analysis has proposed phase-plane plots for the representation of each year of a time series. However, the study of seasonality within functional data analysis has not been explored extensively. Time series analysis is first introduced, followed by phase-plane plot analysis, and then compared by looking at the insight that both methods offer particularly with respect to the seasonal behavior of a variable. Also, the possible combination of both approaches is explored, specifically with the analysis of the phase-plane plots. The methods are applied to data observations measuring water flow in cubic feet per second collected monthly in Newport, TN from the French Broad River. Simulated data corresponding to typical time series cases are then used for comparison and further exploration.

This thesis further investigates the effectiveness of 15 variable moving average strategies that mimic the trading rules used in the study by Brock, Lakonishok, and LeBaron (1992). Instead of applying these strategies to developed markets, unique characteristics of emerging markets offer opportunity to investors that warrant further research. Before transaction costs, all 15 variable moving average strategies outperform the naïve benchmark strategy of buying and holding different emerging market ETF's over the volatile period of 858 trading days. However, the variable moving averages perform poorly in the "bubble" market cycle. In fact, sell signals become more unprofitable than buy signals are profitable. Furthermore, variations of 4 of 5 variable moving average strategies demonstrate significant prospects of returning consistent abnormal returns after adjusting for transaction costs and risk.

In this work was evaluated the effectiveness of artificial neural networks in trading on the stock markets. The subject of the work was the process of optimizing parameters of artificial neural networks, the resulting predictive efficiency was determined on the basis of the application being optimized parameters of neural networks.

This diploma thesis is focused on using statistical methods of technical analysis of stocks. The teoretical part describes basic principals of regression and technical analysis with a description of the technical indicators that are used to predict the future development of share prices and finding appropriate moment to buy or sell stocks. The results of the analysis is the comparison of indicators and their applicability to trade the stock titles.

This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-Winters models were derived, and the resulting model of this study was used to forecast PIT, CIT, VAT and TTAXR for SARS fiscal years 2010/11, 2011/12 and 2012/13. The results show that both the SARIMA and Holt-Winters models perform well in modeling and forecasting PIT and VAT, however the Holt-Winters model outperformed the SARIMA model in modeling and forecasting the more volatile CIT and TTAXR. It is recommended that these methods are used in forecasting future payments, as they are precise about forecasting tax revenues, with minimal errors and fewer model revisions being necessary. / Statistics / M.Sc. (Statistics)

This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-Winters models were derived, and the resulting model of this study was used to forecast PIT, CIT, VAT and TTAXR for SARS fiscal years 2010/11, 2011/12 and 2012/13. The results show that both the SARIMA and Holt-Winters models perform well in modeling and forecasting PIT and VAT, however the Holt-Winters model outperformed the SARIMA model in modeling and forecasting the more volatile CIT and TTAXR. It is recommended that these methods are used in forecasting future payments, as they are precise about forecasting tax revenues, with minimal errors and fewer model revisions being necessary. / Statistics / M.Sc. (Statistics)

The current persistent drought and changes in the climate system have raised water managers expert questions about how to manage water resources in the future. The manifestation of climate change in hydrological series and their influence on the magnitude of threats to the storage functions of reservoirs are more often investigated. Already today, long-term shortages of storage capacity in reservoirs lead to the introduction of special manipulations on water structures. The aim of the thesis is to perform the analysis of time series, respectively decomposition of hydrological series average annual and monthly discharges. Create extended hydrological bases using synthetic discharge series generators and develop a comprehensive analysis of storage volume without considering losses even with the introduction of losses from the water surface vapor in the UNCE RESERVOIR program. The created discharge series are compared and evaluated on the basis of statistical characteristics and reservoir storages results with the real discharge series. The practical app is conducted on the Vranov reservoir in the Dyje River Basin.