In the literature, many methods are proposed to value American options. However, due to computational difficulty, there are only approximate solution or numerical method to evaluate American options. It is not easy for general investors either to understand nor to apply. In this thesis, we build up an option pricing and virtual asset model system, which provides a friendly environment for general public to calculate early exercise boundary of an American option. This system modularize the well-handled pricing models to provide the investors an easy way to value American options without learning difficult financial theories. The system consists two parts: the first one is an option pricing system, the other one is an asset model simulation system. The option pricing system provides various option pricing methods to the users; the virtual asset model system generates virtual asset prices for different underlying models.

In this study, both uni-regime GARCH and Markov Regime Switching GARCH (SW-GARCH) models are examined to analyze Turkish Stock Market volatility. We investigate various models to find out whether SW-GARCH models are an improvement on the uni-regime GARCH models in terms of modelling and forecasting Turkish Stock Market volatility. As well as using seven statistical loss functions, we apply Superior Predictive Ability (SPA) test of Hansen (2005) and Reality Check test (RC) of White (2000) to compare forecast performance of various models.

In the first part of this thesis the prices of six non-ferrous metals (aluminum, copper, lead, nickel, tin, and zinc) are used to assess the forecasting performance of GARCH models. We find that the forecasting performances of GARCH, EGARCH, and TGARCH models are similar. However, we suggest the use of the GARCH model because it is more parsimonious and has a slightly better statistical performance than the other two. In the second part, the prices of six non-ferrous metals and the price of crude oil are used to examine the dynamic links between oil and metal returns by using the BEKK specification of the multivariate GARCH model and the Granger causality-in-variance tests. Results of our study agree with the previous studies in that the crude oil market volatility leads all non-ferrous metal markets. In order to move as far away from the effects of 9/11, daily data for the period December 12, 2003 &ndash / December 15, 2008 is used for the data analysis part of the thesis.

Day-ahead spot electricity markets are the most transparent spot markets where one can find integrated supply and demand curves of the market players for each settlement period. Since it is an indicator for the market players and regulators, in this thesis we model the spot electricity prices. Logarithmic daily average spot electricity prices are modeled as a summation of a deterministic function and multi-factor stochastic process. Randomness in the spot prices is assumed to be governed by three jump processes and a Brownian motion where two of the jump processes are mean reverting. While the Brownian motion captures daily regular price movements, the pure jump process models price shocks which have long term effects and two Ornstein Uhlenbeck type jump processes with different mean reversion speeds capturing the price shocks that affect the price level for relatively shorter time periods. After removing the seasonality which is modeled as a deterministic function from price observations, an iterative threshold function is used to filter the jumps. The threshold function is constructed on volatility estimation generated by a GARCH(1,1) model. Not only the jumps but also the mean reverting returns following the jumps are filtered. Both of the filtered jump processes and residual Brownian components are estimated separately. The model is applied to Austrian, Italian, Spanish and Turkish electricity markets data and it is found that the weekly forecasts, which are generated by the estimated parameters, turn out to be able to capture the characteristics of the observations. After examining the future contracts written on electricity, we also suggest a decision technique which is built on risk premium theory. With the help of this methodology derivative market players can decide on taking whether a long or a short position for a given contract. After testing our technique, we conclude that the decision rule is promising but needs more empirical research.

The purpose of this study is to examine the volatility spillover among a country&rsquo / s foreign exchange, bond and stock markets and the volatility transmission from the global bond, stock and commodity markets to these local financial markets. The sample for the study includes data from both emerging and developed economies in the time period between 2004 and 2011. A multivariate GARCH methodology with the BEKK representation is applied for the local financial markets and global variables are included as exogenous variables into the model. The volatility integration of the financial markets of the emerging economies is stronger compared to the integration of the developed economies. Global variables have a spillover effect on the developed markets only after the global financial crisis, whereas they significantly affect the volatility in emerging markets for both the pre- and post-crisis period. North American countries in the sample, U.S. and Mexico, have low local volatility integration in the pre-crisis era and the integration rises in the post-crisis period. Moreover, they are more open to the internal and global short-term shocks in the post-crisis period. Germany and Turkey are the representatives of the EMEA (Europe, Middle East and Africa) region and they have high local market integration and are open to global shocks for both sub-periods. Far Eastern markets, Japan and Korea, also have high local market integration and their vulnerability to the global effects is large and getting larger for the post-crisis period. The most important limitation of this thesis is the difficulty of reaching sharp generalizations due to the small number of countries analyzed. This limitation can be addressed by the inclusion of a larger number of geographically dispersed countries. The most noteworthy originality of this study is the addition of the exogenous global variables for modeling volatility spillovers. Furthermore, comparison of results for emerging versus developed markets and the pre- versus post-crisis periods is another contribution of this study to the existing literature. The findings of this study can be used by investors interested in assessing the risks of investing internationally.

The current study empirically analyzes the extreme behavior and the impact of deregulation policies as well as financial turmoil on the extreme behavior of changes of Taiwan short term interest rate. A better knowledge of short-term interest rate properties, such as heavy tails, asymmetry, and uneven tail fatness between right and left tails, provide an insight to the extreme behavior of short-term interest rate as well as a more accurate estimation of interest risk. The predicting performances of filtered and unfiltered VaR (Value at risk) models are also examined to suggest the proper models for management of interest rate risk. By applying Extreme Value theory (EVT), tail behavior is analyzed and tested and the VaR based on parametric and non-parametric EVT models are calculated.The empirical findings show that, first, the distribution of change of rate are heavy-tailed indicating that the actual risk would be underestimated based on normality assumption. Second, the unconditional distribution is consistent with the heavier-tailed distributions such as ARCH process or Student¡¦t. Third, the right tail of distribution of change of rate are significantly heavier than the left one pointing out that the probabilities and magnitudes of rise in rate could be higher than those of drop in rate. Fourth, the amount of tail-fatness in tail of distribution of change of rate increase after 1999 and the vital factors to cause structural break in tail index are the interest rate policies taken by central bank of Taiwan instead of the deregulation policies in money market. Fifth, based on the two break points found in tail index of right and left tail, long sample of CP rates should not be treated as samples from a single distribution. Sixth, the dependent and heteroscedastic properties of data series should be considered in applying EVT to improve accuracy of VaR forecasts. Finally, EVT models predict VaR accurately before 2001 and the benchmark model, HS and GARCH, generally are superior to EVT models after 2001. Among EVT models, MRE and CHE are relative consistent and reliable in VaR prediction.

Although a pair trading is well known among South African agricultural commodity traders, there are no comprehensive documented accounts for the selection and trading of agricultural commodity pairs in South Africa. The majority of agricultural commodity pairs traders take positions based on their personal view of price movements, without testing for a statistical relationship between the paired commodities that will guarantee that their prices will move back to a common mean. To remedy this lack of method regarding the pairs selection and pairs trading processes, a comprehensive pairs selection process was developed and is documented in this thesis. During the pairs selection process, several agricultural commodities were put through a rigorous evaluation process to test for any long-run statistical relationships between them. This was done to ensure that only pairs with stable long-run statistical relationships were included in the final pair’s portfolio that was compiled. In order to test the profitability of this pair’s portfolio, several fundamental and technical indicators were used to determine entry and exit points. Although some of these indicators did not render satisfactory results, the RSI and Bollinger bands succeeded in realising an acceptable profit. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2008.

Although a pair trading is well known among South African agricultural commodity traders, there are no comprehensive documented accounts for the selection and trading of agricultural commodity pairs in South Africa. The majority of agricultural commodity pairs traders take positions based on their personal view of price movements, without testing for a statistical relationship between the paired commodities that will guarantee that their prices will move back to a common mean. To remedy this lack of method regarding the pairs selection and pairs trading processes, a comprehensive pairs selection process was developed and is documented in this thesis. During the pairs selection process, several agricultural commodities were put through a rigorous evaluation process to test for any long-run statistical relationships between them. This was done to ensure that only pairs with stable long-run statistical relationships were included in the final pair’s portfolio that was compiled. In order to test the profitability of this pair’s portfolio, several fundamental and technical indicators were used to determine entry and exit points. Although some of these indicators did not render satisfactory results, the RSI and Bollinger bands succeeded in realising an acceptable profit. / Thesis (Ph.D. (Risk Management))--North-West University, Potchefstroom Campus, 2008.

Created in 2009, the digital currency of bitcoin is a relatively new phenomenon. During this short period of time, it has however displayed a strong development of both price and trade volume. This has led to increased media attention, but also regulators and researchers have developed an interest. At this moment, the amount of available research is however limited. With a focus on the price volatility of bitcoin and an aim of finding drivers of this volatility, this study is taking a unique position. The research has its basis in the philosophical position of positivism and objectivism. This has shaped the research question as well as the construction of the study. The result is a describing and explaining research with a deductive research approach, a quantitative research method and an archival research strategy. This has in turn stimulated an extensive literature review and information search. Areas of discussion are microstructure theory, the efficient market hypothesis, behavioural finance and informational structures. Due to the limited amount of previous bitcoin research within the area of price volatility, the study has drawn extensively on research performed on more classical assets such as stocks. Nevertheless, when available, bitcoin research has been used as a foundation/reference and an inspiration. Reviews of academic literature and economic theories, as well as public news helped to identify the variables for the empirical study. These variables are; information demand, trade volume, world market index, trend and six specified events, occurring during the chosen sample period and included in the study as dummy variables. The variables are all analysed and included in a GARCH (1,1) model, modified following a similar research by Vlastakis & Markellos (2012) on stocks. This GARCH (1,1) model is then fitted to the bitcoin volatility registered for the sample period and is able thereby able to generate data of if and how the variables affect the bitcoin volatility. The test result suggests that five of the ten variables are significant on a 5 %-level. More specifically it suggests that information demand is a significant variable with a positive influence on the bitcoin volatility, something that corresponds to the literature on information demand and price volatility. This also relates to the events found significant, as they generated bitcoin related information. The significant events of the Cypriot crisis and the failure of the bitcoin exchange MtGox are thus specific examples of how information affects price volatility. Another significant variable is trade volume, which also displays a positive influence on the volatility. The last significant variable turned out to be a constructed positive trend, suggesting that increasing acceptance of bitcoin decreases its volatility.

台北房市自2003年的SARS低點過後逐漸回暖，並在2006年開始房價出現劇烈的漲幅，在決定房屋供給與需求的基本面未大幅變化的前提下，多數學者質疑台北的房價已呈現泡沫化，購屋的負擔已超過多數受薪家庭的支付能力。本文首先擬以購屋成本及投資報酬率的角度分析台北房市泡沫化的幅度，實證結果指出台北市的房價在1990年代及2006年後明顯出現泡沫化的現象，所得及租金推估的泡沫分別達到三成及六成的幅度，且2006年後的房價泡沫至今仍未有破裂跡象。在此一結論下，本文進一步分析生成台北房價泡沫的原因，實證結果指出房價出現泡沫化的同時，與股市報酬率及貴金屬報酬率明顯呈現正相關，貨幣供給增加亦是促成泡沫化的因素。 此外，對於房地產學界一直關注的議題，即房地產景氣預測及房地產價格的推估，本文亦利用貝式分析的技巧適度修補了現階段實證研究遭遇的困難。對房地產景氣的推估而言，加入事前訊息後的馬可夫轉換模型，在掌握房地產景氣擴短縮長的特性有顯著的改善，同時樣本外的預測亦說明其優越之處。在房地產估價方面，貝式多層次模型在面對較少樣本下的估價亦展現優越之處，特別是房價波動較大的期間，在不同樣本數目下，貝式多層式估計的精確度皆明顯優於傳統的特徵價格估計法。