This paper uses multivariate DCC-GARCH model to investigate the volatility of dynamic correlation between MSCI Taiwan stock index and the USA, China, Japan , Asia and global stock market. The existence of stock market volatility asymmetry, volatility spread of infection and clustering effects also are analysed, while in case of the U.S. sub-prime mortgage crisis and triggered the global financial tsunami. It discusses the Taiwan stock market fluctuations and structural changes in the international markets and the market dynamics related to change of influence and change. The main findings are (1)The volatility of continuity between the spread of infection and the clustering effect between the Taiwan stock market and international market fluctuations, (2) During the global financial tsunami, the correlation between changes in the international market and the market Correlation of different dynamic fluctuations and structural changes occurring in different time point also show the impact of changes of individual markets (3)The correlation between MSCI Taiwan stock index and the USA, China , Japan, indicates that the impact of change of stock the Japanese stock market on the MSCI Taiwan stock index is low, while China and the MSCI Taiwan stock index-related enhances, (4) market structure changes, the MSCI Taiwan stock index and the global dynamic fluctuations in the market is still a significant, The visible impact of the shock oscillation is wide and return to equilibrium of adjustment is still ongoing.

This dissertation investigates the volatility of the relationships between exchange rates and interest rates. The first part of the paper explores the transmission relationship between these two markets using a time-series model. Previous studies have assumed that covariance was constant in both markets. However, if the volatilities of the exchange rate and interest rate markets are correlated over time, the interaction and spillover effects between the two markets may be affected by time-varying covariance. Hence, this paper utilizes the BEKK-GARCH model developed by Engle and Kroner (1995) to capture the dynamic relationship between the exchange rates and interest rates. This study uses the returns data for G7 members¡¦ exchange rates and interest rates to test whether these markets exhibited volatilities spillover from 1978 to 2009. The results show bi-directional volatility spillovers in the markets of the UK, the Euro countries, and Canada, where the volatilities of the two markets were interrelated. The second part of the paper explores the relationship between exchange rates and interest rates using a jump diffusion model. Previous studies assumed that the dynamic processes of exchange rates and interest rates follow a diffusion process with a continuous time path, but an increasing number of empirical studies have shown that a continuous diffusion stochastic model does not capture the dynamic process of these variables. Thus, this paper investigates the discontinuous variables of exchange rates and interest rates and assumes that these variables follow a jump diffusion process. The UIRP model is employed to explore the relationship between both variables and to divide the systematic risk into systematic continuous risk and systematic jump risk. The returns data for G7 members¡¦ exchange rates and interest rates from 2005 to 2010 were analyzed to test whether the expected exchange rate is affected by jump components when the interest rate market experiences a jump. The results show that the jump diffusion model has more explanatory power than the pure diffusion model does, and, when the interest rate market experiences a jump risk, the systematic jump risk has a significant relationship with the expected exchange rates in some G7 countries.

Recently, there are more and more literatures discuss on the issues of investment strategies of leveraged ETFs. In our works, we concentrate our issues on optimal leverage of ETF of S&P 500 index. Based on ARMA-GARCH model¡¦s assumption, we find out that the forecasting optimal leverage can be shown in a formula which contains return and characteristic function. In this paper, we use MA(1)-GARCH(1,1) to forecast volatility based on 1008 rolling window to forecast one day ahead¡¦s volatility; and our estimation time is start from 1954 to March 2011. In this paper, we present four dynamic leverage models (Normal, Student T, VG, and Best model¡¦s leverage) to find out the payoffs under these models. In our model, the forecasting accuracy is just about 55% which is slightly higher than SPX raise probability. But during long-term compound effect, the dynamic leverage models can out-perform than constant leverage. There may exist some important factors in these results, one of them is the crash forecasting ability. During 1980 to 2011 SPX has 14 big crashes and these models can effectively avoid 10 big crashes. In short-term investment horizon none of these five models are always outperform than others but in long-term investment horizon the strategy of best model¡¦s leverage can always earn money when investment horizon is 2400 days.

This study explores the dynamics between stock price and exchange rates through the cointegration methodology proposed by Herwartz and Luetkepohl (2011). Moreover, it consider the vector error correction model (VECM) with conditional heteroscedastic variance. And we use a feasible generalized least squares (FGLS) estimator to estimate the cointegrating vector. This paper analysis some Asian countries' data from 1997 to 2010. The evidence result suggests that Malaysia and Singapor's stock price and exchange rate are positively related. But Hong Kong's stock price is negatively related to exchange rate.

This dissertation investigates the pricing behaviors of two major energy commodities, U.S. natural gas and crude oil, using times series models. It examines the relationships between U.S. natural gas price variations and changes in market fundamentals within a two-state Markov-switching framework. It is found that the regime-switching model does a better forecasting job in general than the linear fundamental model without regime-switching framework, especially in the case of 1-step-ahead forecast. Studies are conducted of the dynamics between crude oil price and U.S. dollar exchange rates. Empirical tests are applied to both full sample (1986—2010) and subsample (2002—2010) data. It is found that causality runs in both directions between the oil and the dollar. Meanwhile, a theoretical 5-country partial dynamic portfolio model is constructed to explain the dynamics between oil and dollar with special attention to the roles of China and Russia. It is shown that emergence of China‘s economy enhances the linkage between oil and dollar due to China's foreign exchange policy. Further research is dedicated to the role of speculation in crude oil and natural gas markets. First a literature review on theory of speculation is conducted. Empirical studies on speculation in commodity markets are surveyed, with special focus on energy commodity market. To test the theory that speculation may affect commodity prices by exaggerating the signals sent by market fundamentals, this essay utilizes the forecast errors from the first essay to investigate the forecasting ability of speculators' net long positions in the market. Limited evidence is provided to support the bubble theory in U.S. natural gas market. In conclusion, this dissertation explores both fundamentals and speculators' roles in the U.S. natural gas and global crude oil markets. It is found that market fundamentals are the major driving forces for the two energy commodities price booms seen during the past several years.

This article emphasizes on fitting GARCH option pricing model with Taiwan stock market. Duan¡¦s(1995) NGARCH option pricing model is adopted. Duan solved the European option by simulation, this article follow the method and extents to pricing American option. In general, simulation approach is not convenient to solve American options as well as European options. However, the least-squares method proposed by Longstaff and Schwartz is a simple and powerful tool, so this article tests the method. The NGARCH model has parameters, and base on loglikelihood function, we fit the model with empirical observations to obtain parameters. Then we can simulate the stock prices, once stock prices are simulated, the option value can be priced. Since the article simulates the option, there should be the antithetic approaches instead of simulation. In practice, the Black-Schoels model is the benchmark for pricing European option, so this article compares the simulated European options with Black-Scholes. For American option, this article compares the simulated American options which are priced by least-squares method with trinomial tree (finite difference method).

In this study, different from previous studies, the explanatory power of Student-t distribution is compared to normal distribution by employing both standard GARCH and EGARCH models to dollar/ lira (USD/TRY) exchange rate. Then the impact of Central Bank of Republic of the Turkey&rsquo / s (CBRT) decisions and actions on both the level of exchange rate and the volatility is investigated. Moreover the relationship between volatility and market liquidity is examined using spot foreign exchange (FX) market volume as a proxy. The results reveal that, in contrast to preceding findings, Student-t could not capture the leptokurtic property better than normal distribution does. Furthermore, an increase in Turkish government benchmark bond rates, CBRT FX purchase interventions and announcement of suspending/ decreasing-the-amount-of FX auctions lead Turkish lira to depreciate. Because of the significant positive leverage effect, the results of GARCH and EGARCH variance equations differ so much. Thereby the results should be evaluated cautiously. In addition it is observed that, only EGARCH model gives significant results when the spot market trading volume is included in the models

This study investigates the time varying behavior of the betas (systematic risk) for the Turkish REIT sector in an attempt to identify whether the betas for the Turkish REITs are stable and if not whether the declining trend valid for the REIT betas of many developed and developing countries is also observed for the Turkish REITs. Three different techniques / namely, Diagonal BEKK (DBEKK) GARCH model, the Schwert and Seguin model and the Kalman Filter algorithm, are employed in order to estimate and analyze the time varying betas of the Turkish REIT sector over the period 2002-2009. The empirical results suggest that, similar to many other countries, betas are not stable in the Turkish REIT sector. The general view of a declining beta trend for the REITs appears to prevail for Turkish REITs as well, reinforcing the defensive characteristics of these publicly traded real estate companies. Comparing the relative forecast accuracy of the three techniques employed, Schwert and Seguin model performs the worst both for weekly and daily data / whereas the Kalman Filter and the DBEKK Garch models provide the lowest forecast errors for the weekly and the daily data, respectively. This study also shows that the use of the data sets with different frequency could lead to different empirical findings.

The purpose of this thesis is to identify the factors playing a key role in the determination of the Turkish stock market liquidity in aggregate terms in a time series context and discuss the joint dynamics of the market-wide liquidity with its selected determinants and the trade volume. The main determinants tested are the level of return, the return volatility and the monetary stance of the Central Bank of the Republic of Turkey. The expected positive relationship between the liquidity and the return is confirmed, while the negative effect of the volatility on liquidity appears one-week later. The behavior of various liquidity variables are also examined around the macroeconomic data announcement dates, during the 2008 financial crisis, and after the tick size change in the Istanbul Stock Exchange (ISE). The time series dynamics between the trade volume, return, volatility and the liquidity are put forward within the Vector Autoregression analysis framework. The GARCH modeling of the return series, which is an input to the liquidity model estimations, is a byproduct of this thesis. It is observed that the return series exhibits volatility clustering, persistence, leverage effects and mean reversion. In addition, while the level of the ISE market return decreased, the volatility of the return increased during the 2008 crisis. Accordingly, EGARCH model assuming normally distributed error terms and allowing a shift in the variance during the crisis period is chosen as the best model.

none