Chinese and Indian are the emerging tourist markets for Thailand. The two nations have tourism potential and make for interesting on doing a study about their tourism demand that was measure as the number of tourist arrivals. This study analyzed relationship between the tourist arrivals from China and India to Thailand by using the copula based GARCH model and the seasonal pattern. The findings by the copula based GARCH model show that there exists a weak positive dependence between the growth rates of tourist arrivals from China and India to Thailand and that this dependence keeps varying over time. The rotated Joe 180. copula, which can capture the lower (left) tail dependence, is chosen to describe the dependence structure. These mean that the growth rates of the tourist arrivals from China and India show a co-movementwhich is both upward and downward but with weak dependence. The rise or loss of tourism demand from China (India) is slightly correlated by a rise or loss of tourism demand from India (China). These results correspond to the seasonal patterns in which the seasonal pattern of China is in a direction opposite to the seasonal pattern of India in several periods, and the patterns showing a co-movement during some periods. Understanding the relationship between Chinese arrivals and Indian arrivals in each time period, it could contribute to policy implications such as developing the appropriate marketing and promotion strategies to attract other tourist markets as substitutes when we lose the regular tourist markets due to shock effects or low season.
We consider joint estimation of conditional Value-at-Risk (VaR) at several levels, in the framework of general conditional heteroskedastic models. The volatility is estimated by Quasi-Maximum Likelihood (QML) in a first step, and the residuals are used to estimate the innovations quantiles in a second step. The joint limiting distribution of the volatility parameter and a vector of residual quantiles is derived. We deduce confidence intervals for general Distortion Risk Measures (DRM) which can be approximated by a finite number of VaR’s. We also propose an alternative approach based on non Gaussian QML which, although numerically more cumbersome, has interest when the innovations distribution is fat tailed. An empirical study based on stock indices illustrates the theoretical findings.
This study examines volatility and co-movement structures of coal and agricultural commodities index returns in China’s bioful era. After taking into account the periodicity of changes in coal and agriculture prices, we show that the Period-GARCH (P-GARCH), which captures the characteristics of two commodities is more adequate in contrast to the previously proposed models where the residuals were skewed and had kurtosis, here the resulting residuals are almost Gaussian. Finally, our proposed P-GARCH time-varying copula models indicate that the dependence between energy and agricultural commodities index returns is positive and increasingly stable.
This paper considers the problem of systemic knowledge synthesis for product recommendation based on the theory of knowledge construction systems. This theory suggests actors to collect knowledge from scientific, social, and creative dimensions and to synthesize them systemically. It is believed that the pursuit of systematic, or mathematical approach in the scientific dimension is the role of a researcher. This paper mainly introduces mathematical information aggregation techniques for product recommendation, but these techniques usually give only partial answers. Finally, the paper returns to the theory of knowledge synthesis to suggest how to provide a better answer to the problem.
China’s economy has experienced rapid development in the past 20 years. In 2010, China’s GDP was valued at $5.87 trillion, surpassing Japan’s $5.47 trillion, and the nation became the world’s second largest economy after the USA. People’s incomes are also rapidly rising in all parts of the country. However, along with the prosperity seems to have come a malady that is the modern world’s woe: obesity. In China, the prevalence of obesity has increased dramatically. Obesity and its related diseases lay a heavy burden on medical expenditure and constrain economic development. Therefore, it is urgent and imperative to identify those influencing factors related to obesity, and take some measures andmake corresponding, appropriate policies to control its prevalence. The objective of this study is to identify the impact factors of obesity from different levels, and to evaluate whether the relationship between urbanization and obesity can be explained by individual socio-demographic, socioeconomic factors and lifestyle habits. Three-level logistic models are used in this paper to evaluate the relationship between each indicator and obesity.
Geometric Process (GP) model is proposed as an alternative model for financial time series. The model contains two components: the mean of an underlying renewal process and the ratio which measures the direction and strength of the dynamic trend pattern over time. They simultaneously account for the uncertainty on the mean and the autoregressive and time-varying nature of the volatility. Compare to the popular GARCH and SV models, this model is simple and easy to implement using the least squares (LS) method.We extend the GP model to analyze the daily asset price range which exhibit threshold and asymmetric effects for some exogenous variables. Models are selected according to mean square error (MSE). Finally forecasting are performed for the best model that allows for both threshold and asymmetric effects.
Standard pricing theory assumes that traders can borrow and lend at a unique risk-free rate, ignoring the intricacies of the collateralization market. Since 2007, the market has adopted an advanced methodology for valuing interest rate derivatives, based on the standard Credit Support Annex (CSA), which is a document used to define the terms under which collateral is posed between counterparties. This change however, has not yet been implemented in South African markets due to the difficulty created by the lack of a liquid overnight indexed swap (OIS) market in South Africa. In this paper, we propose two proxies, which could be used to approximate an OIS market.We compare the implied forward rates as well as the pricing of a vanilla swap under these OIS methods to the classical case.
This study uses maximum entropy method to find an optimal combination of energy sources for electricity generation in Thailand. It sets three targets including unit cost, risk and pollution. In the optimization process, it forms three constraints according to these three targets. It solves the system following the guideline of Golan, Judge and Miller (1996). It analyses six scenarios of the targets. For the major results, it finds that hydropower, nuclear, wind and solar energy are major sources of electricity generation. The country cannot avoid adopting nuclear energy for its electricity generation in order to meet all the three targets that are optimal for its electricity generation and economic development.
This paper aims at analyzing the financial risk and co-movement of stock markets in three countries: Indonesia, Philippine and Thailand. It consists of analyzing the conditional volatility and test the leverage effect in the stock markets of the three countries. To capture the pairwise and conditional dependence between the variables, we use the method of vine copulas. In addition, we illustrate the computations of the value at risk and the expected shortfall using Monte Carlo simulation with copula based GJR-GARCH model. The empirical evidence shows that all the leverage effects add much to the capacity for explanation of the three stock returns, and that the D-vine structure is more appropriate than the C-vine one for describing the dependence of the three stock markets. In addition, the value at risk and ES provide the evidence to confirm that the portfolio may avoid risk in significant measure.
A characteristic of hedge funds is not only an active portfolio management, but also the allocation of portfolio performance between different accounts, which are the accounts for the external investors, an account for the management firm and a provision account. Despite a lack of transparency in hedge fund market, the strategy of performance allocation is publicly available. This paper shows that these complex performance allocation strategies might explain stylized facts observed in hedge fund returns, such as return persistence, skewed return distribution, bias ratio, or implied increasing risk appetite.
In many practical situations, the dependence between the quantities is linear or approximately linear. Knowing that the dependence is linear simplifies computations; so, is is desirable to detect linear dependencies. If we know the joint probability distribution, we can detect linear dependence by computing Pearson’s correlation coefficient. In practice, we often have a copula instead of a full distribution; in this case, we face a problem of detecting linear dependence based on the copula. Also, distributions are often heavy-tailed, with infinite variances, in which case Pearson’s formulas cannot be applied. In this paper, we show how to modify Pearson’s formula so that it can be applied to copulas and to heavy-tailed distributions.
Pairs trading is a popular strategy on Wall Street. Most pairs trading strategies are based on a minimum distance approach or cointegration method. In this paper, we propose an alternative model to the process of pair return spread. Specifically, we model the return spread of potential stock pairs as a three-regime threshold autoregressive model with GARCH effects (TAR-GARCH), and the upper and lower regimes in the model are used as trading entry and exit signals. An application to the Dow Jones Industrial Average Index stocks is presented.
This paper investigates the volatility and dependence of Chinese tourism demand for Singapore, Malaysia, and Thailand (SMT) destinations, using the vine copula based auto regression moving average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model. It is found that a jolt to the tourist flow can have long-standing ramifications for the SMT countries. The estimation of the vine copulas among SMT show that the Survival Gumbel, Frank, and Gaussian copulas are the best copulas for Canonical vine (C-vine) or Drawable vine (D-vine) among the possible pair-copulas. In addition, this paper illustrates the making of time-varying Frank copulas for vine copulas. Finally, there is a discussion on tourism policy planning for better managing the tourism demand for the SMT countries. We suggest tour operators and national tourism promotion authorities of SMT collaborate closely in the marketing and promotion of joint tourism products.
Market interdependence has always been an interesting topic in the study of tourism demand. China, Japan, and Korea are important tourist markets for Thailand tourism. Understanding how the arrivals relate to each other can help in tourism management, in a way that it prepares the tourism industry to plan for the risk management of the tourism demand and tourism supply. The vine copula model was used to analyze the multiple dependencies by decomposing the diversity of the paircopulas which can be arranged and analyzed in a tree structure. For this study, both the C-vine copula and the D-vine copula were used to answer the research question. We give the same conditioning variable for both the C-vine and the D-vine copula models in order to find the answer to our question of whether these two models would give different results. The contributions of the study are obtained from the findings. The C-vine and D-vine copulas provided three pair-copulas, namely, China-Korea, China-Japan, and Korea-Japan given China and there exists a weak positive dependence in each pair. In addition, the results provide evidence that China has influence on the dependence between the tourist arrivals from Korea and Japan. Moreover, the three dimensions of the C-vine and D-vine copula models, which are given the same conditioning variable in the second tree, optimally provide the same estimates of the parameters of interest.
In this study, we used the Monte Carlo simulations to investigate the phenomena in the stock-price market which we considered as a function of temperature and external field which reflect the effects of the environment (e.g., access to external information). The Monte Carlo simulation was used to simulate the Ising model with heat-bath algorithm. The results show that the average orientation of the agents varies with the external field at constant temperature. In other words, the agents always buy when they get good news. And at high temperature, with constant positive external field, the average orientation of the agents is decreased to near zero.
This paper aims to study the structure of interdependencies between the energy, biofuel and agricultural commodity markets. The work concentrates on the dependence between ethanol and agricultural futures returns conditional to crude oil returns, and interdependence among agricultural commodities conditional to crude oil and ethanol futures returns. The C-vine copula based ARMA-GARCH model was used to explain the dependence structure of crude oil and the four related variables, and applied to investigate the risk of energy-agricultural commodity futures portfolio.We generally found symmetry in the tail dependence between the energy, biofuel, and agricultural commodities, and also found a greater significant variability in dependence, specifically, the dependence between the ethanol and agricultural commodity futures returns conditional to crude oil as well as interdependence between corn and soybean conditional to crude oil and ethanol return. This indicates that there is a rise in ethanol productions and that higher crude oil prices have caused a price increase in agricultural commodities such as corn and soybean. Moreover, the higher dynamic dependence and symmetric tail dependences indicate that opportunities for portfolio diversification are reduced, particularly during a downturn in the markets. Finally, our result suggests that the time-varying copula model captures the portfolio risk better than the static copula models.
In this paper we study the term structure of interest rates and test the rational expectations hypothesis using single regression equations and then multivariate regression equations. Single regression equations are found to produce results that are sensitive to outliers due to finite sample. Multivariate regression equations produce results that are less sensitive to outliers due to a larger sample size, and in our sample, yield a borderline rejection of the rational expectatons hypothesis. We apply a distance covariance test statistic measuring the deviation from independence between the forward forecast errors and present information variables. This measure is asymptotically distributed to be bounded below by \(\chi^{2}_{1}\) for usual ranges of critical region, and does not require any distributional assumption. The rational expectation hypothesis is more clearly rejected using the distance covariance metric. There is thus preliminary evidence that distributional and linearity mis-specification of the rationality hypothesis in the term structure could potentially biased toward non-rejection of an otherwise generally unsustainable hypothesis.
In the last decade, vine copulas emerged as a new efficient techniques for describing and analyzing multi-variate dependence in econometrics; see, e.g., [1, 2, 3, 7, 9, 10, 11, 13, 14, 21]. Our experience has shown, however, that while these techniques have been successfully applied to many practical problems of econometrics, there is still a lot of confusion and misunderstanding related to vine copulas. In this paper, we provide a motivation for this new technique from the computational viewpoint. We show that other techniques used to described dependence - Bayesian networks and fuzzy techniques - can be viewed as a particular case of vine copulas.
Political cycles in the Australian stock market from January 1901 to July 2011 are analysed through econometric volatility models. The stochastic volatility model with a skew t distribution for return and a Student-t distribution for volatility is proposed for analysis, estimated via Bayesian techniques. Evidence from the full period shows higher return under non-Labor governments while there is little evidence of election or length-of-term effects on market return. If we split the data before and after World War II, political cycles are non-existent. There is however clear evidence of positive skewness of returns before the war compared to negative skewness otherwise.
The vision of Semantic Web services promises a network of interoperable Web services over different sources. A major challenge to the realization of this vision is the lack of automated means of acquiring domain ontologies necessary for marking up the Web services. In this paper, we propose the DeepMiner system which learns domain ontologies from the source Web sites. Given a set of sources in a domain of interest, DeepMiner first learns a base ontology from their query interfaces. It then grows the current ontology by probing the sources and discovering additional concepts and instances from the data pages retrieved from the sources. We have evaluated DeepMiner in several real-world domains. Preliminary results indicate that DeepMiner discovers concepts and instances with high accuracy.
