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.
Animal health economics is becoming increasingly important as the assistance for decision making on animal health intervention at all levels in attempting to optimize animal health management. Economic analysis of the optimal control of zoonoses associated with livestock production is complex as it depends on the nature of occurrence, transmission, and circulation of the diseases. Recent studies show that the emphasis of most of the veterinary economists is usually on the practical field of the economic evaluation of animal diseases based on a detailed knowledge of the production system. However, the field had not yet begun to address the more complex and real-world problems such as cause of emerging diseases. This empirical research employs a more holistic approach such as that advocated by the Eco-Health One-Health approach, together with the transdisciplinary analytical framework and Bayesian Belief Network analysis that integrates uncertainties into consideration to explain Trichinellosis risk. This fundamental research found that the Bayesian Belief Network modeling for the analysis of zoonoses risk and a combined human and animal health framework can be used to guide decision making for interventions to solve the Eco-Health One-Health problem of Trichinellosis risk. However, the scoring rule results from Netica, an easy to use software for working with Bayesian Belief Network, provide only symmetric loss values based on the assumption that the loss from misestimating is the same in any direction. Nonetheless, this assumption may not be valid in some practical situations such as what we are interested in this research, Trichinellosis risk. The research suggests an approach that takes the idea of decision theory combining the cost of collecting a sample to minimize the pre-posterior expected cost. If the sampling cost of collecting data is very high, or if there is strong prior information about the risk, it is not worth sampling. Also, if the loss of illness is very high, a thorough protection strategy would be more efficient.
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.
In addition to active portfolio management, hedge funds are characterized by the allocation of portfolio performance between the external investors and the management firm accounts. This allocation can take different forms, such as the Loss Carry Forward scheme, and some of them can be coupled with performance smoothing techniques. This paper shows that this additional smoothing component might explain some empirical facts observed on the distribution and the dynamics of hedge fund returns.
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 and an account for the management firm, respectively. Despite a lack of transparency in hedge fund market, the strategy of performance allocation is publicly available. This paper shows that, for the High Water Mark Scheme, these complex performance allocation strategies might explain empirical facts observed in hedge fund returns, such as return persistence, skewed return distribution, bias ratio, or implied increasing risk appetite
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.
This paper aims to investigate the correlation of multivariate dependences between the international trade of Thailand and the USD/THB exchange rate using vine copulas, including canonical (C-vine) and drawable (D-vine) vine copulas which are very flexible dependency structures.Another advantage is that thesemethods overcome limitations and complex dependencymodels. Before we built the paircopula constructions of the vine models, ARMA(1,1)-GARCH(1,1) was adopted to remove time dependence in each of the marginal time series. Furthermore, we got the various standardized residuals to transform into appropriate uniform margins [0,1]. The results can be seen for C-vine case, Gaussian, Rotated Joe, and BB1 which are suitable bivariate copula families for each pair-copula construction. On the other hand, D-vine case, Gaussian, and Rotated Joe are appropriate copula families for the pair-copula construction. In addition, the sequential log-likelihood is quite close to the one obtained by joint maximization; it means that both the vine models are appropriate-fit models. In order to confirm that it is not possible to distinguish between the two models, we employed the Vuong and Clarke tests to verify the suitability of the non-nested model. These tests confirm that the C-vine and Dvine copulas are not distinguishable. It can be concluded that our pair constructions of the time-varying Gaussian copula could be appropriate fits, better than those of the static copula. This study will help policy makers take action to combat the exchange rate volatility.
The dollar is the leading international currency, and it is used widely in the majority of international financial transactions. The various food products that comprise agricultural commodities, as also crude oil, have been using the dollar exchange rate for international trade. Over the past several years, the changes in the dollar exchange rate have shown more volatility in addition to a depreciation trend, which has had an influence on the prices of those commodities. We analyzed the relationship between the dollar exchange rates and the prices of two commodities, palm oil and crude oil, by using the GARCH(1,1) model to examine the volatility of the exchange rates and the future prices 1-Pos. of the prices of both the commodities. The vine copula model is used to analyze the dependence structure between their marginal distributions. The data analyses were based on the daily observations from June 2007 to March 2013. The empirical results of GARCH(1,1) show that the exchange rates, palm oil prices, and crude oil prices have a long-run persistence in volatility. The C-vine copula model reveals that there exists a weak negative dependence for each pair-copula, that is, Exchange rate-Palm oil (E,P) and Exchange rate-Crude oil (E,C) in tree 1. Also, a conditional pair-copula of Palm oil-Crude oil given Exchange rate (P,C|E) in tree 2 offers a weak positive dependence. Moreover, the findings of this study provide evidence that the exchange rate (E) is an important variable that governs the interactions in the dependence structure between palm oil price (P) and crude oil price (C).
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.
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 conduct a study of the volatility and dependence between the exchange rate and inflation rate in Laos. The results of the study show that the ARMA (1, 1) - GARCH (1, 1) models were appropriate for two random variables. The KS and Box-Ljung tests for skewed-t distribution and autocorrelation performed in the study found that the two margins were skewed-t distribution and had no autocorrelation. The modeling of the best-fit copula from the testing process found that the time-varying t copula was the best of all static copulas and time-varying copulas in terms of the AIC and the BIC, which means that it has the highest explanatory power of all the dependence structures. In addition, we can see that the indicator of the correlation (dependence parameter: ) between the growth rates of the exchange rate and the inflation rate describes a high correlation in the long term, and also evinces that the dependence between the growth rates of the exchange rate and the inflation rate was positive, meaning that when the US Dollar appreciates, the inflation rate increases as well. Thus, this model as the time-varying t copula can help policy makers become more aware of what is likely to happen in the future.
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.
This study explains China’s agricultural commodities volatility by using the short-term deviations along with the domestic macroeconomic factors as well as the international price factors. The GARCH-X model shows that the short-term deviations make significant and positive effect on volatility, and so, it can be taken as an important factors in estimating and forecasting the agricultural prices. However, it is disappointing that some of the macroeconomic factors are not significant in our model. This is because China is in a transition process, and many macroeconomic factors are not freely moved. Our study also analyzes China’s policy and macroeconomic changes in last decades. To give a more thorough understanding about China’s recent macroeconomic reform is also one of our objectives.
This paper investigates the relationship between accident-related outcomes and per capita income, and explores the interdependency between them by using vine pair copula constructions. Equations for number of accidents, number of fatalities, and number of people injured are estimated using a provincial level data of Thailand in 2011.We discovered that there exists an inverted U-shaped relationship between accident-related outcomes and per capita income. Moreover, it was found that the accident-injury pair had stronger concordance and tail dependence, whereas the accident-fatality and fatality-injury pairs had weaker concordance and tail dependence. Our findings provide useful insight and information to policymakers who can then use the same to select appropriate road safety measures.
