Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation

Tourism is one of the key service industries in Thailand, with a 5.27% share of Gross Domestic Product in 2003. Since 2000, international tourist arrivals, particularly those from East Asia, to Thailand have been on a continuous upward trend. Tourism forecasts can be made based on previous observations, so that historical analysis of tourist arrivals can provide a useful understanding of inbound trips and the behaviour of trends in foreign tourist arrivals to Thailand. As tourism is seasonal, a good forecast is required for stakeholders in the industry to manage risk. Previous research on tourism forecasts has typically been based on annual and monthly data analysis, while few past empirical tourism studies using the Box–Jenkins approach have taken account of pre-testing for seasonal unit roots based on Franses [P.H. Franses, Seasonality, nonstationarity and the forecasting of monthly time series, International Journal of Forecasting 7 (1991) 199–208] and Beaulieu and Miron [J.J. Beaulieu, J.A. Miron, Seasonal unit roots in aggregate U.S. data, Journal of Econometrics 55 (1993) 305–328] framework. An analysis of the time series of tourism demand, specifically monthly tourist arrivals from six major countries in East Asia to Thailand, from January 1971 to December 2005 is examined. This paper analyses stationary and non-stationary tourist arrivals series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box–Jenkins autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models are estimated, with the tourist arrivals series showing seasonal patterns. The fitted ARIMA and seasonal ARIMA models forecast tourist arrivals from East Asia very well for the period 2006(1)–2008(1). Total monthly and annual forecasts can be obtained through temporal and spatial aggregation.     

Modelling sustainable international tourism demand to the Brazilian Amazon

The Amazon rainforest is one of the world's greatest natural wonders and holds great importance and significance for the world's environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil's north region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. By sustainable tourism is meant a distinctive type of tourism that has relatively low environmental and cultural impacts. Economic progress brought about by illegal wood extraction and commercial agriculture has destroyed large areas of the Amazon rainforest. The sustainable tourism industry has the potential to contribute to the economic development of the Amazon region without destroying the rainforest. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.     

An econometric analysis of SARS and Avian Flu on international tourist arrivals to Asia

This paper compares the impacts of SARS and human deaths arising from Avian Flu on international tourist arrivals to Asia. The effects of SARS and human deaths from Avian Flu are compared directly according to the number of human deaths. The nature of the short run and long run relationship is examined empirically by estimating a static line fixed effect model and a difference transformation dynamic model, respectively. Empirical results from the static fixed effect and difference transformation dynamic models are consistent, and indicate that both the short run and long run SARS effect have a more significant impact on international tourist arrivals than does Avian Flu. In addition, the effects of deaths arising from both SARS and Avian Flu suggest that SARS is more important to international tourist arrivals than is Avian Flu. Thus, while Avian Flu is here to stay, its effect is currently not as significant as that of SARS.     

ARMAX modelling of international tourism demand

Box–Jenkins (1970) models are often used to capture the autoregressive moving average of past observations of tourist arrivals from Japan to Taiwan and New Zealand. However, other explanatory variables, such as real income in the origin country, have also affected the demand for international travel. The purpose of this paper is to use the ARMAX model to investigate the dynamic relationship between tourism demand and real income of Japan, and to compare the findings with the single-equation model. Unit root tests and diagnostics are performed before estimating the income elasticity of travel demand by Japan for New Zealand and Taiwan based on seasonally unadjusted quarterly data for 1980(1) to 2004(2). The empirical results of the ARMAX model support the economic theory that the demand for international travel is positively related to income of the origin country.     

Optimizing mathematical parameters of Grey system theory: an empirical forecasting case of Vietnamese tourism

Accurately forecasting the demand for international and domestic tourism is a key goal for tourism industry leaders. The purpose of this study is to present more appropriate models for forecasting the demand for tourism in Vietnam. The authors apply GM(1,1), Verhulst, DGM(1,1) and DGM(2,1) to test which concise prediction models can improve the ability to predict the number of tourists visiting this country. In order to guarantee the accuracy of forecasting process, data cover in the period from 2005 through 2013 and are obtained from the official website of VNATR “Vietnam National Administration of Tourism” report. The MAPE, MSE, RMSE and MAD are four important criteria which are used to compare the various forecasting models results. Key findings indicate that the optimal value of GM(1,1), Verhulst, DGM(1,1) can enhance the forecasting results perfectly with minimum predicted errors. In the case of the tourism revenue, using the Verhulst model is evidently better than the others. For the number of international and domestic tourist prediction, the application of Verhulst and DGM(1,1) models is well done. For visitors coming from specific countries (i.e., China, Korea, Taiwan, Japan and America), DGM(2,1) is very poor for predicting in this situation, whereas remaining three models GM(1,1), Verhulst, DGM(1,1) and DGM(2,1) perform excellently. The results also pointed out that the tourism demands in Vietnam are growing rapidly; thus, the governments must be well prepared for tourism industry and enhance relative fundamental construction for tourism markets.

     

Comparison of MCMC Methods for Estimating Stochastic Volatility Models

This article investigates performances of MCMC methods to estimate stochastic volatility models on simulated and real data. There are two efficient MCMC methods to generate latent volatilities from their full conditional distribution. One is the mixture sampler and the other is the multi-move sampler. There is another efficient method for latent volatilities and all parameters called the integration sampler, which is based on the mixture sampler. This article proposes an alternative method based on the multi-move sampler and finds evidence that it is the best method among them.JEL classification C22     

A detailed comparison of value at risk estimates

This work investigates the performance of different models of value at risk. We include several methods (parametric, historical simulation, Monte Carlo, and extreme value theory) and some models to compute the conditional variance. We analyze several international stock indexes and examine two types of periods: stable and volatile periods. To choose the best model, we employ a two-stage selection approach. The result indicates that the best model is a parametric model with conditional variance estimated by an asymmetric GARCH model under Student's t-distribution of returns. This paper shows that parametric models can obtain successful VaR measures if conditional variance is estimated properly.     

Modelling risk in agricultural finance: Application to the poultry industry in Taiwan

The volatility in agricultural prices, such as for broiler and color broiler chickens in Taiwan, is similar in various aspects to financial volatility as it relates to the risk and returns associated with agricultural production. However, as the characteristics of agricultural markets may be different from financial markets, the results arising from empirical risk analysis need to be investigated. The broiler and color broiler industries are the second and third largest livestock industries in Taiwan. When Taiwan applied to join the World Trade Organization (WTO) in the 1990s, these two industries faced the threat of deregulation of chicken meat imports. However, developments in these two industries have not been the same under deregulation, with the level of competition in the broiler and color broiler industries being markedly different. The purpose of the paper is to model the prices, growth rates and their respective volatilities in weekly broiler and color broiler chicken prices in Taiwan from January 1995 to June 2007. The empirical results show that the time series of broiler and color broiler prices, their logarithms and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. The empirical second moment and log-moment conditions also support the statistical adequacy of the estimated volatility models. The empirical results have significant implications for risk management and policy considerations in the agricultural production industry in Taiwan.     

Modeling Hong Kong’s stock index with the Student t-mixture autoregressive model

It is well known that financial returns are usually not normally distributed, but rather exhibit excess kurtosis. This implies that there is greater probability mass at the tails of the marginal or conditional distribution. Mixture-type time series models are potentially useful for modeling financial returns. However, most of these models make the assumption that the return series in each component is conditionally Gaussian, which may result in underestimates of the occurrence of extreme financial events, such as market crashes. In this paper, we apply the class of Student t-mixture autoregressive (TMAR) models to the return series of the Hong Kong Hang Seng Index. A TMAR model consists of a mixture of g autoregressive components with Student t-error distributions. Several interesting properties make the TMAR process a promising candidate for financial time series modeling. These models are able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time-varied from short- to long-tailed or from unimodal to multi-modal. The use of Student t-distributed errors in each component of the model allows for conditional leptokurtic distribution, which can account for the commonly observed unconditional kurtosis in financial data.     

On pseudo maximum likelihood estimation for multivariate time series models with conditional heteroskedasticity

We consider a general multivariate conditional heteroskedastic model under a conditional distribution that is not necessarily normal. This model contains autoregressive conditional heteroskedastic (ARCH) models as a special class. We use the pseudo maximum likelihood estimation method and derive a new estimator of the asymptotic variance matrix for the pseudo maximum likelihood estimator. We also study four special cases in this class, which are conditional heteroskedastic autoregressive moving-average models, regression models with ARCH errors, models with constant conditional correlations, and ARCH in mean models.     

Structural breaks,tourism development,and economic growth: Evidence from Taiwan

Our paper differs from previous studies by examining the issue of whether regime changes have broken down the stability of the long-run relationships between tourism development and real GDP in Taiwan for the 1959–2003 period. We empirically investigate the co-movements and the causal relationships among real GDP, tourism development, and the real exchange rate in a multivariate model. We use two different tourism variables—international tourism receipts and number of international tourist arrivals. To employ the unit root tests and the cointegration tests allowing for a structural break, the empirical evidence clearly shows that the causality between tourism and economic growth is bi-directional. Lastly, the international and cross-strait political change, economic shocks, and the relaxing of some tourism control and policies would break down the stability of the relationships between tourism development and economic growth. Overall, we do find the structural breakpoints, and they look to match clearly with the corresponding critical economic, political, or tourist incidents.     

A novel evolutionary-negative correlated mixture of experts model in tourism demand estimation

Mixtures of experts (ME) model are widely used in many different areas as a recognized ensemble learning approach to account for nonlinearities and other complexities in the data, such as time series estimation. With the aim of developing an accurate tourism demand time series estimation model, a mixture of experts model called LSPME (Lag Space Projected ME) is presented by combining ideas from subspace projection methods and negative correlation learning (NCL). The LSPME uses a new cluster-based lag space projection (CLSP) method to automatically obtain input space to train each expert focused on the difficult instances at each step of the boosting approach. For training experts of the LSPME, a new NCL algorithm called Sequential Evolutionary NCL algorithm (SENCL) is proposed that uses a moving average for the correlation penalty term in the error function of each expert to measure the error correlation between it and its previous experts. The LSPME model was compared with other ensemble models using monthly tourist arrivals to Japan from four markets: The United States, United Kingdom, Hong Kong and Taiwan. The experimental results show that the estimation accuracy of the proposed LSPME model is significantly better than the other ensemble models and can be considered to be a promising alternative for time series estimation problems.     

Estimating stochastic volatility models of stock returns in Chinese markets

Volatility plays a key role in microstructure issues in the study of financial markets. Stochastic volatility (SV) models have been applied to the study of the behavior of financial variables. Two stock markets exist in China: Shanghai Stock Exchange and Shenzhen Stock Exchange. As emerging stock markets, investors are increasingly concerned about the volatilities of these two stock markets. We briefly introduce how to estimate SV models using the Markov chain Monte Carlo (MCMC) method. In order to do full and comprehensive analyses of the volatilities of stock returns, we estimated SV models using most of the historical data and the different data frequencies of the two Chinese markets. We found that estimated values of volatility parameters are very high for all data frequencies. This suggests that stock returns are extremely volatile even at long-term intervals in Chinese markets.     

Clarifying the dynamics of the relationship between option and stock markets using the threshold vector error correction model

This work examines how the option and stock markets are related when using the threshold vector error correction model (hereinafter referred to as threshold VECM). Moreover, compared to previous studies in the literature of application of threshold models, this study not only investigates the impacts of price transmission mechanisms on stock return means but also the volatilities of returns. The model is tested using the U.S. S&P 500 stock market. The empirical findings of this investigation are consistent with the following notions. First, the equilibrium re-establishment process depends primarily on the option market and is triggered only when price deviations exceed a critical threshold. Second, arbitrage behaviors between the option and stock markets increase volatility in these two markets and reduce their correlation.     

Spectral analysis of seasonality in tourism demand

This paper aims to analyse the seasonality in New Zealand tourism demand from Australia and the USA using spectral analysis. Tourism demand is divided into four different categories depending on the tourists’ visiting purposes as registered in the customs cards upon their arrivals in New Zealand. Spectral analysis based on the sample from January 1980 to December 2007 revealed that different travel purposes share similar cyclical components but their contributions to the total variation in tourism demand differ between travel purposes and markets. More importantly, the results show that analysing aggregated data is often not sufficient to reveal the underlying seasonal patterns of tourist arrivals and policy makers would benefit greatly by analysing disaggregated data rather than relying on the analysis of aggregated data alone.     

Optimization models for computer data storage design: An application

In this paper we discuss a model being used to optimize the system design of the Computer Centre of one of the most important Italian banking groups. Data and transactions, processed by the system, are grouped respectively in data sets and by type, so it is possible to deal with the large dimensions of the corresponding optimization models. The transactions' arrivals are considered as stochastic variables and their probability values are estimated on the base of theoretical considerations. The solutions for two optimization problems, constructed and solved for different scenarios, are discussed in detail.     

Neural network models for conditional distribution under bayesian analysis

We use neural networks (NN) as a tool for a nonlinear autoregression to predict the second moment of the conditional density of return series. The NN models are compared to the popular econometric GARCH(1,1) model. We estimate the models in a Bayesian framework using Markov chain Monte Carlo posterior simulations. The interlinked aspects of the proposed Bayesian methodology are identification of NN hidden units and treatment of NN complexity based on model evidence. The empirical study includes the application of the designed strategy to market data, where we found a strong support for a nonlinear multilayer perceptron model with two hidden units.     

Estimating stochastic volatility models using daily returns and realized volatility simultaneously

Realized volatility, which is the sum of squared intraday returns over a certain interval such as a day, has recently attracted the attention of financial economists and econometricians as an accurate measure of the true volatility. In the real market, however, the presence of non-trading hours and market microstructure noise in transaction prices may cause bias in the realized volatility. On the other hand, daily returns are less subject to noise and therefore may provide additional information on the true volatility. From this point of view, modeling realized volatility and daily returns simultaneously based on the well-known stochastic volatility model is proposed. Empirical studies using intraday data of Tokyo stock price index show that this model can estimate realized volatility biases and parameters simultaneously. The Bayesian approach is taken and an efficient sampling algorithm is proposed to implement the Markov chain Monte Carlo method for our simultaneous model. The result of the model comparison between the simultaneous models using both naive and scaled realized volatilities indicates that the effect of non-trading hours is more essential than that of microstructure noise and that asymmetry is crucial in stochastic volatility models. The proposed Bayesian approach provides an estimate of the entire conditional predictive distribution of returns under consideration of the uncertainty in the estimation of both biases and parameters. Hence common risk measures, such as value-at-risk and expected shortfall, can be easily estimated.     

Experiments on the application of IOHMMs to model financial returnsseries

Input-output hidden Markov models (IOHMM) are conditional hidden Markov models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be linear, logistic, or nonlinear (using for example multilayer neural networks). We compare the generalization performance of several models which are special cases of input-output hidden Markov models on financial time-series prediction tasks: an unconditional Gaussian, a conditional linear Gaussian, a mixture of Gaussians, a mixture of conditional linear Gaussians, a hidden Markov model, and various IOHMMs. The experiments compare these models on predicting the conditional density of returns of market and sector indices. Note that the unconditional Gaussian estimates the first moment with the historical average. The results show that, although for the first moment the historical average gives the best results, for the higher moments, the IOHMMs yielded significantly better performance, as estimated by the out-of-sample likelihood.     

Predicting returns on Canadian exchange rates with artificial neural networks and EGARCH-M models

This paper investigates the problem of predicting daily returns based on five Canadian exchange rates using artificial neural networks and EGARCH-M models. First, the statistical properties of five daily exchange rate series (US Dollar, German Mark, French Franc, Japanese Yen and British Pound) are analysed. EGARCH-M models on the Generalised Error Distribution (GED) are fitted to the return series, and serve as comparison standards, along with random walk models. Second, backpropagation networks (BPN) using lagged returns as inputs are trained and tested. Estimated volatilities from the EGARCH-M models are used also as inputs to see if performance is affected. The question of spillovers in interrelated markets is investigated with networks of multiple inputs and outputs. In addition, Elman-type recurrent networks are also trained and tested. Comparison of the various methods suggests that, despite their simplicity, neural networks are similar to the EGARCH-M class of nonlinear models, but superior to random walk models, in terms of insample fit and out-of-sample prediction performance.