• Journal of the Korean Data and Information Science Society
• /
• v.16 no.1
• /
• pp.33-40
• /
• 2005

The estimating function method was proposed by Godambe(1985) for parameter estimation under unknown distributions for errors in the models. Threshold Autoregressive Heteroscedastic (Threshold-ARCH) models have been developed by Zakoian(1994) and Li and Li(1996) for explaining the asymmetric properties in the financial time series data. In this paper, we apply the estimating function method to the Threshold-ARCH model and show that the proposed estimators perform better than the MLE under the heavy-tailed distributions.

• The Korean Journal of Applied Statistics
• /
• v.33 no.3
• /
• pp.297-308
• /
• 2020

We focus on the functional autoregressive conditional heteroscedasticity (fARCH) modelling to analyze intraday volatilities based on high frequency financial time series. Multivariate volatility models are investigated to approximate fARCH(1). A formula of multi-step ahead volatilities for fARCH(1) model is derived. As an application, in implementing fARCH(1), a choice of appropriate time interval for the intraday return is discussed. High frequency KOSPI data analysis is conducted to illustrate the main contributions of the article.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.01a
• /
• pp.122-127
• /
• 2001

This study investigates the effectiveness of time delay neural networks(TDNN) for the time dependent prediction domain. Although it is well-known fact that the back-propagation neural network(BPN) performs well in pattern recognition tasks, the method has some limitations in that it can only learn an input mapping of static (or spatial) patterns that are independent of time of sequences. The preliminary results show that the accuracy of TDNN is higher than the standard BPN with time lag. Our proposed approaches are demonstrated by the stork market prediction domain.

• The Korean Journal of Financial Studies
• /
• v.2 no.1
• /
• pp.215-244
• /
• 1995

The purpose of this paper is to examine the three anomalies phenomena that appear in the initial public offerings(IPOs) market. Of them, the first anomaly is that the new issues are underpriced in the short-run. Secondly, the hot issue market phenomenon appears. Thirdly, in the long-run, the initial public offerings of equities are overpriced. These phenomena have been documented by Inany studies using the us stock market data. In particular, we will investigate whether these three anomalies also appear in the UK new issues market. Firstly, the underpricing phenomenon of initial public offerings in the short-run will be examined. Then the long-run performance of new issues will be examined using cross-sectional and time-series analysis. Finally, we will briefly examine the existence of the hot issue market in the uk IPOs market.

• International Commerce and Information Review
• /
• v.12 no.1
• /
• pp.153-175
• /
• 2010

This study conducts an analysis to verify an existence of co-movement among the exchange rates of Yuan-Dollar, Yen-Dollar and Won-Dollar by using time series data. An analysis period is divided into two periods. Therefore the first analysis period is from Dec. 17, 1997 to Jul. 21th. 20, 2005 and the second analysis period is from Jul. 25th, 2005 to Nov. 20th. 2009. This paper uses VAR model and daily data of exchange rates during the period. According to the result of an empirical analysis, yuan-dollar exchange rate has affected by th other variables ; yen-dollar exchange rate. It can be proved by result of an impulse response test and variance decomposition test in the second period. Therefore the won-dollar, yen-dollar, and Yen-dollar exchange rate has been influenced each other and the relationship will be maintained.

• Management Science and Financial Engineering
• /
• v.15 no.2
• /
• pp.101-118
• /
• 2009

In quantitative contexts, data mining is widely applied to the prediction of stock prices from financial time-series. However, few studies have examined the potential of data mining for shedding light on the qualitative problem-solving knowledge of experts who make stock price predictions. This paper presents a GA-based data mining approach to characterizing the qualitative knowledge of such experts, based on their observed predictions. This study is the first of its kind in the GA literature. The results indicate that this approach generates rules with higher accuracy and greater coverage than inductive learning methods or neural networks. They also indicate considerable agreement between the GA method and expert problem-solving approaches. Therefore, the proposed method offers a suitable tool for eliciting and representing expert decision rules, and thus constitutes an effective means of predicting the stock price index.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.2
• /
• pp.365-374
• /
• 2012

Weather derivatives designed to manage casual changes of weather, as opposed to catastrophic risks of weather, are relatively a new class of financial instruments. There are still many theoretical and practical challenges to the effective use of these instruments. The objective of this paper is to develop a pricing approach for valuing weather derivatives and presents a case study that is practical enough to be used by the risk managers of electrical utility firms. Utilizing daily average temperature data of Guangzhou, China from $1^{st}$ January 1978 to $31^{st}$ December 2010, this paper adopted a univariate time series model to describe weather behavior dynamics and calculates equilibrium prices for weather futures and options for an electrical utility firm in the region. The results imply that the risk premium is an important part of derivatives prices and the market price of risk affects option values much more than forward prices. It also demonstrates that weather innovation as well as weather risk management significantly affect the utility's financial outcomes.

• East Asian Economic Review
• /
• v.28 no.1
• /
• pp.95-140
• /
• 2024

This study empirically investigates the international transmission of China's uncertainty shocks. It estimates a time-varying parameter Bayesian global structural vector autoregressive model (TVP-BGVAR) using time series data for 33 countries to evaluate heterogeneous international linkage across countries and time. Uncertainty shocks are identified via sign restrictions. The empirical results reveal that an increase in uncertainty in China negatively affects the global economy, but those effects significantly vary over time. The effects of China's uncertainty shocks on the global economy have been significantly altered by China's WTO accession, the global financial crisis, and the recent US-China trade conflict. Furthermore, the effects of China's uncertainty shocks, typically on inflation, differ significantly across countries. Moreover, Trade openness appears crucial in explaining heterogeneous GDP responses across countries, whereas the international dimension of monetary policy appears to be important in explaining heterogeneous inflation responses across countries.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.87-94
• /
• 2020

This study aims to evaluate the effectiveness of sterilization in Vietnam. We estimate a simultaneous equation by using Two-Stage least squares (2SLS) regression analysis. The time-series data was collected for the first quarter of 2004 to the fourth quarter of 2018. In particular, the effectiveness of sterilization is considered in terms of dollarized economy, since making the transition from a centrally planned to a market economy system, the Vietnamese economy has remained in a state of dollarization. In addition, we also assess whether the global financial crisis had an impact on the sterilization effectiveness of the State Bank of Vietnam (SBV). On the basis of the estimated sterilization and offset coefficients, our results suggest that the State Bank of Vietnam (SBV) has not been able to fully neutralize the impact on the domestic money supply when intervening in the foreign exchange market, and the capital inflows respond strongly to changes in domestic monetary conditions. The results also show that the global financial crisis has changed the effectiveness of these sterilization policies. An analysis of this study's empirical findings provides the opportunity to derive some recommendations that may assist in increasing the effectiveness of the State Bank of Vietnam's sterilization policies in the process of accumulating international reserves.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.11-19
• /
• 2021

This study aims to analyze the causality relationship between sectoral stock markets (agricultural, financial, industrial, and mining sectors) and economic growth in the short and long term as well as to analyze whether it has similar types or not. The data used is quarterly time-series data (first quarter 2009 to fourth 2019). To determine the causality relationship, this study conducts a variable and multivariate causality test. The results of the varying granger causality test show that there is only a one-way relationship, where the economic growth of the agriculture sector affects its shares. A one-way relationship also occurs in stocks of the industrial sector, which has an influence on economic growth. The multivariate causality test shows that the economic growth of the agricultural sector has a two-way causality relationship, and it also exists between the industrial sector and the financial sector stock markets. The two-way causality relationship between the stock market and sectoral economic growth is a convergence towards long-term equilibrium. The findings of this study suggest that the government through the Financial Services Authority and the Indonesia Stock Exchange have to maintain stability in the stock market as a supporter of the national economy.