• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.185-190
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• 2024

In 2022, we can see the real estate market in Korea going down. Corona 19 and the Russian invasion of Ukraine are cited as the biggest causes for this. These two problems ignited the economic recession, causing prices to fall and subsequently raising exchange rates and interest rates. Due to the aforementioned problems in the previously active real estate market, the number of actual transactions has decreased, resulting in a decline in the real estate market due to high interest rates. Data provided by the public data portal, KOSIS, and the Seoul Metropolitan Government were collected through Logstash, transferred to Elasticsearch, and visualized inflation, exchange rates, and loan interest rates using the dashboard function provided by Kibana, to analyze causes and derive results. In addition, three specific apartments in Nowon-gu and Jongno-gu, which have the highest number of actual transactions in Seoul, are selected and the actual transaction prices that change every month are displayed in the Data Table.

• Communications of the Korean Mathematical Society
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• v.31 no.2
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• pp.415-422
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• 2016

We derive a closed-form expression for the price of a European quanto call option when both foreign and domestic interest rates follow the Vasicek's short rate model.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.179-184
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• 2005

Empirical findings on interet rate dynamics imply that short rates show some long memories and non-Markovin. It is well-known that fractional Brownian motion(fBm) is a proper candidate for modelling this empirical phenomena. fBm, however, is not a semimartingale process. For this reason, it is very hard to apply such processes for asset price modelling. With some modifications, this paper investigate the fBm interest rate theory, and obtain a pure discount bond price and Greeks.

• Korean Journal of Agricultural Science
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• v.30 no.1
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• pp.89-101
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• 2003

For the pre-feasibility study and project evaluation, the discount rate or cutoff rate should be given by the government authority. To get the Benefit/Cost Ratio, NPV(Net Present Value) and IRR(Internal Rate of Return) of the public investment projects, the pertinent and realistic discount rate should be determined to be suitable to the present time period. The cut-off rates in Korea were equivalent to 15% in 1970's, 10% in 1980's and 8% in 1990's. The prevailing rate of discount as 8% is considered not to be suitable for the 2000's public project appraisal considering the present interest rates on deposit and national bonds. To determine the socio-economic feasibility of the public projects, the IRR should be bigger than the present cutoff rate. When we still use the high old rate of discount, the analytical results of project appraisal will show always economically unfeasible. Therefore the new rate of discount suitable for present time should be determined by the government. The public projects to be implemented in 2000 year onward are recommended to be adapted 5% of cutoff rate for the project appraisal and evaluation according to the results of reviewing the tendency of discount rates and market rates of interest in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.4
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• pp.35-44
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• 2018

The study is aimed at investigating the main factors that affect the interest rate yields, in the long-term. In addition, the study surveys the theories and literature relating to the determinants of interest rate. The importance of which is essential not only for governments, but also for banks and corporate financial risk management decisions, including risk exposures in banks and capital markets. Interest rate influences corporate profit as well as growth. For this purpose, the study examines the impact of budget deficit, risk-free rate, capital inflows, money supply and business cycles on real interest rate in Jordan. These factors are based upon well-established theories and straightforward practical view as interest rate determinants. Using data for (1990-2015), the study employed Johansen's co-integrating test, which takes into consideration the long-term unsynchronized relationships. The data is tested for normality, symmetric correlations, covariance diagonal and unit root. The results show that the government budget deficit, short-term risk-free interest rate, capital inflows, money supply and business cycle are long-term determinants of the real interest rate in Jordan. The coefficients of government budget deficit, short-term risk-free rate, money supply and business cycle all are inversely affecting the real interest rate, while capital inflows has a positive impact on the real interest rate.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.183-188
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• 2019

This study analyzed the effects of short-term interest rates, exchange rates and international oil prices on international grain prices using the EGARCH-GED model. The yield before one month of the international grain prices itself was found to have a significant effect on international grain prices for most periods. During the entire analysis period, none of the economic variables appeared to have a significant effect on international grain prices, whereas during the exchange fall period, only oil prices were shown to have a significant effect on international grain prices. In addition, during the pre-crisis period, interest rates, exchange rates and oil prices did not all have a significant effect, but during the post-crisis period only oil prices had a significant effect on international grain prices. It turns out that the factors affecting international grain prices are changing with the passage of time.

• Economic Analysis
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• v.27 no.1
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• pp.1-36
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• 2021

Given the US dollar's status as a global safe haven, global factors, such as US monetary policy, may have considerable impacts on financial markets in other countries. Regarding such hypothesis, this paper looked at the impacts of US monetary policy on domestic bond and FX swap markets through an event study. According to our analysis, US monetary policy had significant positive impacts on domestic interest rates. In particular, it turned out to have bigger impacts on long-term products with high term premiums. By period, the correlation between US monetary policy and domestic interest rates was not significant before the financial crisis, but was clearly positive after the crisis. The US conventional monetary policy was seen to have big impacts on short-term and medium-term KTB yields, while its unconventional monetary policy had major impacts on long-term KTB yields. Moreover, FX swap rates reacted very sensitively to US monetary policy shocks before the financial crisis, while they did not show any significant reactions after the crisis. This suggests that, in line with the covered interest rate parity, the impact of US monetary policy shocks was transmitted to domestic financial markets mainly through swap rate adjustments before the global financial crisis, but through the changes in domestic interest rates during the post-crisis period.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.174-180
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• 2021

The necessity of this study is as follows. A decrease in the number of newborns, an increase in the youth unemployment rate, and a decrease in the employment rate are having a fatal impact on universities. To help increase the employment rate of universities, we intend to utilize Big Data of university public information. Big data refers to the process of collecting and analyzing data, and includes all business processes of finding data, reprocessing information in an easy-to-understand manner, and selling information to people and institutions. Big data technology can be divided into technologies for storing, refining, analyzing, and predicting big data. The purpose of this study is to find the vision and special department of a university with a high employment rate by using big data technology. As a result of the study, big data was collected from 227 universities on www.academyinfo.go.kr site, We selected 130 meaningful universities and selected 25 universities with high employment rates and 25 universities with low employment rates. In conclusion, the university with a high employment rate can first be said to have a student-centered vision and university specialization. The reason is that, for universities with a high employment rate, the vision was to foster talents and specialize, whereas for universities with a low employment rate, regional bases took precedence. Second, universities with a high employment rate have a high interest in specialized departments. This is because, as a result of checking the presence or absence of a characterization plan, universities with a high employment rate were twice as high (21/7). Third, universities with high employment rates promote social needs and characterization. This is because the characteristic departments of universities with high employment rates are in the order of future technology and nursing and health, while universities with low employment rates promoted school-centered specialization in future technology and culture, tourism and art. In summary, universities with high employment rates showed high interest in student-centered vision and development of special departments for social needs.