• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.251-257
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• 2020

The study investigates the role of commodity prices and tax purpose recognition on bitcoin prices. Since the introduction of bitcoin in 2008, emphasis has focused on economists, policy-makers and analysts drastically increasing bitcoin's accessibility and commodity values (Dumitrescu & Firică, 2014). This study employs GARCH and EGARCH from ARCH/GARCH family on daily nature data. We measure the volatile behavior of bitcoin by employing auto-regressive conditional heteroscedasticity model with the aim to explore the relationship between major commodities and bitcoin volatility. We focus on major commodities like gold, silver, platinum, and crude oil to be regressed with bitcoin. The daily prices of commodities were retrieved from www.investing.com and bitcoin prices from www.coindesk.com for the period from 29April 2013 to 16 October 2018. Results confirmed the currency's long-term volatile behavior, which is due to its composition and market dynamics, whereas the existence of asymmetric information effect is not confirmed. Tax recognition by other countries may in future help in controlling the volatility as bitcoin is not a country-specific security. But, only silver impacts on volatility in comparison to oil prices and platinum, which is due to its similar features with gold. Eventually, bitcoin can be used for risk diversification and money making.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.197-202
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• 2021

Bitcoin and blockchain are often making headlines not only on TV or media but also among the public in today's society. These technologies have been developed after the risk of the centralized financial system came to the fore during the 2007 global financial crisis. Since then, an anonymous inventor called Satoshi Nakamoto penned the bitcoin white paper where a blockchain-based reference implementation was introduced. Bitcoin was able to achieve unprecedented growth by positioning itself as one of the top global currencies in terms of market capitalization after five years since its development. The pace of Vietnam's economic development is notably fast among Asian nations, while the nation was expected to be a Southeast Asian blockchain hub but they have banned virtual currency trading recently. However, they've also designated the State Bank of Vietnam (SBV) as a responsible agency for the research of blockchain-based cryptocurrencies, the construction of a service ecosystem, and their test operations. The fast-growing economy, increasing number of smartphone users, and the Vietnam government's support policies for startups substantiate these efforts. Therefore, this paper attempts to study the current status of Vietnam's blockchain technology that has been considered to be the center of blockchain systems right behind Singapore, and its implications for Korean companies.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.61
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• pp.273-302
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• 2014

Project finance ("PF") is a method of raising long-term debt financing based on lending against the cash flow generated by the project alone. Project finance is a nonrecourse or limited recourse financing structure against the sponsors(or the investors). The debt terms in a project finance are not based on the creditor's credit support or on the value of the assets of the project. Lenders rely on the future cash flow to be generated by the project for debt repayment and interest, rather than the value of the project or the credit ratings of the sponsors. The non-recourse or limited recourse financing usually prompt potential project finance lenders to assess carefully all possible risks that might arise in a project to ensure that those risks are mitigated and controlled. In this respect, project finance is a opposite financing method of corporate finance. Project finance has rapidly grown over the last 20 years due to the worldwide process of privatization of public sector and development of natural resources. Global project finance volume reached the record USD 406.5 billion in 2011. In 2012, however, Global project finance volume dropped 6% to USD 382.3 billion. Infrastructure overtook Energy to lead all sectors with USD 113.6 billion. It is generally recognized that there are more and higher risks in project finance compared with corporate finance. Project finance is exposed to commercial risks as well as political risks. The main commercial risks are completion risks, environmental risks, operating risks, input supply risks, revenue risks, etc, and the main political risks are currency convertibility and transfer risks, expropriation risks, war and civil disturbance risks, risks of breach of government concession agreement, etc. Completion risks include permits risks, risks relating to the EPC Contractor, construction cost overrun, delay in completion, inadequate performance on completion, etc.

• Journal of Korea Port Economic Association
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• v.27 no.3
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• pp.161-177
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• 2011

Nowadays, a capital flow and intimacy of financial system among countries have been increasing in global financial environment. So it is easily possible that the risk of some countries which are in financial crisis infects other countries in the world. A recent global financial crisis reminds countries in East Asia of advancing the financial cooperation as well as financial integration. Countries in East Asia agreed with the Chiang Mai Initiative to prevent a recurrence of financial crisis in East Asia. A bilateral swap arrangement of the CMI has several purposes in order to offer foreign currency liquidity against economic crisis, remove the opportunity cost of foreign exchange reserve, push ahead the financial integration, increase the export-import logistics and so on. This paper analyzes the effect of financial cooperation in East Asia on the export-import logistics with random effect estimation and fixed effect estimation. As a result, each of country in East Asia is able to increase almost 10.3% of the export-import logistics on average.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.577-582
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• 2021

This study was conducted to find ways to restore the domestic medical tourism industry, which was seriously hit by a sharp drop in foreign patients after the COVID-19 Pandemic. Kendall's W verification was used by asking expert panel for keyword advice by ranking. The conclusion of the study is that institutions attracting foreign patients need an opportunity to turn the crisis situation into an opportunity by expanding treatment for severe foreign patients. In addition, it is possible to gain familiarity and trust in hospitals in situations where it is difficult to visit overseas through virtual and augmented reality, and to prevent the risk of infection and protect patients in the untact era. In addition, the blockchain can maintain patient information supplementation, share it safely, minimize customer inconvenience by using payment means using virtual currency, and finally, smart healthcare can manage and provide information to patients regardless of location.

• Journal of Korea Trade
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• v.27 no.2
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• pp.96-114
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• 2023

Purpose - This paper elucidates a nexus between the occurrence of rare disaster events and the volatility of economic growth by distinguishing the likelihood of rare events from stochastic volatility. We provide new empirical facts based on a quarterly time series. In particular, we focus on the role of financial liberalization in spreading the economic crisis in developing countries. Design/methodology - We use quarterly data on consumption expenditure (real per capita consumption) from 44 countries, including advanced and developing countries, ending in the fourth quarter of 2020. We estimate the likelihood of rare event occurrences and stochastic volatility for countries using the Bayesian Markov chain Monte Carlo (MCMC) method developed by Barro and Jin (2021). We present our estimation results for the relationship between rare disaster events, stochastic volatility, and growth volatility. Findings - We find the global common disaster event, the COVID-19 pandemic, and thirteen country-specific disaster events. Consumption falls by about 7% on average in the first quarter of a disaster and by 4% in the long run. The occurrence of rare disaster events and the volatility of gross domestic product (GDP) growth are positively correlated (4.8%), whereas the rare events and GDP growth rate are negatively correlated (-12.1%). In particular, financial liberalization has played an important role in exacerbating the adverse impact of both rare disasters and financial market instability on growth volatility. Several case studies, including the case of South Korea, provide insights into the cause of major financial crises in small open developing countries, including the Asian currency crisis of 1998. Originality/value - This paper presents new empirical facts on the relationship between the occurrence of rare disaster events (or stochastic volatility) and growth volatility. Increasing data frequency allows for greater accuracy in assessing a country's specific risk. Our findings suggest that financial market and institutional stability can be vital for buffering against rare disaster shocks. It is necessary to preemptively strengthen the foundation for financial stability in developing countries and increase the quality of the information provided to markets.

• KDI Journal of Economic Policy
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• v.36 no.2
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• pp.103-136
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• 2014

The covered interest rate parity condition (CIRP) has been widely used in open macroeconomic analysis, risk management, exchange rate forecasts, and so forth. Due to the recent global financial crises, there have been remarkable changes in the financial markets of the emerging markets. These changes possibly influenced the dynamics of the covered interest rate parity condition. In this paper, we investigate whether the CIRP dynamics has changed, and what is the nature of the regime changes. To do this, we propose and estimate multiple-state Markov regime switching models using a Bayesian MCMC method. Our estimation results indicate that the default risk or the deviation from the CIRP has been decreased after the crisis. It seems to be associated with the more active interaction between the short-term bond market and the short-term foreign exchange market than before. The tightened relation of these two financial markets is caused by the arbitrage transaction of foreign investors.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.53-78
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• 2008

Foreign investors who invest in the Korean stock markets are exposed to two kinds of foreign exchange rate risk, the economic exposure and the translation exposure. The former is the foreign exchange rate exposure in return generating process of the assets invested and the latter is the foreign exchange rate exposure in the translation of domestic return into foreign investors' currency. Domestic investors, however, are exposed only to foreign exchange rate exposure in the asset invested. This different situation on foreign exchange rate exposure between foreign investors and domestic investors can induce different response to exchange rate change by investor groups. Previous studies on foreign exchange rate exposure of Korean firms reported that quite a few Korean firms are exposed to foreign exchange risks and suggested to manage the foreign exchange risks. Also, many studies on the market segmentation showed that a market can be practically segmented according to the characteristics of investor groups. These studies support the hypothesis that the Korean stock market can be practically segmented by the foreign investors' attitude to the foreign exchange rate exposure. This study examines the response of both foreign investors and domestic investors to the foreign exchange rate exposures in Korean stock markets. Test results show that foreign investors increase their sell transactions when the foreign exchange rate exposure of the previous day is negative. This result can be possible when foreign investors attempt to actively manage the decrease in value of their assets due to rising of exchange rate. Analysis on the sell order data is also supportive to this interpretation. Foreign investors also increase their buy transactions when the foreign exchange rate exposure of the previous day is negative. This result can be possible when foreign investors use actively the relation between the increase in asset value and the translation gain due to declining of exchange rate. Analyses on buy order data, however, do not show the same result as the analyses on transaction data. This difference may come from the difference of information contained in transaction data and order data. In summary, the result of the paper supports the hypothesis that foreign investors response differently to foreign exchange rate exposure compared with domestic, Korean investors. Two groups do not show different response when exchange rate exposure is positive, i.e., as foreign exchange rate is increase (decrease), the asset value is increase (decrease). However, foreign investors' response is different from that of domestic investors when exchange rate exposure is negative, i.e., as foreign exchange rate is increase (decrease), the asset value is decrease (increase). These results mean that foreign investors and domestic investors are placed in different situations related to foreign exchange rate exposure, and these differences are reflected in the Korean stock markets. And domestic investors need to consider foreign investors' different attitude to the foreign exchange rate exposure when they analysis foreign investors' trading behavior.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1419-1429
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• 2010

Industry structure and environment of the domestic bank have been changed by an influx of large foreign-banks and advanced financial products when the currency crisis erupted in Korea. In a competitive environment, accurate forecasts of changes and tendencies are essential for the survival and development. Forecast of whether to approve loan applications for customer or not is an important matter because that is related to profit generation and risk management on the bank. Therefore, this paper proposes the method to improve forecast accuracy of loan underwriting. Processes in experiments are as follows. First, we select the predictor variables which affect significantly to the result of loan underwriting by correlation analysis and feature selection technique, and then cluster the customers by the 2-Step clustering technique based on selected variables. Second, we find the most accurate forecasting model for each clustering by applying LR, NN and SVM. Finally, we compare the forecasting accuracy of the proposed method with the forecasting accuracy of existing application way.