• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.1-13
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• 2021

Previous studies have found the significant adverse effects of coronavirus disease 2019 (COVID-19) on stock returns and volatility. The effects varied with the confirmed cases and deaths. However, the extent of the effects have never been measured exactly. This study proposes a measurement model for the COVID-19 effects. In the proposed model, stock returns in the COVID-19 period are weighted averages of pre-COVID-19 normal returns and COVID-19-induced returns. The effects are measured by the contributing weights of the COVID-19-induced returns. Kalman filtering is used to estimate the model for the world and Chinese markets, in combination with 10 markets - five most affected countries (United States, India, Brazil, Russia, and France) and five best recovering countries (Hong Kong, Australia, Singapore, Thailand, and South Korea). The sample returns are daily, obtained from the closing Morgan Stanley global investable market indexes. The full period is from September 24, 2018, to October 30, 2020, whereas the COVID-19 period is from November 18, 2019, to October 30, 2020. The contributing weights are significant and close to 100% for all markets. The COVID-19-induced returns replace the pre-COVID-19 normal returns; they are negatively auto-correlated and highly volatile. The COVID-19-induced returns are new normal returns in the COVID-19 period.

• The Journal of Asian Finance, Economics and Business
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• 제6권3호
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• pp.103-113
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• 2019

The effectiveness of monetary policy critically depends upon how well the transmission mechanism functions, so that the desired impact on output and inflation is achieved. The purpose of this paper is to study the transmission mechanism of monetary policy by analyzing the impact on inflation and output during multiple indicator regime (1998-99 to 2014) in an emerging economy-India. The Inflation Targeting Regime is also briefly outlined alongwith the impact on output and inflation. Using quarterly data for the period 1997 to 2017, the paper uses weighted average call money market rate as a proxy for the policy rate and evaluates the strength of the interest rate channel. We use a conventional Structural vector auto regression (SVAR) methodology to evaluate the efficacy and show the impluse response functions. Our results find that changes in the policy rate impact output growth steeply with a lag of about two quarters and the impact on inflation is maximized after three quarters. The study concludes that the monetary policy in India has a significant impact on output and inflation in the short-to-medium-run. After the policy shock, the fall in the output growth rate is of greater magnitude than the fall in inflation.

• Communications for Statistical Applications and Methods
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• 제30권4호
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• 문화기술의 융합
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• 제10권4호
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• pp.563-574
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• 2024

금융 서비스 앱에서 챗봇의 도입과 전문용어 풀어 쓰기가 일반화되고 있으나, 자동차 구매자들을 위한 금융 앱은 여전히 용어 이해가 어렵고 내용이 딱딱하여 만족도가 낮은 상태이다. 이 연구는 자동차 금융 앱에서 대화형 챗봇 인터랙션 모드와 전문 용어 풀어쓰기 방식이 사용자 경험에 미치는 효과를 검증하였다. 사용자 인터랙션 채널 유형(챗봇 vs 일반)과 전문 용어 사용 여부를 조건으로 자동차 리스 견적 태스크에 대한 프로토타입을 제작하고, 2 x 2 실험 조사를 수행하여 친근감, 이해 용이성, 신뢰, 정확성 인식의 차이를 측정하였다. 그 결과, 대화형 인터랙션 기반의 챗봇 모드는 메뉴 인터랙션 기반의 일반 모드에 비해 친근감이 더 높아지는 효과를 보였으며, 전문 용어 풀어쓰기는 이해 용이성과 친근감에 유의미한 영향을 미쳤다. 그러나, 신뢰와 정확성에는 두 조건 모두 차이가 나타나지 않았다. 또한, 모든 변인에서 두 조건 간 상호작용 효과는 발견되지 않았다. 이 연구의 의의는 챗봇 상담 모드의 고객 경험 효과와 금융 서비스 특성에 따른 전문용어 풀어쓰기 효과에 대한 정량적 검증과 실무적 제안을 제시한 점이다.

• 무역학회지
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• 제44권3호
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• pp.271-287
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• 2019

This paper reviews and analyzes the ethical issues of multinational corporations (MNCs) in Africa. First, we find that the transparency and institutions of a host country have a negative relationship with the number of ethical violations of the MNCs. Second, this covers the effects of industry characteristics on each category of ethical issues such as the human rights and the environment. Based on the database of "Ethical Consumer", we show that the Auto, Chemical, Finance, and Telecommunication industries are more likely to violate human rights issues, and that Mining, Oil, Cosmetics, and Chemical industries are more likely to pollute the environments. Further, the country of origin does matter: the US and Asian companies are more likely to be involved with the business ethics violations than are their European counterparts.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.677-682
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• 2020

To investigate the nexus between urban population, gross capital formation, and economic growth in the Kingdom of Saudi Arabia, yearly data was collected from the World Bank for the period 1974- 2018. Basic statistics test and correlation matrix was used to investigate the causal effect among the tested parameters, followed by Augmented Dickey-Fuller (ADF) stationary test, co-integration analysis by Johansen test after that Vector Auto-Correction Model for both short-run and long-run and finally the Granger-Causality tests. Result of unit root test analysis shows that the urban population became stationary at I (0) level while economic growth and gross capital formation became stationary at I (1). Johansen co-integration analysis indicates that there is presence of both long-run and short-run relationship between the three variables in the Kingdom of Saudi Arabia. The result of the VECM Model reflects that both economic growth and gross capital formation have a negative impact on urban population in the short run. According to the Granger-Causality tests, there is unidirectional causality with the urban population by both gross capital formation and economic growth. Also, the result of the Granger Causality tests show that there is unidirectional causality between economic growth and gross capital formations.

• The Journal of Asian Finance, Economics and Business
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• 제7권9호
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• pp.191-200
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• 2020

This research paper examines the causal relationship between India's economic growth and sectoral contribution to Gross Domestic Product (GDP) and vice versa, in the short-run and long-run, over a 10 years time period. Johansen's method of cointegration is used to study the cointegration between the sectoral contributions to Indian GDP vis-à-vis India's economic growth. Further, the route of interconnection between economic growth and sectoral contribution is tested by using Vector Auto Regression (VAR) model. Special attention was given for investigating impulse responses of economic growth depending on the innovations in sectoral contribution using time-series data from 1960 to 2015. This paper highlighted a dynamic co-relationship among industrial sector contribution and agricultural sector contribution and economic development. In the long run, one percent change in industrial sector contribution causes an increase of 3.42 percent in the economic growth and an increase of 1.12 percent in the primary sector contribution, while in the short run industrial and service sector contributions showed significant impact on economic development and agriculture sector. The changing composition of sector contribution is going to be an important activity for the policymakers to monitor and control where the technology and integration of sectors play a significant role in economic development.

• 산경연구논집
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• 제8권1호
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• pp.5-13
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• 2017

Purpose - In this article, a dynamic model like a VAR is an appropriate choice for estimating the possible interrelationship between ownership structure and firm performance as a dynamic process. Research design, data, and methodology - Data of this work are collected from Chinese stock exchange including 350 Chinese-listed firms during the period of 1999-2012. We hypothesize that this interrelationship dynamically exists between ownership structure and firm performance. To examine the correlation, a panel Vector Auto-regression (PVAR) approach generated by GMM method is utilized to test the possible dynamic relation embedded in corporate governance. Another two dynamic analysis solutions such as orthogonalized impulse-response function and variance decomposition are also used simultaneously. Results - Findings of this study indicate the evidence that dynamically endogenous relationship exists between ownership structure and firm performance. Further, there is a dynamical correlation between investment and performance. Impulse response and variance decomposition illustrate that impact of a shock to variables themselves is the main source for their variability. Conclusions - The conclusion in this study is that there is a bidirectional and inter-temporal effect between proportion of ownership and corporate performance for a long run in accordance with impulse response function. Overall, our results suggest that corporate governance in China is more market oriented.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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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.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.37-41
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• 2021

This study aims to investigate the relationship between the exchange rate and Trade Balance. Trade Balance is used as the dependent variable, and the independent variables are Exchange Rate, Gross Domestic Product, and Inflation. Augmented Dickey-Fuller unit root test was adopted to test the stationary property of time series data, Auto Regressive Distributed Lag model was employed to find the long run and short-run relationship and long-run adjustment, Bound test approach, the unrestricted Error Correction Model and Granger Causality Test are used to analyze the data from 1977 to 2019. The research findings suggest that inflation has a positive impact on the trade balance in the short run. The exchange rate and the Gross Domestic Product have adverse effects on Trade balance in the long run. The coefficient of ER in the previous year is negative, and the coefficient of TB in the previous year is positive and significant. This is consistent with the J-Curve phenomenon, which states that devaluation may not improve trade balance in the immediate period, but will significantly impact the trade balance improvement in subsequent periods. Hence Marshall Lerner Condition exists in Sri Lanka.