Trade credit is being used as a price discrimination strategy by the suppliers in order to increase the customer's demand. From the viewpoint of the customer, if delayed payment is allowed for a certain period of time from the supplier, the effect of reducing the inventory carrying cost will positively affect the customer's order quantity. Also, in deriving the economic order quantity(EOQ) formula, it is tacitly assumed that the customer's ordering cost is a fixed cost. However in many business transactions, the customer pays the freight cost for the transportation of his order and so, the customer's ordering cost contains not only a fixed cost but also a freight cost which is a function of the order size. Therefore, in this study, we analyzed the inventory model which considers that the customer's ordering cost contains not only a fixed cost but also a freight cost which is a function of the customer's order size when the supplier permits a delay in payments. For the analysis, it is also assumed that inventory is exhausted not only by customer's demand but also by deterioration. Investigation of the properties of an optimal solution allows us to develop an algorithm whose validity is illustrated using an example problem.
This study analyzes the dynamic characteristics of cargo volume (demand), ship fleet (supply), and freight rate (price) of container, dry bulk, and tanker shipping markets by using the VAR and VECM models. This analysis is expected to enhance the statistical understanding of market dynamics, which is perceived by the actual experiences of market participants. The common statistical patterns, which are all shown in the three shipping markets, are as follows: 1) The Granger-causality test reveals that the past increase of fleet variable induces the present decrease of freight rate variable. 2) The impulse-response analysis shows that cargo shock increases the freight rate but fleet shock decreases the freight rate. 3) Among the three cargo, fleet, and freight rate shocks, the freight rate shock is overwhelmingly largest. 4) The comparison of adjR2 reveals that the fleet variable is most explained by the endogenous variables, i.e., cargo, fleet, and freight rate in each of shipping markets. 5) The estimation of co-integrating vectors shows that the increase of cargo increases the freight rate but the increase of fleet decreases the freight rate. 6) The estimation of adjustment speed demonstrates that the past-period positive deviation from the long-run equilibrium freight rate induces the decrease of present freight rate.
Overview of Research: Product availability is one of important competences of store to fulfill consumer needs. If stock-outs which means a product what consumer wants to buy is not available occurs, consumer will face decision-making uncertainty that leads to consumer's negative responses such as consumer dissatisfaction on store. Stockouts was much studied in the field of academia as well as practice in other countries. However, stock-outs has not been researched at all in Marketing and/or Distribution area in Korea. The main objectives of this study are to find out determinants of consumer responses such as Substitute, Delay, and Leave(SDL) when consumer encounters out-of-stock situation and then to examine the effects of these factors on consumer responses. Specifically, this study focuses on situational characteristics(e.g., purchase urgency and surprise), store characteristics (e.g., product assortment and store convenience), and consumer characteristics (e.g., brand loyalty and store loyalty). Then, this study empirically investigates relationships these factors with consumers behaviors such as product substitution, purchase delay, and store switching.
This paper develops a theory of the demand for insurance. The present model incorporates insurance demand time value of insurance premium, and demand for listless and risky assets simultaneously within the expected utility framework. For a special case of CARA, an insurance decision can be made separately from other portfolio decisions. However, in general, the interactions of both decisions cannot be ignored even when insurable and speculative risks are stochastically independent. In particular, the role of risky investment in hedging insurable risk is demonstrated and it is shown that this role cannot be duplicated by an insurance contract. When the investment decision is made simultaneously with the insurance decision, some of the classic theory on insurance should be modified. As an example, the authors characterize the sufficient conditions, under which the Bernoulli criteria (without and with premium loadings) hold or are violated in terms of the net gain of risky investment, the net cost of insurance, and the stochastic relationship between insurable and speculative risks. The authors interpret the results using the Rothschild and Stiglitz's (1970) notion of 'increase in riskiness'.
This study aims to analyze the factors affecting the re-subscription period upon initial termination of the reverse mortgage subscription. The study utilized the Korea Housing Finance Corporation's database to extract the information regarding re-subscribers of the reverse mortgage from July 2007 to June 2021. The ordered logit model was employed and found that a set of user (subscriber) characteristics are influential towards the re-subscription period. Among the individual characteristics, changes in age group, marital status from married to single-living, maintaining single-living, and the initial subscription period were found statistically significant, highlighting that the increase in the initial subscription period decreased the re-subscription period. Among the housing (home equity) characteristics, changes in housing price and ownership type (single and partial ownership) were statistically significant, indicating that the change in ownership type decreases the re-subscription period. Lastly, the variables related to loan terms were found significant, revealing that changes in payout method and schedule were both increasing factors of the re-subscription period. Based on the findings, necessary policy implications can be considered to secure the returning subscribers of the reverse mortgage effectively.
The container shipping sector is an important international logistics operation that connects open economies. Freight rates rapidly change as the market fluctuates, and staff related to the shipping market are interested in factors that determine freight rates in the container market. This study uses the Vector Error Correction Model(VECM) to estimate the impact of factors affecting container freight rates. This study uses data published by Clarksons. The analysis results show a 4.2% increase in freight rates when world container traffic increases at 1.0%, a 4.0% decrease in freight rates when volume of container carriers increases by 1.0%, a 0.07% increase in freight rates when bunker price increases by 1.0%, and a 0.04% increase in freight rates accompanying 1.0% increase in libor interests rates. In addition, if the current freight rate is 1.0% higher than the long-term equilibrium rate, the freight rate will be reduced by 3.2% in the subsequent term. In addition, if the current freight rate is 1.0% lower than the long-term equilibrium rate, the freight rate will decrease by 0.12% in the following term. However, the adjusting power in a period of recession is not statistically significant which means that the pressure of freight rate increase in this case is neglectable. This research is expected to contribute to the utilization of scientific methods in forecasting container freight rates.
The BTO-a projects is the types, which has a demand risk among the type of PPP projects in Korea. When demand risk is realized, private investor encounters financial difficulties due to lower revenue than its expectation and the government may also have a problem in stable infrastructure operation. In this regards, the government has applied various risk sharing policies in response to demand risk. However, the amount of government's risk sharing is the government's contingent liabilities as a result of demand uncertainty, and it fails to be quantified by the conventional NPV method of expressing in the text of the concession agreement. The purpose of this study is to estimate the value of investment risk sharing by the government considering the demand risk in the profit sharing system (BTO-a) introduced in 2015 as one of the demand risk sharing policy. The investment risk sharing will take the form of options in finance. Private investors have the right to claim subsidies from the government when their revenue declines, while the government has the obligation to pay subsidies under certain conditions. In this study, we have established a methodology for estimating the value of investment risk sharing by using the Black - Scholes option pricing model and examined the appropriateness of the results through case studies. As a result of the analysis, the value of investment risk sharing is estimated to be 12 billion won, which is about 4% of the investment cost of the private investment. In other words, it can be seen that the government will invest 12 billion won in financial support by sharing the investment risk. The option value when assuming the traffic volume risk as a random variable from the case studies is derived as an average of 12.2 billion won and a standard deviation of 3.67 billion won. As a result of the cumulative distribution, the option value of the 90% probability interval will be determined within the range of 6.9 to 18.8 billion won. The method proposed in this study is expected to help government and private investors understand the better risk analysis and economic value of better for investment risk sharing under the uncertainty of future demand.
Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.
본 논문에서는 최근 통신 산업에서 중요한 쟁점으로 떠오르고 있는 단말기 무선이동 통신 단말기의 발전 방향을 소비자 선호에 기반하여 살펴보았다. 소비자 선호 정보를 얻기 위하여 컨조인트(conjoint) 분석 방법이 사용되었다. 컨조인트 방법은 가상의 대안들에 대한 응답자의 진술 선호에 기반을 두기 때문에 미래의 무선 이동통신 단말기에 대해 분석하는데 적합한 방법이다. 컨조인트 방법을 위한 설문은 대한민국 서울에서 445 명의 성인남녀를 대상으로 행해졌다. 소비자의 이질적인 선호를 알기 위해 혼합 로짓(mixed legit) 모형을 사용하였다. 추정은 최근 새로운 시뮬레이션 기법으로 떠오르고 있는 베이지안(Bayesian) 방법을 이용하였다. 선호의 분포 가정으로 기존의 일관적인 정규 분포 가정과 달리 가격 계수를 위하여 로그 정규(lognormal) 분포, 하이퀄리티 인터넷 특성과 PC 프로그램 호환 가능 여부의 계수들에 대해서 잘린 정규(censored normal) 분포를 가정 하였다. 추정 결과 무선 이동 통신 단말기의 각 속성들에 대한 응답자들간 선호가 크게 차이 나는 것을 알 수 있었다. 화면 크기의 경우에는 대부분의 소비자들이 현재 일반적인 핸드폰보다는 큰 화면을 선호한다는 것과 휴대성을 상당히 고려한다는 것을 간접적으로 알 수 있었다. 또한, 소비자들이 무선 이동 통신 단말기가 휴대 인터넷과 PC 프로그램 호환이 가능한지 여부에는 대부분 무관심하다는 것을 알 수 있었다. 본 논문의 결과는 차세대 무선 이동 통신 단말기의 속성 조합 시 고려해야 할 점과 휴대 인터넷 서비스의 방향에 대해 시사점을 줄 수 있을 것이다.각 73.44±0.87%, 72.88±0.25%의 함량이였다. 운동사육시간이 길어질수록 운동사육구에서는 수분함량이 운동5일째에는 73.56±0.22%였으며, 운동 20일에는 75.88±0.94%로 초기수분함량보다는 3%정도 증가하였다. 반면, 비운동사육구에서는 큰 변화를 나타내고 있지 않았다(p<0.05). 운동과 비운동시킨 참돔의 지질 함량의 변화는 운동시킨 참돔은 운동으로 인한 에너지 소비로 인하여 함량이 유의적으로 감소했으며(r=-0.35), 비운동사육구에서는 절식으로 인하여 지질함량이 감소하였다(r=-0.38). 파괴강도와 가장 밀접한 영향을 가지는 콜라겐은 운동과 비운동 모두 사육기간동안 큰 변화는 보이지 않았다. 초기의 파괴강도값은 1.45±0.02kg(운동사육구), 1.36±0.18kg(비운동사육구)이였으며 사육기간동안 운동사육구는 파괴강도값이 증가한 반면, 비운동수조에서는 참돔의 파괴강도는 사육기간동안 큰 유의차가 없었다. 각 성분간의 상관도를 살펴보면, 수분함량과 파괴강도는 상관성을 가졌으며, 지질함량과 파괴강도도 같은 경향은 나타내었다. 운동기간동안의 파괴강도와 콜라겐 사이에는 상관성의 거의 없었다. 이는 운동기간에 따른 파괴강도의 증가가 콜라겐의 함량의 증가보다는 지질함량의 감소와 수분함량의 증가와 같은 성분과의 상관성이 크다고 판단된다. 다음으로는, 운동횟수에 의한 영향으로써 운동시간을 1일 6시간으로 설정하여, 운동횟수를 결정하기 위하여 오전, 오후에 각 3시간씩 운동시키는 방법과 오전부터 6시간동안 운동시키는 두 방법을 이용하여 품질을 비교하였다. 각 조건에 따라 운동시킨 참돔의 수분함량을 나타낸 것으로, 2회(오전
In this paper, the purpose is to verify the impact of performance expectations, effort expectations, social impact, individual innovation and perceived value on the intent of use and the behavior of use. Used Unified Theory of Acceptance and Use of Technology (UTAUT) to verify the applicability of this model in China, and established the research model by adding two new variables to UTAUT according to the situation of the Chinese market. To achieve this goal, 345 questionnaires were collected for experienced music creators using artificial intelligence nuggets in China by means of Internet research. The collected data were analyzed through frequency analysis, factor analysis, reliability analysis, and structural equation analysis through SPSS V. 22.0 and AMOS V 22.0. The verification of the hypotheses presented in the research model identified the decisive influence factors on the use of artificial intelligence music acceptance by Chinese users. The study is innovative in that it attempts to verify the applicability of UTAUT in the Chinese context. In the construction of the user acceptance model of AI music, three influencing factors will have an effect on users' intentions, and according to the degree of effect, from largest to smallest, they are respectively Perceived Innovativeness, Performance Expectancy and Effort Expectancy. This paper will also provide some management advices, i.e. improving the utility and usability of AI music, encouraging users with individual innovativeness, developing competitive and attractive pricing policies, increasing publicity, and prioritizing word-of-mouth advertising.
shows the research model of this study. To accomplish above-mentioned research objectives, the following ten hypotheses were proposed and verified : is the estimation results of l\1NL model, and
shows the marginal effects for each determinant to consumer's responses(SDL). Significant statistical results were as follows. Purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty were turned out to be significant determinants to influence consumer alternative behaviors in case of out-of-stock situation. Specifically, first, product substitution behavior was triggered by purchase urgency, surprise, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Second, purchase delay behavior was led by purchase urgency, purchase quantities, and brand loyalty. Third, store switching behavior was influenced by purchase urgency, purchase quantities, pre-purchase plan, product assortment, store price image, brand loyalty, and store loyalty. Finally, when out-of-stock situation occurs, store convenience and salesperson service did not have significant effects on consumer alternative responses.
A Financial Theory of the Demand for Insurance With Simultaneous Investment Opportunities
(투자(投資)와 보험수요(保險需要)의 상관관계(相關關係)에 관한 재무경제학적(財務經濟學的) 연구(硏究))
Determinants of Re-Subscription Period of Early Termination Subscribers of Reverse Mortgage
(주택연금 중도해지자의 재가입 소요기간 결정요인 분석)
Analysis of Factors Affecting on the Freight Rate of Container Carriers
(컨테이너 운임에 미치는 영향요인 분석)
Real Option Analysis to Value Government Risk Share Liability in BTO-a Projects
(손익공유형 민간투자사업의 투자위험분담 가치 산정)
Estimation of GARCH Models and Performance Analysis of Volatility Trading System using Support Vector Regression
(Support Vector Regression을 이용한 GARCH 모형의 추정과 투자전략의 성과분석)
Demand analysis on new Mobile Telecommunication Terminal using Conjoint analysis and Mixed logit
(컨조인트 분석과 혼합 로짓 모형을 이용한 차세대 무선 이동 통신 단말기의 수요 분석)
A Study on the User Acceptance Model of Artificial Intelligence Music Based on UTAUT
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