• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.15 no.4
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• pp.277-290
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• 2011

We define some multi-type asset models derved from L$\acute{e}$vy proceses which emphasize coefficients of stochastic differential equations. Also these asset models can be represented by Doleance-Dade linear equations derived from jump-type semimartingales which are decomposed by various terms of time basically. For these asset models, we can construct optimal portfolio strategy by using filtered various information at each check time.

• Journal of the Korean Home Economics Association
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• v.30 no.1
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• pp.219-235
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• 1992

This study attempted to examine a theoretical framework which synthesizes risk attitude, type of investment management and investment behavior and to provide the specific investment strategy by analysing several variables which have effect upon the investment behavior. The results of this research were as follow : 1. Risk attitude had significant differences by the variabels such as age, sex, education, income and debt/asset ratio. Also the type of investment management was influenced significantly by the variables such as age, education, occupation, income, total asset, debt/asset ratio, achievement motivation and risk attitude. The ratio of risky asset holdings was affected by the variabels such as age, education, occupation, housing ownership, income, total asset, debt/asset ratio, achievement motivation, risk attitude and type of investment management. 2. Among several variables affecting the ratio of risky asset holding risk attitude, education, type of investment management, debt/asset ratio and achievement motivation had direct effect on it. Besides age had indirect effect through risk attitude and age, achievement motivation and risk attitude had indirect effect through the type of investment management. 3. The results of this study showed that causal relation between input, throughput and output can be applied to household's investment behavior and the concept of risk or risk attitude can be applied to other fields except household's investment. Also it could be attributed to provide the investment strategy for improving level of household's financial well-being.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.79-85
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• 2005

In this paper, we dealt with substantial asset analysis methodology applied to two-dimensional asset classification and qualitative evaluation method according to the business process. Most of the existent risk analysis methodology and tools presented classification by asset type and physical evaluation by a quantitative method. We focused our research on qualitative evaluation with 2-dimensional asset classification. It converts from quantitative asset value with purchase cost, recovery and exchange cost, etc. to qualitative evaluation considering specific factors related to the business process. In the first phase, we classified the IT assets into tangible and intangible assets, including human and information data asset, and evaluated their value. Then, we converted the quantitative asset value to the qualitative asset value using a conversion standard table. In the second phase, we reclassified the assets using 2-dimensional classification factors reflecting the business process, and applied weight to the first evaluation results. This method is to consider the organization characteristics, IT asset structure scheme and business process. Therefore, we can evaluate the concrete and substantial asset value corresponding to the organization business process, even if they are the same asset type.

• Korean Journal of Mathematics
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• v.16 no.2
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• pp.233-242
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• 2008

We deal with the closed forms of European option pricing for the general class of volatility asset model and the jump-type volatility asset model by several methods.

• The Korean Journal of Community Living Science
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• v.26 no.1
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• pp.191-206
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• 2015

This study investigates the differences in retirement preparation and asset type in the elderly life of employees in their forties and fifties. Retirement preparation showed a score of 3.30, and the emotional preparation dimension of retirement preparations showed a slightly higher score. The retention of guarantee assets(insurance and, national pension) scored higher than other types of assets. There was a high correlation between economic preparation and the type of asset retention. According to the hierarchical regression analysis, economic preparation, monthly income, home ownership, and the education level had significant effects on assets retention, and the explanatory power of these independent variables was 39.7%.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.2
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• pp.87-100
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• 2009

Hedging strategy for European option of jump-type semimartingale asset model, which is derived from stochastic differential equation whose driving process is a jump-type semimartingle, is discussed.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.9 no.1
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• pp.31-53
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• 2005

We analyze some real data and, from the background of analysis of data, we define a multi-dimensional jump-type asset model which is derived from short-sampling asset prices. We study some basic properties of this asset model.

• The Journal of Industrial Distribution & Business
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• v.12 no.2
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• pp.41-52
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• 2021

Purpose: This study aims to provide implications for the government's housing supply policy by analyzing the factors that determine the type of real estate holding and household debt. This study started from the awareness that the determinants of household debt differ depending on the type of real estate holding. Research design, data and methodology: Real estate ownership type was classified and analyzed into 4 models: model 1 (1 household 1 house and self-resident), model 2 (1 household multiple real estate ownership and self-resident), model 3 (1 household 1 house and rent residence), model 4 (1 household holds a large number of real estate and rent residence). The analysis method used multiple regression analysis. The dependent variable was household total debt. As independent variables, household debt, annual gross household income, financial assets, real estate net assets, annual repayment, demographic & residential characteristics were used. Results: 1) Model 4 has the highest household debt and the highest gross income, Model 2 has the most real estate mortgage loans and real estate net asset, and Model 1 has the highest real estate mortgage payments. 2) The positive factor of common household debt determinants is real estate net assets, and the negative factor is financial assets. 3) It was the net assets of real estate that acted as a positive factor in common for the four models. In other words, the more financial assets, the less household debt. It was analyzed that the more net assets of real estate, the more household debt. The annual repayment of financial liabilities had no influence on household debt, while the annual repayment of loan liabilities and household debt had a positive relationship. Conclusions: 1) It is necessary to introduce benefits and systems that can increase the proportion of household financial asset. Specific alternatives include tax benefits and reduced fees for financial asset investment. 2) In the case where a homeless person prepares one house for one household, it is necessary to prepare various support measures according to the income level. The specific alternative is to give additional points for pre-sale or apply an interest rate cut incentive for mortgage loans.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.4
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• pp.211-224
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• 2010

We define some asset models which are useful for portfolio construction in various terms of time. Our asset models are geometric jump-diffusions defined by the solutions of stochastic differential equations which are decomposed by various terms of time basically. We also can study pricing and hedging strategy of options in our models roughly.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.81-86
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• 2001

In this paper, we investigated the modeling of Bank Asset Management System(BAME) based on intelligent agent. To achieve this goal, we introduced several kinds of agents that show intelligent features. BAMS is a user friendly system and adopts fuzzy converting system and fuzzy matching system that returns reasonable similarity matching results. Generation function of the proximity degree is suggested. Fuzzification of investment type categories and feature values are defined, and generation of proximity degree is also derived. An example of bank asset management system is introduced and simulated. Investment type matching utilizing fuzzy measure is tested and it showed quite reasonable similarity matching results.