• Journal of the Economic Geographical Society of Korea
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• v.13 no.2
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• pp.234-252
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• 2010

This paper examines the tendency of housing assets to become increasingly quasi-financial assets by analyzing the relationships between risks and returns in three Gangnam districts (Gangnam-gu, Seocho-gu and Songpa-gu) apartment markets in Seoul, especially for the apartments to be reconstructed, capitalizing upon some capital asset pricing models (CAPM). A single factor CAPM model shows positive relationships between risks and returns regardless of the types of apartments in three Gangnam districts. Multi-factors CAPM models also confirm that the market and SMB (small minus big) factors are positively related to the rate of returns regardless of the types of apartments. However, the unsystematic risk factor is found to be statistically positive especially for the apartments to be reconstructed, while the momentum factor is dependent upon the regression models used. An analysis on some portfolios classified by the size of apartments and price volatility and/or beta values suggests that there are the positive linear relationships between risks and returns and the SMB factor is clearly found to be significant in determining the rate of returns. In particular, housing assets are highly highlighted as investment goods and/or quasi financial assets for the apartments to be constructed in the Gangnam housing.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.33-40
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• 2020

The main aim of the study is to test a house pricing model by combining hedonic and asset-based pricing models. An understanding of the relationship between house pricing and its return (the rental income) helps to establish houses as a significant asset class. The model tested the relationship between house pricing (dependent variable) and the house attributes (independent variables) derived from Freeman's framework of housing attributes. This study uses a large data-set of 1,899 sample of new, high-end houses purchased between 2016 and 2019 collected from the national capital region of India (Delhi-NCR). The algorithm was built in R-Script, and stepwise multiple linear regression was used to analyze the model. The analysis of the model proves that the three significant variables, namely, carpet area, pay-off, and annual maintenance charges explain the price function. Further, the model is statistically fit. The major contribution of the study is to understand the key factors and their influence on the house pricing. The model will be helpful in risk assessment in the housing investment and enhance the chances of investment. Policy-makers can use information about the underlying valuation drivers of the house prices to stabilize the market and also in framing the tax policies.

• The Journal of Asian Finance, Economics and Business
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• v.5 no.3
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• pp.19-29
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• 2018

This research examined the alternatives of Jensen's alpha (α) estimation models in the Capital Asset Pricing Model, discussed by Treynor (1961), Sharpe (1964), and Lintner (1965), using the robust maximum likelihood type m-estimator (MM estimator) and Bayes estimator with conjugate prior. According to finance literature and practices, alpha has often been estimated using ordinary least square (OLS) regression method and monthly return data set. A sample of 50 securities is randomly selected from the list of the S&P 500 index. Their daily and monthly returns were collected over a period of the last five years. This research showed that the robust MM estimator performed well better than the OLS and Bayes estimators in terms of efficiency. The Bayes estimator did not perform better than the OLS estimator as expected. Interestingly, we also found that daily return data set would give more accurate alpha estimation than monthly return data set in all three MM, OLS, and Bayes estimators. We also proposed an alternative market efficiency test with the hypothesis testing Ho: α = 0 and was able to prove the S&P 500 index is efficient, but not perfect. More important, those findings above are checked with and validated by Jackknife resampling results.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.199-215
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• 2020

Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.695-708
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• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.490-497
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• 2018

In this study, we examined whether common idiosyncratic volatility(CIV), a risk factor for idiosyncratic volatility, can be evaluated as a pricing factor. The sample is listed on the Korea Exchange. The analysis period is 288 months from July 1992 to June 2016. The main results of this study are as follows. First, in the empirical verification of the market excess returns of the testing portfolios, the difference in the return on the CIV factor sensitivity difference was statistically significant. In other words, we confirmed that there is a risk premium for CIV factors. Second, CAPM, FF3 factor model, and FF5 factor model do not explain the risk premium for CIV factors, whereas factor models that add CIV factors explain the risk premium for CIV factors. In other words, the CIV factor can be evaluated in terms of pricing factors.

• The Korean Journal of Financial Management
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• v.7 no.1
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• pp.1-20
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• 1990

In most of studies on market efficiency, the stability of risk measures and the normality of residuals unexplained by the pricing model are presumed. This paper re-examines stock splits, taking the possible violation of two assumptions into accounts. The results does not change the previous studies. But, the size of excess returns during the 2-week period before announcements decreases by 43%. The results also support that betas change around announcements and the serial autocorrelation of residuals is caused by events. Based on the results, the existing excess returns are most likely explained as a compensation to old shareholders for unwanted risk increases in their portfolio, or by uses of incorrect betas in testing models. In addition, the model suggested in the paper provides a measure for the speed of adjustment of the market to the new information arrival and the intensity of information contents.