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

• Journal of Digital Convergence
• /
• v.7 no.1
• /
• pp.73-80
• /
• 2009

In this study, the pricing performances of alternative simple option models are examined by creating a simulated market environment in which asset prices evolve according to a stochastic volatility process. To do this, option prices fully consistent with Heston[9]'s model are generated. Assuming this prices as market prices, the trading positions utilizing the Black-Scholes[4] model, a semi-parametric Corrado-Su[7] model and an ad-hoc modified Black-Scholes model are evaluated with respect to the true option prices obtained from Heston's stochastic volatility model. The simulation results suggest that both the Corrado-Su model and the modified Black-Scholes model perform well in this simulated world substantially reducing the biases of the Black-Scholes model arising from stochastic volatility. Surprisingly, however, the improvements of the modified Black-Scholes model over the Black-Scholes model are much higher than those of the Corrado-Su model.

• KDI Journal of Economic Policy
• /
• v.34 no.1
• /
• pp.29-52
• /
• 2012

During the global financial turmoil in 2007-2008, deviation from the covered interest parity (CIP) between the Korean won and US dollar through the foreign exchange swap has escalated in its magnitude beyond 1,000bp in November 2008, and it still persists around 100bp level. In this paper, we examine a newly developed margin based asset pricing model using Kalman filter approach and show that the escalation of the CIP deviation is found to be significantly related to the global dollar funding illiquidity and country-specific funding conditions. Furthermore, we find evidence that the poor funding conditions (or higher margins) are driven by the general money market illiquidity and may lead to higher funding illiquidity, which suggests the reinforcing effects of the liquidity spiral. We also show that the supply of dollar liquidity and improved funding conditions help alleviate the deviations from the parity, however the persistent anomaly is found to be related to the high level of volatility in the FX swap market.

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

• Journal of Korean Institute of Industrial Engineers
• /
• v.36 no.2
• /
• pp.101-107
• /
• 2010

Among a variety of asset dynamics models in order to explain the common properties of financial underlying assets, parametric models are meaningful when their parameters are set reliably. There are two main methods from which we can obtain them. They are to use time-series data of an underlying price or the market option prices of the underlying at one time. Based on the Girsanov theorem, in the pure diffusion models, the parameters calibrated from the option prices should be partially equivalent to those from time-series underling prices. We call this phenomenon model consistency. In this paper, we verify that the two-state regime switching Black-Scholes model is superior in the sense of model consistency, comparing with two popular conventional models, the Black-Scholes model and Heston model.

• Asia pacific journal of information systems
• /
• v.9 no.3
• /
• pp.93-110
• /
• 1999

The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.20 no.2
• /
• pp.123-135
• /
• 2016

This article deals with the pricing of Asian options under a constant elasticity of variance (CEV) model as well as a stochastic elasticity of variance (SEV) model. The CEV and SEV models are underlying asset price models proposed to overcome shortcomings of the constant volatility model. In particular, the SEV model is attractive because it can characterize the feature of volatility in risky situation such as the global financial crisis both quantitatively and qualitatively. We use an asymptotic expansion method to approximate the no-arbitrage price of an arithmetic average Asian option under both CEV and SEV models. Subsequently, the zero and non-zero constant leverage effects as well as stochastic leverage effects are compared with each other. Lastly, we investigate the SEV correction effects to the CEV model for the price of Asian options.

• Journal of Digital Convergence
• /
• v.7 no.4
• /
• pp.43-56
• /
• 2009

The concept causing all the fuss is "the utility computing or the usage-based IT service", which now represents the future for IT asset in all aspects of the way they work in business, the commercial and public sector. The core of "utility computing or usage-based IT service" is changing the IT assert from "ownership" to "borrowing", which enables managers to get greater utilization of data-centre resources at lower operating costs. This trend is spreaded in public sector centering the Governmental Internet data Center of Korea(NCIA). So, it has need to make an usage-based IT service model that is suitable for public sector. In this paper, we propose the usage-based IT service model that is composed of IT service framework, service pricing model and IT service architecture.

• Communications for Statistical Applications and Methods
• /
• v.24 no.5
• /
• pp.507-518
• /
• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• Asian Journal of Innovation and Policy
• /
• v.6 no.2
• /
• pp.225-244
• /
• 2017

When evaluating the economic value of technology or business project, we need to consider the period and cost for commercialization. Since the discounted cash flow (DCF) method has limitations in that it can not consider consecutive investment or does not reflect the probabilistic property of commercialization cost, we often take it desirable to apply the concept of real options with key metrics of underlying asset value, commercialization cost, and volatility, while regarding the value of technology and investment as the opportunity value. We at this moment provide more elaborated real options model with the effective region of volatility, which reflects the uncertainty in the option pricing model (OPM).