• Proceedings of the Safety Management and Science Conference
• /
• 2003.11a
• /
• pp.329-333
• /
• 2003

This paper reviews differences between the traditional investment evaluation methods and real option approaches. This study considers management flexibility, contingency and volatility from a strategic management perspective. The roles of various real option approaches are discussed.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.251-260
• /
• 2017

The option pricing of Black와 Scholes (1973) and Merton (1973) has been widely reported to fail to reflect the time varying volatility of financial time series in many real applications. For example, Duan (1995) proposed GARCH option pricing method through Monte Carlo simulation. However, financial time series is known to follow a fat-tailed and leptokurtic probability distribution, which is not explained by Duan (1995). In this paper, in order to overcome such defects, we proposed the option pricing method based on GARCH models with normal mixture errors. According to the analysis of KOSPI200 option price data, the option pricing based on GARCH models with normal mixture errors outperformed the option pricing based on GARCH models with normal errors in the unstable period with high volatility.

• Bulletin of the Korean Mathematical Society
• /
• v.53 no.3
• /
• pp.733-749
• /
• 2016

We aim to compute discretely monitored geometric Asian option prices under the Heston model. This method involves explicit formula for multivariate generalized Fourier transform of volatility process and their integrals over different time intervals using a recursive method. As numerical results, we illustrate efficiency and accuracy of our method. In addition, we simulate scenarios which show evidently practical importance of our work.

• Bulletin of the Korean Mathematical Society
• /
• v.53 no.5
• /
• pp.1411-1425
• /
• 2016

We provide a partial differential equation for European options on a stock whose price process follows a general geometric Riemannian Brownian motion. The existence and the uniqueness of solutions to the partial differential equation are investigated, and then an expression of the value for European options is obtained using the fundamental solution technique. Proper Riemannian metrics on the real number field can make the distribution of return rates of the stock induced by our model have the character of leptokurtosis and fat-tail; in addition, they can also explain option pricing bias and implied volatility smile (skew).

• The Korean Journal of Applied Statistics
• /
• v.26 no.1
• /
• pp.187-200
• /
• 2013

In this paper, we deal with the problem of deciding a hedging volatility for ATM plain options when we hedge those options based on geometric Brownian motion. For this, we study the relation between hedging volatility and hedge profit&loss(P&L) as well as perform Monte Carlo simulations and real data analysis to examine how differently hedge P&L is affected by the selection of hedging volatility. In conclusion, using a relatively low hedging volatility is found to be more favorable for hedge P&L when underlying asset prices are expected to be range bound; however, a relatively high volatility is found to be favorable when underlying asset prices are expected to move on a trend.

• Asia-Pacific Journal of Business
• /
• v.14 no.4
• /
• pp.319-330
• /
• 2023

Purpose - The purpose of this paper is to examine the connectedness between categorical economic policy uncertainty (monetary, fiscal, trade and foreign exchange policy uncertainty) indexes and option-implied volatility index in Korea, Japan and the US. Design/methodology/approach - This paper employs the Diebold-Ylmaz (2012) model based on a VAR and generalized forecast error variance decomposition. This paper also conducts regression analyses to investigate whether the volatility indexes are explained by categorical policy uncertainty indexes. Findings - First, we find the total connectedness is stronger in Korea and Japan relative to the US. Second, monetary, fiscal, and foreign exchange policy uncertainty indexes are connected to each other but trade policy uncertainty index is not. Third, the volatility index in Japan and the US is mainly associated with monetary policy uncertainty while the volatility index in Korea is explained by fiscal policy uncertainty index. Research implications or Originality - To our knowledge, this is the first study to investigate the connectedness among categorical policy uncertainty indexes and the volatility index in Korea, Japan, and the US. The empirical results on the connectedness suggest that transparent policy and communication with the market in one type of policy would reduce the uncertainty in other policies.

• New & Renewable Energy
• /
• v.10 no.1
• /
• pp.41-49
• /
• 2014

For a proper valuation of wind power project, it is necessary to consider volatilities of key parameters such as annual energy production, electricity sales price, and long term interest rate. Real option methodology allows to calculate option values of these parameters. Volatilities to be considered in wind project valuation are 1) annual energy production (AEP) estimation due to meteorological variation and estimation errors in wind speed distribution, 2) changes in system marginal price (SMP), and 3) interest rate fluctuation of project financing which provides refinancing option to be exercised during a loan tenor for commercial scale projects. Real option valuation turns out to be more than half of the sales value based on a case study for a FIT scheme wind project that was sold to a financial investor.

• Journal of Korea Technology Innovation Society
• /
• v.7 no.3
• /
• pp.533-554
• /
• 2004

Real option valuation considers the managerial flexibility to make ongoing decisions regarding implementation of investment projects and deployment of real assets. The appeal of the framework is natural given the high degree of uncertainty that firms face in their technology investment decisions. This paper suggests an algorithm for estimating volatility of logarithmic cash flow returns of real asset based on Monte Carlo simulation. This research uses a binomial model to obtain point estimate of real option value with embedded expansion option case and provides also an array of numerical results to show the interval estimation of option value using Monte Carlo simulation.

• 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).

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1305-1325
• /
• 2008

As index option markets grow recently, many analysts and investors become interested in forecasting the volatility of KOSPI 200 Index to achieve portfolio's goal from the point of financial risk management and asset evaluation. To serve this purpose, we introduce NN and SVM integrated with other financial series models such as GARCH, EGARCH, and EWMA. Moreover, according to the empirical test, Integrating NN with GARCH or EWMA models improves prediction power in terms of the precision and the direction of the volatility of KOSPI 200 index. However, integrating SVM with financial series models doesn't improve greatly the prediction power. In summary, SVM-EGARCH was the best in terms of predicting the direction of the volatility and NN-GARCH was the best in terms of the prediction precision. We conclude with advantages of the integration process and the need for integrating models to enhance the prediction power.