• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.742-745
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• 2004

Most of options pricing theory including Black and Scholes continuous model and Cox, Ross, and Rubinstein(CRR)'s binomial lattice model were developed based on the notion that continually revised risk-free hedges involving options and stock should earn the risk-free interest rate. This notion is valid with the assumption that the investor's attitude toward risk is neutral. In reality, this assumption may be frequently violated. Therefore, Hodder, Mello, and Sick proposed the way to value real options using the risk-adjusted interest rate. However, they did not show how to derive the mathematical expression for it. In this paper, we will clearly present how to obtain the mathematical expression for the risk-adjusted interest rate for real options and demonstrate two numerical examples to show its applicability.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.403-412
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• 1992

This note examines a situation where a risk-neutral insurer and a risk-averse individual (prospective insured) negotiate to reach an arbitration point of the price of insurance over the terms of an insurance contract in order to maximize their respective self-interests. The situation is modeled as a Nash bargaining problem. We analyze the dependence of the price of insurance, which is determined by the Nash solution, on the parameters such as the size of insured loss, the probability of a loss, the degree of risk-aversion of the insured, and the riskiness of loss distribution.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.487-493
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• 2009

We consider a multiple defaultable zero coupon bond. Assuming defaults occur according to a marked point process, we explain how to estimate the time-t discounted price of zero coupon bond by simulation. For the special case of a given specific random face value, we show that the real probability measure is the risk neutral probability measure. In this case the time-t discounted conditional price can be obtained by observing a single sample path upto the time t in the real world. Furthermore the time-t discounted price can be estimated by observing real situations or by simulation under the real probability measure.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.495-501
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• 2004

HJM representation of the term structure of interest rates sometimes produces the negative interest rates with positive probability. This paper shows that the condition of positive interest rates can be derived from the jump diffusion process, if a proper positive martingale process with the compensated jump process is chosen. As in Flesaker and Hughston, the condition is incorporated into the bond price process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.433-438
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• 2018

This study examined the effects of ambiguity aversion on the self-protection and self-insurance efforts using a two-period model to consider the time difference between making an effort and occurring loss, which is in contrast with the existing one-period model. The loss follows a binary distribution while the distribution is ambiguous. The distribution depends on the state variable. First, the effort of ambiguity averse individuals is not always greater than that of ambiguity neutral ones. Second, the effects of absolute ambiguity aversion (AAA), which does not appear in one-period model, were observed. Not-increasing AAA is a sufficient condition to increase the efforts of ambiguity averse individuals compared to those of ambiguity neutral ones. In addition, the change in effort also depends on the probability function of the state. Lastly, the results hold even when the individual is risk neutral or risk loving. As a result, ambiguity aversion needs to be considered independently with risk aversion.

• Management Science and Financial Engineering
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• v.21 no.2
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• pp.13-23
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• 2015

This study addresses the question as to whether the option prices have useful predictive information on the direction of stock markets by investigating a forecasting power of volatility curvatures and skewness premiums implicit in S&P 500 index option prices traded in Chicago Board Options Exchange. We begin by estimating implied volatility functions and risk neutral price densities every minute based on non-parametric method and then calculate volatility curvature and skewness premium using them. The rationale is that high volatility curvature or high skewness premium often leads to strong bullish sentiment among market participants. We found that the rate of return on the signal following trading strategy was significantly higher than that on the intraday buy-and-hold strategy, which indicates that the S&P500 index option prices have a strong forecasting power on the direction of stock index market. Another major finding is that the information contents of S&P 500 index option prices disappear within one minute, and so one minute-delayed signal following trading strategy would not lead to any excess return compared to a simple buy-and-hold strategy.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.620-627
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• 2003

The real option pricing theory has emerged as the new investment decision-making techniques superceding the traditional discounted cash flow techniques and thus has greatly received muck attention from academics and practitioners in these days the theory has been widely applied to a variety of corporate strategic projects such as a new drug R&D, an internet start-up. an advanced manufacturing system. and so on A lot of people who are interested in the real option pricing theory complain that it is difficult to understand the true meaning of the real option value. though. One of the most conspicuous reasons for the complaint may be due to the fact that there exit many different ways to calculate the real options value in this paper, we will present a replicating portfolio method. a risk-neutral probability method. a risk-adjusted discount rate method (quasi capital asset pricing method). and an opportunity cost concept-based method under the conditions of a binomial lattice option pricing theory.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.243-257
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• 2017

In this paper, we compare several methods to approximate option prices: Edgeworth expansion, A-type and C-type Gram-Charlier expansions, a method using normal inverse gaussian (NIG) distribution, and an asymptotic method using nonlinear regression. We used two different types of approximation. The first (called the RNM method) approximates the risk neutral probability density function of the log return of the underlying asset and computes the option price. The second (called the OPTIM method) finds the approximate option pricing formula and then estimates parameters to compute the option price. For simulation experiments, we generated underlying asset data from the Heston model and NIG model, a well-known stochastic volatility model and a well-known Levy model, respectively. We also applied the above approximating methods to the KOSPI200 call option price as a real data application. We then found that the OPTIM method shows better performance on average than the RNM method. Among the OPTIM, A-type Gram-Charlier expansion and the asymptotic method that uses nonlinear regression showed relatively better performance; in addition, among RNM, the method of using NIG distribution was relatively better than others.