• Management Science and Financial Engineering
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• v.20 no.1
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• pp.21-26
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• 2014

A lot of researches have been conducted to estimate the volatility smile effect shown in the option market. This paper proposes a method to approximate an implied volatility function, given noisy real market option data. To construct an implied volatility function, we use Gaussian Processes (GPs). Their output values are implied volatilities while moneyness values (the ratios of strike price to underlying asset price) and time to maturities are as their input values. To show the performances of our proposed method, we conduct experimental simulations with Korean Equity-Linked Warrant (ELW) market data as well as toy data.

• Korean Management Science Review
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• v.33 no.2
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• pp.117-131
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• 2016

This study introduces the concepts on the phase-shifting phenomenon of financial markets, which was firstly used in econophysics area and explains how the phase-shifting behavior is studied in the fields of business management and finance. Specifically, we explain how the phases of financial markets are extremely changed under some external conditions, do an extensive literature review, and carry out case studies focusing on the 3 major financial crisis events including the 87 October crash, 97 Asian financial crisis, and 2007 global financial crisis. We also empirically examine the phase-shifting behavior of the Korean ELW products that has a similar payoff structure to the KOSPI200 options.

• Industrial Engineering and Management Systems
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• v.8 no.2
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• pp.93-100
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• 2009

In this paper, we review widely used methods to extract local volatility surfaces (LVSs) from implied volatility surfaces (IVSs) and suggest a model averaging method for constructing implied and local volatility surfaces weighted by trading volumes. It makes use of model averaging method by means of bandwidth priors, and then produces a robust LVS estimation. The method is shown to provide the information about the confidence interval of estimators as well as a rather less variable weighted mean value for the IVS and LVS. To show the merits of our proposed method, we conduct simulations on equity-linked warrants (ELWs) with reasonable and acceptable results.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.84-94
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• 2011

In this paper, we propose a method to provide the distribution of option price under local volatility model when market-provided implied volatility data are given. The local volatility model is one of the most widely used smile-consistent models. In local volatility model, the volatility is a deterministic function of the random stock price. Before estimating local volatility surface (LVS), we need to estimate implied volatility surfaces (IVS) from market data. To do this we use local polynomial smoothing method. Then we apply the Dupire formula to estimate the resulting LVS. However, the result is dependent on the bandwidth of kernel function employed in local polynomial smoothing method and to solve this problem, the proposed method in this paper makes use of model averaging approach by means of bandwidth priors, and then produces a robust local volatility surface estimation with a confidence interval. After constructing LVS, we price barrier option with the LVS estimation through Monte Carlo simulation. To show the merits of our proposed method, we have conducted experiments on simulated and market data which are relevant to KOSPI200 call equity linked warrants (ELWs.) We could show by these experiments that the results of the proposed method are quite reasonable and acceptable when compared to the previous works.