• Journal of Distribution Science
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• v.13 no.5
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• pp.53-60
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• 2015

Purpose - The purpose of this paper is to shed light on the importance of the slope and curvature of the volatility curve implied in option prices in the KOSPI 200 options index. A number of studies examine the implied volatility curve, however, these usually focus on cross-sectional characteristics such as the volatility smile. Contrary to previous studies, we focus on time-series characteristics; we investigate correlation dynamics among slope, curvature, and level of the implied volatility curve to capture market information embodied therein. Our study may provide useful implications for investors to utilize current market expectations in managing portfolios dynamically and efficiently. Research design, data, and methodology - For our empirical purpose, we gathered daily KOSPI200 index option prices executed at 2:50 pm in the Korean Exchange distribution market during the period of January 2, 2004 and January 31, 2012. In order to measure slope and curvature of the volatility curve, we use approximated delta distance; the slope is defined as the difference of implied volatilities between 15 delta call options and 15 delta put options; the curvature is defined as the difference between out-of-the-money (OTM) options and at-the-money (ATM) options. We use generalized method of moments (GMM) and the seemingly unrelated regression (SUR) method to verify correlations among level, slope, and curvature of the implied volatility curve with statistical support. Results - We find that slope as well as curvature is positively correlated with volatility level, implying that put option prices increase in a downward market. Further, we find that curvature and slope are positively correlated; however, the relation is weakened at deep moneyness. The results lead us to examine whether slope decreases monotonically as the delta increases, and it is verified with statistical significance that the deeper the moneyness, the lower the slope. It enables us to infer that when volatility surges above a certain level due to any tail risk, investors would rather take long positions in OTM call options, expecting market recovery in the near future. Conclusions - Our results are the evidence of the investor's increasing hedging demand for put options when downside market risks are expected. Adding to this, the slope and curvature of the volatility curve may provide important information regarding the timing of market recovery from a nosedive. For financial product distributors, using the dynamic relation among the three key indicators of the implied volatility curve might be helpful in enhancing profit and gaining trust and loyalty. However, it should be noted that our implications are limited since we do not provide rigorous evidence for the predictability power of volatility curves. Meaning, we need to verify whether the slope and curvature of the volatility curve have statistical significance in predicting the market trough. As one of the verifications, for instance, the performance of trading strategy based on information of slope and curvature could be tested. We reserve this for the future research.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.151-157
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• 2010

This paper proposes the Pyramid strategy which is based on the straddle sell. The Pyamid strategy has multi-entry features with starting date and delta parameters. And It is hedged against a loss by mutual trades and dynamic ripples. This paper analyzes the profit and MDD(maximum draw down) of the Pyramid strategy on system trading. The portfolio tool is used for the experiment which is one of the Multicharts' package. The Multicharts is a good trading system of recent years. For the experiment, three call options and three put options are used at october in 2009. Two parameters are used which are the starting date from first October to twentieth October in 2009 and delta from eight percent to fifty percent. As a result, the profit of composite option is about 3 million won. If the strategy starts before the beginning of option month, investors feel uncomfortable because of a large MDD. If a delta belows 20%, it shows high profit and the ratio of profit and MDD builds up a low value. However a low delta makes frequent trades and results in a loss unless increasing entry levels which mean more amount of investment. This work provides a safer trade system than native option trades. It is important how much levels of multi-entry are acceptable. And an amount of investment with appropriate levels of multi-entry is a subject of a future study.

• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.465-491
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• 2012

Climate change, a result of increasing global warming, has been receiving more public attention due to its serious impact upon many industries. In this study we consider sustainable- (Green-) Growth and Green-Finance, and in particular temperature derivatives, as appropriately active responses to the world's significant climate change trends. We characterize the daily average temperatures in Seoul, South Korea with their seasonal properties and cycles of error terms. We form forecasting models and perform Monte Carlo simulations, and find that the risk-neutral values for CDD call-options and HDD put-options have risen since 1960s, which implies that the trend of temperature increase can be quantified in the financial markets. Contrary to the existing models, the Vasicek model with the explicit consideration of cycles in the error terms suggests that the significant option-values for the CDD call -options above certain exercise prices, implying that there is the possibility of explicit hedging against the considerable and stable increase in temperature.

• Korea Trade Review
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• v.41 no.5
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• pp.113-138
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• 2016

This study analyzed the impact when Korea expands Chinese Renminbi(RMB) as invoicing currency on the trade to China using Monte-Carlo simulation. Primarily, we analyzed the impact on the balance of Korean Won(KRW) converted from RMB in a case that simulated exchange rate(Korean won to Chinese Renminbi) and realized historically identical probability distribution but in different stochastic process. In addition, we developed the simulation of the case where the volatility of RMB to KRW exchange rate abnormally expanded. The major results found in this study are as follows. First, in the case where RMB exchange rate simulated in identical probability distribution but in the different stochastic process, no matter how much RMB was utilized as invoicing currency, expansion of the RMB exchange rate and exchange rate volatility operated as positive mechanism to increase the KRW converted balance. Secondly, while the expansion of US dollar exchange rate volatility positively influences the balance on average, it caused a polarization of balance, which makes under-average-balance lower and over-average-balance higher. On the contrary, the expansion of RMB exchange rate volatility even shows a similar mechanism but the impact is more moderate than USD exchange rate volatility. Thirdly, as RMB exchange rate volatility expanded, the balance of translated invoicing currency (RMB) declined, whilst the negative impact of RMB exchange rate volatility on balance of translated invoicing currency(RMB) showed diminishing effect. Lastly, the influence of RMB's exchange rate volatility through RMB usage ratio trends similar to bull spread strategy, which is a combination of call option with put option. Therefore, since RMB usage in invoicing currency could spawn a hedging effect, corporations might utilize RMB as a strategic device for maximizing profits.

• Journal of Venture Innovation
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• v.1 no.1
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• pp.83-98
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• 2018

This paper makes a theoretical approach to the differences between transaction tax and capital gains tax when the financial instruments are traded and imposed taxes in K-OTC market, a newly emerging off-board market. Since it is difficult to reduce risk to the level which investors would like to pursue - depending on the taxation methods of portfolio-composed financial instruments - when it comes to forming a synthetic bond to hedge risk, this paper also seeks for effective taxation methods to make this applicable. First of all, to thoroughly review the taxation balance of synthetic bonds, this paper analyzed the effects of the transaction tax and capital gains tax imposed upon synthetic bonds according to the changes in final stock price and strike price in K-OTC market, and analyzed after-tax profit differences among them depending on whether income tax deduction took place or not. As a result of the research upon the tax gap in transaction tax and capital gains tax according to the changes of final stock prices, it was shown that imposing transaction tax is more likely to be effective for some level of risk hedging with replicating portfolio considering taxation policies and financial markets, since the effect of the transaction tax has a much lower tax gap than that of capital gains tax. In addition, in relation to whether income tax deduction was permitted or not, it was proved that the effect of the transaction tax and the capital gains tax vary depending on the variation in the strike price. Above all, it was shown that if the strike price is lower than the stock price, the transaction tax will be less affected by the existence of income tax deduction than the capital gains tax, while both will be equally affected by the existence of income tax deduction if the strike price is higher than the stock price. Further study would be to demonstrate the validation of this in the K-OTC market with actual financial instruments and, also, to seek for a more systematic hedging method by using a ratio analysis approach to the calculation of the option transaction tax