KOSPI200 Index Research Articles

금융시장 예측을 위한 시계열자료의 변환기법 융합을 이용한 패턴 모델 결정

수출주도정책, FTA 체결 및 규제개선 등과 같은 다양한 시장지향적인 정책을 통해 경제시장의 규모가 지속적으로 커졌다. 이에 따라 올바른 의사결정을 위하여 경제시장을 정확하게 분석, 예측하는 문제가 중요한 이슈가 되었다. 경제시장을 표현하는 여러 지표 중 가장 대표적인 주식지표의 정확한 분석 및 의사결정을 위하여 시계열자료의 모델링에 적합한 은닉마아코프모델을 토대로 자료 내에 내재된 예외적인 특징과 잡음을 제거하기 위한 변환기법의 융합모델을 제안하여 모델 추정과 예측 문제에 적용하였으며 그 유효성을 확인하였다. 실험 결과를 통해, 본 연구에서 제안하는 변환조합을 적용하는 모델추정 기법이 유효한 모델 상태 추정 결과를 보여주었으며 실제 코스피지수와 예측의 문제에서도 매우 유사한 운동양태를 확인할 수 있었다. Export-led policies, FTA signed and economics of scale through a variety of market-oriented policies, such as regulations to improve market grew constantly. Accordingly, the correct decision making accurately analyze the economics market for decision, a problem has been an important issue in predicting. For accurate analysis and decision-making of the most common indicators of the stock market by proposing a number of indicators of economic transformation techniques were applied to the convergence model combining estimation and forecasts problem confirmed its effectiveness. Experimental result, gave the model estimation method to apply a transform to show the valid combinations proposed model state estimation result was confirmed in a very similar exercise aspect of the physical problem and the KOSPI index prediction.

금융자산의 시장 미시구조 잡음에 대한 부트스트래핑 라그랑지 승수 검정

Stationary bootstrapping is applied to a Lagrangian multiplier (LM) test to test market microstructure noise (MMN) in financial asset prices. A Monte-Carlo experiment shows that the bootstrapping method improves the size of the original LM test which has some size distortion for conditional heteroscedastic models. The proposed test is illustrated for real data sets like KOSPI index and Won-Dollar exchange rate.

Effect of the subprime mortgage crisis on a leading emerging market

This study examines how the US subprime mortgage crisis affects the behaviour of the Korean stock and futures market and how the futures traders react to the shocks related to the crisis. Analysing a unique and high-quality daily data set on the ABX subprime index of the United States, Korea's implied volatility index (VKOSPI), and the KOSPI200 index and futures, we find a significant linkage and contagion effect between the US subprime market and the Korean market during the crisis period. However, the explanatory power of the ABX index return dissipates during the period of the recovery (after 2010). Our analysis, based on unique information about the types of futures traders, indicates that foreign investors are quite sensitive to the subprime shocks, whereas domestic investors are not. Furthermore, the empirical findings indicate that domestic individual investors invest their money in the opposite direction of the ABX index's movement during the subprime crisis period.

Investment Strategies for KOSPI200 Index Futures Using Negative Correlation of Time-Series

This paper presents 6 time-series that have negative correlation with KOSPI200 Index and a quantitative trading methodology based on stochastic control chart using these time-series. The proposed quantitative trading framework detects trade (long or short) timing by monitoring whether a time-series touches 4 trigger lines, which play a role as control limits in control chart. In other words, a time-series upwardly touches one of trigger line, then the framework take a short position on KOSPI200 Index Futures, while in case of downward touch, it takes a long position. The 6 time-series are derived from VKOSPI and USD Futures Index that are negatively correlated with KOSPI200 Index, and have a significance that prevents disclosure of trading strategies by processing and transforming the original time-series. Computational experiments using real KOSPI200 futures index for recent 4 years are conducted to show the excellence of the proposed investment strategies against benchmark strategies under quantitative trading framework.

벡터오차수정모형과 다변량 GARCH 모형을 이용한 코스피200 선물의 헷지성과 분석

벡터오차수정모형과 다변량 GARCH 모형을 이용한 코스피200 선물의 헷지성과 분석

증권시장 흐름과 투자 집단 간의 투자 포지션

우리나라 유가증권시장의 추세 흐름과 투자자들의 투자 포지션의 관계를 중심으로 투자자들의 거래가 어떠한 특징을 갖는지를 실증적으로 분석하였다. 연구 대상 기간은 2004년 1월부터 2011년 12월까지 8년간인 총 96개월의 1,991일간이다. 분석에 사용된 자료는 한국종합주가지수(KOSPI)와 각 거래집단의 총매수량과 매도량, 매수대금과 매도대금 등이다. 매월 말과 매일 종가 두 자료를 이용하여, 각 집단 간의 상관관계를 검정하고 회귀분석을 하였다. 본 연구는 시장을 장기적인 관점에서 국면을 세분화하여 각 투자집단의 투자 포지션을 검정하였다. 외국인들과 기관들은 주로 고가 종목에 대한 거래에 집중되었다는 것을 알 수 있었다. 그리고 국면을 좀 더 세분화하여 검정한 결과 외국인 투자자들은 기관들과는 달리 상승3기에 지수가 상승할 경우 증시에서 빠져나가는 현상을 볼 수 있었다. 하락 말기에 개인들은 매도를 증가시키고 있다는 것을 알 수 있었다. 일반적으로 개인들은 시장의 흐름과 역행하면서, 외국인 투자자들의 상대방이 되어 반대 포지션을 선택하고 있다고 할 수 있을 것 같다. In this paper, characteristics of transactions by investors were examined based on the relationship between South Korea's stock market trends and the amount of net purchasing by investors. The study period is from January of 2004 to December of 2011, a total 1,991 days on 96 months. Data used for correlation and regression analysis include the value of the KOSPI index at the end of each month, the monthly net purchase amount of each of the groups, as well the daily volume, the daily price. In this study, the long-term phase of the market divided by refining. and each of the investment position of invest group was investigated. As a result, foreign investors are a net selling position when market was rising phase of the tertiary. And private investors were a net short positions when the market was decline phase of the tertiary. Regardless of the flow changes, the private investors had opposite position to the flow of the mark, also they had opposite position to the position of the foreign investors.

Volatility and Trading Volumes of Trader Types in KOSPI200 Index, Futures, and Options Markets

In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.

Laeding and Following Variance Risk Premiums: Evidence from S&P500 and KOSPI200 Options

This study aims to examine the return predictability of variance risk premium, which is defined as the difference between risk-neutral variance and expected realized variance, on KOSPI 200 index returns. Although extant literature shows that variance risk premium estimated from U.S. index options has a predictive power on underlying returns, little study has been conducted in KOSPI 200 index returns. In addition, there is no conclusion for the predictive power of variance risk premium in other financial markets. In this paper, we can find the predictive power of S&P500 variance risk premium on KOSPI200 index returns as well as on S&P500 index returns, but cannot find the predictive power of KOSPI200 variance risk premium on both indices. These results are consistent to Londono (2012) and Bollerslev et al. (2013). The poor performance of KOSPI200 variance risk premium is explained by the assumption that U.S. economy is a leader economy, while Korea economy is a follower economy. To support this conclusion, we conduct Vector Auto-Regression (VAR) using two variance risk premiums. Two premiums have bi-directional lead-lag relationship but S&P500 variance risk premium is informationally superior to KOSPI200 variance risk premium regarding return predictions.

Measuring the Toxicity of Order Flow using High Frequency Data: The Case of KOSPI200 Index Futures

This study presents a new informed trading probability measure VPIN (Volume-Synchronized Probability of Informed Trading) to estimate toxic order flow of KOSPI200 index futures in a high frequency world. This measure does not require to estimate non-observable parameters as PIN. Also, it is estimated based on volume time, so it can estimate toxicity of order flow in a high frequency world. We show a relation between KOSPI200 index futures VPIN and futures market volatility using correlation and conditional probability distribution. A main empirical result is that persistently high VPIN signifies a high risk of subsequent large futures market volatility. It means that VPIN is a useful measure to estimate a toxicity induced volatility.

Volatility Spillovers from Australia's Major Trading Partners Across the GFC

This paper features an analysis of volatility spillover effects from Australia's major trading partners, namely, China, Japan, Korea and the United States, for a period running from 1st January 2004 to 30th June 2014. This captures the impact of the Global Financial Crisis (GFC). These markets are represented by the following major indices: The Shanghai composite and the Hangseng. (in the case of China, as both China and Hong Kong appear in Australian trade statistics), the S&P500 index, the Nikkei225 and the Kospi index. We apply the Diebold and Yilmaz (2009) Spillover Index, constructed in a VAR framework, to assess spillovers across these markets in returns and in volatilities. The analysis confirms that the US and Hong Kong markets have the greatest influence on the Australian one. We then move to a GARCH framework to apply further analysis and apply a tri-variate Cholesky-GARCH model to explore the effects from the US and Chinese market, as represented by the Hang Seng Index.

The Variance Swaps based on KOSPI200 Index and the Term Structure of Variance Risk Premium

This study examines whether the variance risk is a priced risk factor in Korea using the over-the-counter variance swap quotes and realized variance data. We also study the term structure of variance risk premium. The empirical results show that the model with 2 stochastic variance risk factors with jumps in return is required to fit the variance swap and realized variance data. The analyses with the estimated models suggest that the variance risk premium in Korea are highly negative and the size of the premium increase with the maturities, meaning that risk averse investors in Korea are willing to pay a premium to hedge variance risk.

ETF와 블랙리터만 모형을 이용한 인핸스드 인덱스 전략

In this paper, we deal with an enhanced index fund strategy by implementing the exchange trade funds (ETFs) within the context of the Black-Litterman approach. The KOSPI200 index ETF is used to build risk-controlled portfolio that tracks the benchmark index, while the proposed Black-Litterman model mitigates estimation errors in incorporating both active investment views and equilibrium views. First, we construct a Black-Litterman model portfolio with the active market perspective based on the momentum strategy. Then, we update the portfolio with the KOSPI200 index ETF by using the equilibrium return ratio and weighted averages, while devising optimization modeling for improving the information ratio (IR) of the portfolio. Finally, we demonstrate the empirical viability of the proposed enhanced index strategies with KOSPI 200 data.

Hedging Effectiveness of KOSPI200 Index Futures and Options

The purpose of this study is to investigate hedging effectiveness of KOSPI200 index futures and options using three measures proposed by Fishburn (1977), Ederington (1979), and Howard and D’Antonio (1987). The comparison of hedging effectiveness is conducted based on the market prices of KOSPI200 index futures and options traded in Korea Exchange (KRX) between January of 2001 and January of 2011, during which bootstrapping method is utilized to make a dataset of 100,000 random samples with holding period of 1, 3, 6, and 12 months, respectively. We examine the hedging performance of hedge portfolios made of short futures, protective puts and covered calls respectively based on three hedging effectiveness measures. One of our finding is that short futures hedging is better than options in minimizing total volatility risk as well as down-side risk, which is consistent to the previous researches. Also futures hedging is more effective in reducing the VaR than the others. Secondly, the optimal hedge ratios of futures in minimizing total risk and down-side risk are turned out to be 0.97~0.98 and 0.94~0.95 respectively. Third, OTM short call hedge is the best hedging instrument when hedgers would like to maximize the Sharpe ratio. Finally, protective put hedging strategy is in general inferior to the short futures and covered call hedge based on three hedging effectiveness measures.

Information Flow between Stock Skew and Options Risk Neutral Skew: Evidence from KOSPI200 Options

This paper investigates the effects of risk neutral distribution (RND) from option prices on the distribution of the underlying asset. More specifically, we focus on the third moment of distribution, called skewness, which contains important information predicting the jumps of stock index. The sample period covers from January 2002 to July 2006 with the closing price returns of KOSPI200 Index and the KOSPI200 options. The skewness of the risk neutral distribution is estimated from non-parametric method of Bakshi et al.(2003) and the parametric method of Corrado and Su (1996). When estimating the skewness of the underlying assets, we employ Chen et al.(2001) model and calculate the historical skewness from the1-month ahead return underlying asset. Using statistical methodology such as VAR (Vector Autoregressive model), Granger causality test, impulse response and variance decomposition model, we examine whether the skewness of the underlying asset responds to the change of the implied RND. Followings are the major findings and implications drawn from the empirical analysis of the Korean options market. First of all, skewness of options estimated from non-parametric method have information contents predicting the third-moment of KOSPI200 index return whereas skewness of options estimated from parametric method does not have any information forecasting the skewness of KOSPI200 index return.

No-arbitrage implied volatility functions: Empirical evidence from KOSPI 200 index options

Implied and local volatility are very important variables to market practitioners because such variables can be exploited in numerous option models for the pricing and hedging of diverse exotic options. In the present study, we propose a method to implement no-arbitrage constraints in estimating the implied and local volatility surfaces extracted from data on option prices. With the aid of multiple local bandwidths, we increase the functional flexibility of estimators and provide a simple method by which to construct no-arbitrage volatility surfaces, such that the ready-in-computation advantage of the derivatives in local quadratic smoothing is preserved. To show the effectiveness of the arbitrage-free models, we perform a comprehensive empirical study on the performance of the competing models using the KOSPI 200 index options from January 2001 through December 2010. Using experiments, we examine the performance of the models based on three measures: in-sample pricing, out-of-sample pricing, and hedging errors. We find that implied and local volatility modeling under arbitrage-free conditions show better performance in terms of estimation, pricing, and hedging near the out-of-the-money with short maturities. From the range depicted in the findings, we often observe clear differences between the models with and without no-arbitrage conditions imposed. ► Implementation of no-arbitrage implied volatility surfaces is proposed. ► Comprehensive empirical study using the KOSPI200 index options is given. ► No-Arbitrage local volatility models show better performance for OTM and ITM.

Price impact asymmetry of futures trades: Trade direction and trade size

By analyzing the high-quality intraday transaction dataset of KOSPI200 index futures contracts, one of the most actively traded index futures products in the world, this study examines price impact asymmetry between buyer- and seller-initiated trades and the difference in information content across the size of trades. To measure the permanent price impact incurred by each futures trade, which can be translated into the quality of information content of each trade, we use a modified version of the MRR model (Madhavan et al., 1997), which is appropriate for gauging the price impact and information content as well as analyzing the intraday price discovery issues that arise in purely order-driven markets.Consistent with the empirical results of previous studies on market microstructure issues in Korea's index derivatives market (i.e., KOSPI200 index futures and options market), we find that large trades generally incur greater permanent price impacts than small trades. This indicates that large trades generally have greater information content than the smaller ones. However, in contrast to the majority of empirical studies in this area, which have reported that buy trades are more informative than sell trades in global financial markets, we find that the permanent price impact of seller-initiated trades is clearly and substantially larger than that of buyer-initiated trades in the KOSPI200 futures market. This indicates that sell trades are more informed than buy trades in the index futures market, where informed investors can freely submit sell orders without any restrictions. The greater information content of sell trades is also apparent when trades are classified by their size. These results are quite remarkable considering that the sample period of this study (2003–2006) corresponds to a recovery period, during which the underlying stock index price and the futures price continued to increase.

변동성지수와 관리도를 이용한 KOSPI200 지수선물 투자전략

This paper proposes quantitative investment strategies for KOSPI200 index futures using VKOSPI and control chart. Stochastic control chart is employed to decide when to take a position as well as what position out of long and short should be taken by monitoring whether VKOSPI or difference of VKOSPI touches the control limit lines. The strategies include 4 approaches, which are traditional control chart and 2-Area control chart coupled with VKOSPI and its difference, respectively. Computational experiments using real KOSPI200 futures index for recent 3 years are conducted to show the excellence of the proposed investment strategies under control chart framework.

A Two Phase Algorithm for Fuzzy Time Series Forecasting using Genetic Algorithm and Particle Swarm Optimization Techniques

ABSTRACT Fuzzy Time series is being used for forecasting since last two decades for forecasting. Nature inspired computing techniques like other domains are now being used for optimization purpose in Fuzzy Time Series forecasting models to get improved results. In this paper we have presented a new algorithm for multivariate fuzzy time series forecasting having two phases. Genetic Algorithm and Particle Swarm Optimization techniques are used in this algorithm for optimization. We applied our algorithm on Taiwan forex Exchange (TAIFEX) index and got better results and minimized error rate as compared to previous methods. General Terms Nature inspired computing; Time Series forecasting Keywords Fuzzy time series; two-factor high-order fuzzy logical relationships; Genetic Algorithm, Particle Swarm Optimization; TAIFEX index. 1. INTRODUCTION Forecasting is prediction of unseen values of some sequence. It holds significance in economic and financial modeling as entrepreneurs use predicted values for business planning and taking key decisions. In the last two decades Fuzzy Time Series (FTS) is being used for forecasting purpose. Song and Chissom [1, 2] used the concept of FTS and forecasted number of enrollments of the University of Alabama. Many other researchers proposed their models for enrollments forecasting of the University of Alabama ([3], [4], [5], [6], [7], [8], [9], [10] and [11]), temperature prediction [12, 13] and car road accidents [14, 15] using fuzzy time series. Huarng [7] presented heuristic based model for fuzzy time series forecasting. Jilani and Burney [14] presented M-factor high order fuzzy time series forecasting model for car road accident data. Jilani and Burney [16] presented new heuristic based approach for frequency density based partitioning for fuzzy time series forecasting of stock market. In the recent years many researchers started applying Nature inspired computation (NIC) techniques for optimization purpose in FTS forecasting. Kuo et al. [17] and Huang et al.[18] proposed Particle Swarm Optimization (PSO) based FTS forecasting models for enrollments data of the University of Alabama. Park et al. [19] proposed a forecasting model for TAIFEX and KOSPI-200 index, which was based on FTS and swarm intelligence. Jilani et. al. [20] proposed a PSO based FTS forecasting model for car road accidents. Jilani et. al. presented a hybrid algorithm based on Genetic Algorithm (GA) and PSO for forecasting TAIFEX Genes are randomly generated initially to make chromosomes and KSE-100 index. Jilani et. al. [22] presented a trend based heuristic approach using GA for forecasting car road accidents. In this paper we have applied genetic algorithm in first phase to optimize weights, and in second phase we used those weights and optimized interval length to get best forecasting result.

Forecasting nonnegative option price distributions using Bayesian kernel methods

► The proposed Bayesian learning model predicts positive option price distribution. ► A new transform measure preserves distributions of large deviations. ► Comprehensive empirical study using the KOSPI200 index options is given. ► Simulation verifies better performance of the proposed method. This paper proposes a novel Bayesian kernel model that can forecast the non-negative distribution of target option prices, which are constrained to be positive. The method utilizes a new transform measure that guarantees the non-negativity of option prices, and can be applied to Bayesian kernel models to provide predictive distributions of option prices. Simulations conducted on the model-generated option data and KOSPI 200 index option data show that the proposed method not only provide a predictive distribution of non-negative option prices, but also preserves the probabilistic distribution of large deviations. We also perform a very extensive empirical study on a large-scale time series of option prices to assess the prediction performance of the proposed method. We find that the method outperforms other state of the arts non-parametric methods in prediction accuracy and is statistically different.

재고통제기법을 이용한 효율적 포트폴리오 관리 방안

This paper proposes an efficient portfolio management methodology named sSPPM with consideration of risk and required return. sSPPM employs Markowitz's portfolio model to select securities and adopts (<TEX>$s$</TEX>, <TEX>$S$</TEX>) policy that is a well-known technique in the inventory control area to revise the current portfolio. Computational experiments using virtual stock prices generated by monte carlo simulation method as well as real stock ones of KOSPI for recent 4 years are conducted to show the excellence of the portfolio management under (<TEX>$s$</TEX>, <TEX>$S$</TEX>) policy framework. The result shows that sSPPM is remarkably superior to both 6 or 12 months based periodic portfolio revision method and market (KOSPI index).