• Title/Summary/Keyword: 변동률

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A Study on the Asymmetric Volatility in the Korean Bond Market (채권시장 변동성의 비대칭적 반응에 관한 연구)

  • Kim, Hyun-Seok
    • Management & Information Systems Review
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    • v.28 no.4
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    • pp.93-108
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    • 2009
  • This study examines the asymmetric volatility in the Korean bond market and stock market by using the KTB Prime Index and KOSPI. Because accurate estimation and forecasting of volatility is essential before investing assets, it is important to understand the asymmetric response of volatility in bond market. Therefore I investigate the existence of asymmetric volatility in Korean bond market unlike the previous studies which mainly focused on stock returns. The main results of the empirical analysis with GARCH and GJR-GARCH model are as follow. At first, it exists the asymmetric volatility on KOSPI returns like the previous studies. Also, I find that the GJR-GARCH is more suitable one than GARCH model for forecasting volatility. Second, it does not exist the asymmetric volatility on KTB Prime Index returns. This result is showed by that using the GARCH model for forecasting volatility in bond market is sufficient.

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Estimation of GARCH Models and Performance Analysis of Volatility Trading System using Support Vector Regression (Support Vector Regression을 이용한 GARCH 모형의 추정과 투자전략의 성과분석)

  • Kim, Sun Woong;Choi, Heung Sik
    • Journal of Intelligence and Information Systems
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    • v.23 no.2
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    • pp.107-122
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    • 2017
  • Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

Value-at-Risk Estimation of the KOSPI Returns by Employing Long-Memory Volatility Models (장기기억 변동성 모형을 이용한 KOSPI 수익률의 Value-at-Risk의 추정)

  • Oh, Jeongjun;Kim, Sunggon
    • The Korean Journal of Applied Statistics
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    • v.26 no.1
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    • pp.163-185
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    • 2013
  • In this paper, we investigate the need to employ long-memory volatility models in terms of Value-at-Risk(VaR) estimation. We estimate the VaR of the KOSPI returns using long-memory volatility models such as FIGARCH and FIEGARCH; in addition, via back-testing we compare the performance of the obtained VaR with short memory processes such as GARCH and EGARCH. Back-testing says that there exists a long-memory property in the volatility process of KOSPI returns and that it is essential to employ long-memory volatility models for the right estimation of VaR.

수익률 측정기간단위 변화에 따른 주식간 상관관계의 영향 연구

  • Eom, Cheol-Jun
    • The Korean Journal of Financial Studies
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    • v.10 no.1
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    • pp.231-248
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    • 2004
  • 본 연구는 주식간 상관관계 속성을 검증하고자 하는 연구의 일환으로, 한국주식시장에서 1980년 1월부터 2003년 5월까지 기간동안에 수익률 측정기간단위 변화에 따라 주식수익률간 상관관계에 어떤 영향이 관찰되는지를 검증하고자 하였다. 즉, 주식수익률간 상관관계가 시간의 함수(시간종속성)인지를 관찰하고자 하였다. 또한, 수익률의 측정기간단위에 따라 영향을 받는 주식수익률간 상관관계가 주식수익률에 영향을 미치는 요인의 어떤 변화에 기인하는 것인지를 시장모형을 이용하여 개별주식수익률 변동성의 구성요소로 분해 및 분석함으로써 그 원인을 찾고자 하였다. 검증결과에 의하면, 수익률의 측정기간단위가 증가함에 따라 주식수익률간 상관관계는 증가하는 경향을 나타냄에 따라 시간의 함수임을 부정할 수 없었고, 또한 측정기간단위가 단기에서 장기로 변화함에 따라 개별주식수익률의 변동성 구성요소에서 개별기업요인에 기인하는 부분은 감소되고 시장요인에 기인하는 부분은 증가하는 것을 알 수 있었다. 즉, 수익률 측정기간단위는 주식수익률간 상관관계에 유의적인 영향을 미치고, 이러한 영향은 주식수익률에 영향을 미치는 요인 중, 시장요인의 변화를 야기하는 것에서 원인을 찾을 수 있었다.

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Stock return volatility based on intraday high frequency data: double-threshold ACD-GARCH model (이중-분계점 ACD-GARCH 모형을 이용한 일중 고빈도 자료의 주식 수익률 변동성 분석)

  • Chung, Sunah;Hwang, S.Y.
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.221-230
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    • 2016
  • This paper investigates volatilities of stock returns based on high frequency data from stock market. Incorporating the price duration as one of the factors in volatility, we employ the autoregressive conditional duration (ACD) model for the price duration in addition to the GARCH model to analyze stock volatilities. A combined ACD-GARCH model is analyzed in which a double-threshold is introduced to accommodate asymmetric features on stock volatilities.

An Empirical Study on Investment Performance using Properties of Realized Range-Based Volatility and Firm-Specific Volatility (실현범위변동성(RRV) 및 기업고유변동성의 속성과 투자성과 측정)

  • Byun, Youngtae
    • Management & Information Systems Review
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    • v.33 no.5
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    • pp.249-260
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    • 2014
  • This paper explores the relationship between firm-specific volatility and some firm characteristics such as size, the market-to-book ratio of equity, PER, PBR, PCR, PSR and turnover in KOSDAQ market. In addition, I investigate whether portfolios with difference to realized range-based volatility and firm-specific volatility have different investment performance using CAPM and FF-3 factor model. The main findings of this study can be summarized as follows. First, firm-specific volatility have mostly positive relationship between firm-specific volatility and some firm characteristics. Second, this study found that realized range-based volatility and firm-specific volatility are positively related to expected return. It means that portfolios with high idiosyncratic volatility have significantly higher expected return than portfolios with low firm-specific volatility.

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The Effect of Auditory Condition on Voice Parameter of Teacher (청각 환경이 교사의 음성 파라미터에 미치는 영향)

  • Lee Ju-Young;Baek Kwang-Hyun
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.5
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    • pp.207-212
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    • 2006
  • The purpose of this study was to compare voice parameters in auditory conditions (normal/noise/music) between a teacher group and a control group. Results of statistical analysis showed that the teacher group had higher jitter (%) and shimmer (%) values than the control group. It indicated that the teacher group had larger variations in pitch and dynamic of their voice. In the teacher group, the voice under noisy condition showed a higher value of fundamental frequency than that under normal condition. though its fundamental frequency did not show any significant difference between the noisy condition and the musical condition. In the control group, however, although the voice under noisy condition also showed a higher value of fundamental frequency than that under normal condition, its fundamental frequency was significantly different between the noisy condition and the musical condition.

Predicting The Direction of The Daily KOSPI Movement Using Neural Networks For ETF Trades (신경회로망을 이용한 일별 KOSPI 이동 방향 예측에 의한 ETF 매매)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
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    • v.10 no.4
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    • pp.1-6
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    • 2019
  • Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

Comparison of realized volatilities reflecting overnight returns (장외시간 수익률을 반영한 실현변동성 추정치들의 비교)

  • Cho, Soojin;Kim, Doyeon;Shin, Dong Wan
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.85-98
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    • 2016
  • This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.

The Relation between the Return Rate and the Volatility of Oil Market and Natural Gas Market : Focusing on the Market of US and EU (석유시장과 천연가스시장의 수익률 및 변동성 간의 관계 : 미국과 유럽 시장을 중심으로)

  • Kim, Young-Duk;Lee, Dong-Woo
    • International Area Studies Review
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    • v.14 no.1
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    • pp.99-119
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    • 2010
  • This study explores the natural gas market and the oil market in the U.S. and the European oil market. It focuses on two kinds of analyses; one is to confirm whether there is the predictive power between spot and futures within homogeneous commodity market(or inter-heterogeneous commodity market) through Granger-causality test in terms of the return rate and the volatility. The other is to examine the spot price stabilizing effect of futures price through regression analysis. When it comes to the predictive power of inter-commodity market, there was a conflicting aspect between the return rate of spot and futures. Overall, however, its statistical significance was low. With respect to the volatility, we found that the natural gas market has little influence on the oil market unlike the predictive power of oil market on natural gas market. Concerning the return rate of the predictive power within homogeneous commodity market, we found that the return rate of spot has the predictive power on futures only in the European market. In addition, we identified that there is feedback between spot and futures in the all commodity markets regarding volatility. As a result of the spot price stabilizing effect analysis of futures price, futures volatility increased the spot volatility.