고저변동성을 활용한 변동성 예측: 한국과 미국시장을 중심으로

염명훈,백재승,하정 한국금융공학회 2023 금융공학연구 Vol.22 No.2

In this study, we suggest the high-low volatility as a means of predicting future volatility. The high-low volatility well reflects the opinions of real market participants, as it is calculated based on the highs and the lows formed by investors in trading; the implied volatility inherent in the option price formed during investors’ trading process directly reflects investors’ opinions well. On the other hand, the historical volatility, which is calculated based on the average and the standard deviation of the rate of returns utilizing the underlying asset’s closing price, has limitations in well-reflecting investors’ intraday opinions. In this respect, it is expected that both the high-low volatility and the implied volatility can predominate historical volatility in predicting future volatility. As a result of an empirical analysis using the VAR and GARCH model in consideration of volatility variables in Korea and the United States to examine the variability using the high-low volatility, we found that the high-low volatility of the S&P500 at time t-1 had a significant positive (+) effect on the implied volatility of the KOSPI200 at time t. However, the effect of the S&P500’s historical volatility at time t-1 on KOSPI200’s implied volatility was unclear. This suggests that the use of high-low volatility can give more stability in managing volatility risks in the market. 본 연구에서는 미래변동성에 대한 예측 수단으로 고저변동성을 제시한다. 고저변동성은 투자자가 형성한 고점과 저점을 사용하여 산출되기 때문에 실제 시장참여자의 의견을 잘 반영하게 된다. 투자자의 거래과정에서 형성되는 옵션가격에 내재되어 있는 내재변동성은 투자자의 의견이 직접적으로 반영된 변동성이다. 하지만 역사적변동성은 기초자산의 종가를 사용한 수익률의 평균과 표준편차를 활용하여 계산된 변동성이기 때문에 투자자의 장중 의견을 반영하지 못하는 한계가 있다. 이러한 관점에서 볼 때 고저변동성이나 내재변동성의 미래변동성에 대한 예측력은 역사적변동성보다 우위를 점할 수 있을 것으로 예상된다. 고저변동성을 활용한 변동성 예측을 살펴보기 위해 한국과 미국시장의 변동성 변수를 고려하여 GARCH 모형 등을 활용한 실증분석을 수행한 결과, t-1시점의 S&P500 고저변동성은 t시점의 KOSPI200 내재변동성에 대해 유의한 양(+)의 영향을 미친다는 결과를 발견하였다. 하지만 t-1시점 S&P500 역사적변동성이 KOSPI200 내재변동성에 대해 미치는 영향력은 분명하지 않았다. 이는 변동성 관리에 고저변동성을 함께 사용할 경우 시장의 변동성 위험을 보다 안정적으로 관리할 수 있다는 점을 제시한다. 본 연구에서 사용한 고저변동성의 산출방식은 직관적이고 실제 투자전략에 활용하기 용이한 장점이 있는 반면 후속연구에서 이론의 전개에 관한 보완이 필요할 수 있다. 본 연구결과에서 제시한 고저변동성의 효용성은 투자전략의 추가적인 변동성 예측지표로서의 시사점을 제공한다는 의의가 있다.

파생상품시장 도입이 현물시장의 기본적 변동성과 일시적 변동성에 미치는 영향

유한수,이필상 대한경영학회 2002 大韓經營學會誌 Vol.15 No.2

Volatility may be defined as the sum of fundamental volatility caused by information arrival and transitory volatility caused by noise trading. This study decomposes the observed KOSPI200 volatility into fundamental volatility and transitory volatility using Kalman filtering method. This study investigates the effects of the introduction of derivatives on the KOSPI200 volatility. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatilty and transitory volatility individually. The sample period is devided into two sub-periods, before the introduction of derivatives (period A, 1990.1.3 ∼ 1996.5.2), after the derivatives (period B : 1996.5.3 ∼ 1997.10.31). Analysis showed that observed volatility is increased significantly at 5% level, fundamental volatility is increased at 10% level, and transitory volatility is increased significantly at 5% level. In particular, transitory volatility is increased more significantly than fundamental volatility. This means that noise trading by irrational investors is increased.

환율 변동성 요인 분석 및 환율 변동성이 실물경제에 미치는 영향 - 미국 통화정책 영향을 중심으로 -

최남진 한국동북아경제학회 2022 동북아경제연구 Vol.34 No.2

This study empirically analyzes factors that can affect the volatility of the domestic won/dollar exchange rate, which advocates a small open economy, focusing on the United States, a representative key currency country. In addition, the effect of exchange rate volatility on the domestic real economy was analyzed. First, the EGARCH model is estimated in order to extract won/dollar exchange rate volatility. The results show that there is a volatility clustering phenomenon in the won/dollar exchange rate volatility. Next, factors of exchange rate volatility are estimated through the regression analysis. The estimated results indicate that variables of the US currency amount and Korea-US interest rate spread show significant amounts of coefficients, and each variable increase causes the increase of won/dollar exchange rate volatility. This explains that the expansionary monetary policy of the US implemented for domestic business recovery raises won/dollar exchange rate volatility through direct currency channel and portfolio channel. On the other hand, the growth rate of the US shows significant negative coefficient, and the increase of relevant variables leads to the reduction of won/dollar exchange rate volatility. In the results of estimating the SVAR model in order to check the effects of exchange rate volatility on real economy of Korea, the shock of exchange rate volatility increase reduces trades between Korea and the US while limiting the growth rate of Korea. Through the above analysis results, it is expected that unexpected monetary policy(austerity monetary policy) of major key currency countries including the US might expand the won/dollar exchange rate volatility, which could function as an element to restrict domestic real economy.

The Impacts of Macroeconomic and Non-macroeconomic Variables on Stock Return Volatility: An Empirical Investigation of the Tourism Industry

( Hayat Benourrad ),( Hyunjoon Kim ) 한국항공경영학회 2021 한국항공경영학회 춘계학술대회 Vol.2020 No.-

Volatility within the securities market is one of the factors eroding investor trust. Understanding causes that affect stock return volatility is a vital concern for investors, researchers, and portfolio managers. The ability of investors to forecast the long-run macroeconomic and nonmacroeconomic factors that affect stock volatility is extremely important in making profitable investment decisions. Researchers have been trying to understand and predict stock volatility since the global financial crisis of 2008 and its effects on stock markets and the global economy. Theories in finance have strongly related stock volatility to changes in several macroeconomic variables such as capital markets, fluctuations in interest rates, inflation rate, exchange rate, oil prices, and gold prices. Various studies have attempted to establish relations between macroeconomic factors and stock fluctuations. Chen, Roll, and Ross (1986) were the first to use macroeconomic variables to predict stock returns in the United States, using seven macroeconomic variables: term structure, industrial productivity, risk premium, inflation, market return, consumption, and oil prices. Their results showed a positive relationship between macroeconomic variables and expected stock returns. They pointed out that when these selected macroeconomic variables are extremely volatile, they can significantly explain expected returns. Caner and Onder (2005), outline sources of stock volatility as dividend yield, exchange rate, interest rate, inflation rate, and movement of world market index. Abugri (2008); Hwang & Young (2000) identify inflation rate, interest rate, exchange rate, dividend yield, and money supply as notable factors influencing stock volatility. Volatility forecasting research has been for long a hot topic in empirical finance. Nevertheless, in the Tourism field exploring problems in volatility and their resolution across different models have received little interest and still lack proper investigations. While macro-economic factors often affect stock volatility, Uncontrollable circumstances such as pandemics, natural disasters, political events, mega events, and economic crises could have a huge impact on stock volatility. Global tourism and economics have marked a wide range of crises and disastrous events such as the September 11 terrorist attacks (2001), the global economic crisis in 2008/2009, the eruptions of Eyjafjallajökull (2010) that hit hard the equity market and contributed to high volatility. And, the recent COVID-19 crisis has impacted heavily on international travel, tourism demand, and the hospitality industry. The relationship between the tourism industry performance and uncertain non-macroeconomic factors has received limited academic coverage. This study sought to examine the impact of five macro-economic variables namely; interest rate, inflation rate, exchange rate, unemployment rate, and Oil price changes, and other five nonmacroeconomic variables namely; COVID-19 crisis, the U.S Presidential Election, the World Expo, FIFA World Cup, and the Olympics on the stock return volatility of airlines, hotels and restaurant firms in the U.S for a period of 11 years (January 2010 to December 2020). The study results showed that stock return volatility has a significant relationship with all macroeconomic variables. However, only unemployment rate has positively impacted stock return volatility. The results show also that among all non-macroeconomic variables, only the COVID-19 pandemic has a positive significant impact on the stock return volatility. It has been indicated that that the new coronavirus COVID-19 has dramatically affected the performance of the Tourism industry. Airlines, hotels, and restaurants reported a sharp decline in revenues after the emergence of the COVID-19 outbreak. Further explanation is discussed in the paper.

Support Vector Regression을 이용한 GARCH 모형의 추정과 투자전략의 성과분석

김선웅(Sun Woong Kim),최흥식(Heung Sik Choi) 한국지능정보시스템학회 2017 지능정보연구 Vol.23 No.2

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

KOSPI 200 지수 옵션시장에서 변동성 차익거래에 관한 실증분석

박형진 ( Hyoung Jin Park ) 한국금융공학회 2008 금융공학연구 Vol.7 No.1

본 연구는 2003년 4월에서 2005년 1월까지 KOSPI 200 지수 옵션시장에서 straddle 거래를 통하여 변동성 차익거래의 기회가 있었는지 살펴보았다. 거래는 일별 세 가지 미래 변동성지표를 비교하는 것으로 전략을 구성하였다. 첫 번째 변동성 지표는 CBOE에서 공시하고 있는 VIX (Volatility Index)와 같은 계산방식으로 구한 변동성 지수 두 번째는 등가격 콜과 풋 옵션의 내재변동성을 거래량으로 가중평균 하여 구한 내재변동성 그리고 마지막 지표는 과거 일별 KOSPI 200 지수 로그 수익률로 계산된 역사적 변동성이다. 이재하, 정제련 (2006)의 연구에서 KOSPI 200 지수 옵션시장에서 VIX가 미래변동성을 가장 잘 나타냄을 관찰한 것과 일치하게 VIX를 기준으로 하여 행한 거래전략의 수익이 가장 높았다. 특히 매도로 거래를 개시한 경우 모든 거래에서 이익이 났으며 이는 KOSPI 200지수 옵션시장의 내재변동성이 과도하게 높다는 일부 국내 기관 거래자들의 의견과도 부합하는 결과이다. 마지막으로 거래전략별 수익과 투자자 집단별 순매수량간의 상관관계를 살펴보았다. 결과에서 straddle 매수(매도)로 거래를 개시하여 청산시 외국인투자자만이 순매도(순매수)하는 경향이 나타났다. 이는 Kang and Park (2008)에서 관찰된 국내 투자자들이 방향성거래를 변동성 투자보다 선호하는 경향에 의한 것으로 추론된다. This study examines whether there is arbitrage volatility opportunity in KOSPI 200 index option market from April 2003 to January 2005. Volatility trading is done by comparing three volatility measures which can be used as a proxy for future realized volatility; VIX (volatility index), the volume weigheted implied volatility by using at-the-money call and put options, and historical volatility. The trading result with comparing VIX to the implied volatility is the best. Especially, every trade which is opened by short straddle is profitable. This result is consistent with Lee and Jung (2006) which shows VIX is most superior measure for estimating future realized volatility. In addition, it supports some domestic fund managers` opinion that options` implied volatilities in the market are too high. Finally, the correlation between each trade profit and investor groups` net buyings shows that only foreign traders seem to trade toward the direction of volatility trading done in this study. This is because domestic investors seem to heavily do directional trading shown in Kang and Park (2008).

아웃바운드와 인바운드 관광수요의 변동성 추정

모수원,이광배 한국호텔관광학회 2016 호텔관광연구 Vol.18 No.4

The importance of a correctly specified volatility model is clear from the range of applications requiring estimates of conditional volatilities. That is because volatility can be regarded as a measure of information flow. We employ three models of predictable volatility and present a news impact curve which characterizes the impact of past shocks on the visitor volatility implicit in a volatility model. This paper suggests several diagnostic tests based on the news impact curve and compares the GARCH model with other volatility models that allow for asymmetry in the impact of news on volatility. This paper initially regresses the change rate of tourists on a constant and the seasonal dummies to obtain a measure of unpredictable element of outbound and inbound tourists. The volatility of unpredictable element, however, displays the clustering phenomenon associated with GARCH processes. Large shocks of either sign tend to be followed by large shocks, and small shocks of either sign tend to follow small shocks. There is also some negative skewness in unconditional variance of outbound tourists and zero excess kurtosis is confidently rejected. From the Ljung-Box test statistic for twelfth-order serial correlation, we find significant serial correlation left in the unpredictable series after our adjustment procedure. The sign bias test and size bias test are highly significant. These statistics mean that GARCH-type model is appropriate for estimating volatility. We, hence, employ symmetric the GARCH model and asymmetric EGARCH and GJR models to estimate conditional heteroskedasticity. We find that GARCH and EGARCH model are appropriate for modeling the conditional volatility of outbound as well as inbound tourists, but there is a difference between them. The news impact curve estimates for outbound tourists suggests that the conditional volatility of EGARCH model is smaller than that of GARCH model and there is a little volatility difference of bad and good news. The news impact curve of inbound tourists shows that the conditional volatility of the EGARCH model is smaller that of the other models but relative to the other volatility models, the EGARCH model tend to overstate the variance for bad news and to underestimate the variance for good news.

내재변동성 측정방법에 따른 실현변동성 예측력 분석

김태혁 ( Tae Hyuck Kim ),박종해 ( Jong Hae Park ) 한국금융공학회 2006 금융공학연구 Vol.5 No.2

본 연구의 목적은 내재변동성과 시계열모형을 이용하여 측정한 변동성의 기초자산 실현변동성에 대한 에측력을 비교하는 것이다. 분석대상이 되는 내재변동성을 선택하기 위해 기존의 내재변동성의 측정방법에 관한 다양한 연구들을 정리하였다. 이를 통해, 가중방법을 달리하여 9개의 내재변동성을 측정하고, GARCH, GJR-GARCH모형을 이용하여 2개의 변동성 측정해 모두 11개의 변동성을 대상으로 하였다. 실현변동성은 일중 고빈도 데이터를 이용하여 계산하였으며, 각각의 변동성과 실현변동성과의 예측오차를 측정하는 방법으로 예측력을 비교, 분석하였다. 그 결과, CBOE에서 이용하고 있는 VIX계산 방법으로 측정한 내재변동성이 다른 내재변동성에 비해 우월한 예측결과를 보였다. 그리고, GARCH모형으로 추정한 변동성은 거래량 가중방식으로 가중한 내재변동성에 비해서는 우월한 예측결과를 보이지만 행사가격대별 가중치를 적용하여 측정한 내재변동성에 비해서는 예측력이 떨어지는 것으로 나타났다. 따라서 내재변동성과 시계열모형을 이용한 변동성간의 예측력 비교는 내재변동성의 측정방법 및 과거 데이터에 적용하는 시계열 모형에 따라서 각각 다른 예측결과를 보임을 알 수 있었다. Our Research is that Compare the implied volatility and econometircs literature for predicting realized volatility. We investigate various weighting schemes for calculating implied volatility and select implied volatility using our research. We calculate 9 implied volatilitis weighted various schemes, 2 historical volatilities using GARCH-type model and a realized volatility using high-frequency data. Next, we compare forecast accuracy between implied volatility and historical volatility using RMSE, MAE, MAPE. We find that VIX and NewVIX has strong predictive power and better than other implied volatility. GARCH-type volatility forecast better than implied volatility weighted by trading volume, but worst than any other implied volatility. According to measuring rule of implied volatility and econometrics models, predictive power is different.

Forecasting Future Volatility from Option Prices Under the Stochastic Volatility Model

Suk Joon Byun,Sol Kim,Dong Woo Rhee 한국재무학회 2009 한국재무학회 학술대회 Vol.2009 No.05

The implied volatility from Black and Scholes (1973) model has been empirically tested for the forecasting performance of future volatility and commonly shown to be biased. Based on the belief that the implied volatility from option prices is the best estimate of future volatility, this study tries to find out a better model, which can derive the implied volatility from option prices, to overcome the forecasting bias from Black and Scholes (1973) model. Heston (1993)’s model which improves on the problems of Black and Scholes (1973) model the most for pricing and hedging options is one candidate, and VIX which is the expected risk neutral value of realized volatility under the discrete version is the other. This study conducts a comparative analysis on the implied volatility from Black and Scholes (1973) model, that from Heston (1993)’s model, and VIX for the forecasting performance of future volatility. From the empirical analysis on KOSPI200 option market, it is found that Heston (1993)’s implied volatility eliminates the bias mostly which Black and Scholes (1973) implied volatility has. VIX, on the other hand, does not show any improvement for the forecasting performance.

주식시장 변동성과 채권시장 변동성

유한수 한국기업경영학회 2008 기업경영연구 Vol.15 No.2

본 연구는 투자자들의 많은 투자의 대상이 되고 있는 주식시장과 채권시장 변동성의 관계를 연구한 논문으로 관측변동성에 대해서만 연구한 기존논문들과는 차별적으로 상태공간모형에 의해 추정된 영속적 변동성과 일시적 변동성에 대해서도 분석하였다. 동시기적 상관관계분석에서는 관측변동성, 영속적 변동성의 경우에는 상관관계가 유의한 것으로 나타났으며, 일시적 변동성은 상관관계가 유의하지 않은 것으로 나타났다. 동태적 관계분석에서는 관측변동성은 시차 4, 5에서 채권시장이 주식시장을 선도하는 것으로 나타났으며, 영속적 변동성의 경우에는 서로 양방향으로 영향을 미치는 상호작용관계가 있는 것으로 나타나 변동성 중에서 추세부분이 피드백관계를 갖는 것으로 판단된다. 잡음거래로부터 발생되는 일시적 변동성의 경우에는 시차 4, 5, 6의 경우에 국고채 일시적 변동성이 KOSPI 일시적 변동성에 대해 선도관계가 있는 것으로 나타났다 The purpose of this paper is to investigate the relationship between KOSPI volatility and KTB(Korea Treasury Bond) volatility. As a distinguishing feature from prior studies, observed volatility is decomposed into permanent volatility and temporary volatility by state-space model in this paper. Permanent volatility is modelled as a random walk process becuase it is trend component. And temporary volatility is assumed as an AR(1) process because it passes away with time. This paper focuses on permanent volatility and temporary volatility, respectively. Because the coefficient of asymmetric volatility term is statistically significant, KOSPI observed volatility and KTB observed volatility are estimated by GJR GARCH model. This means that bad news have high volatility than good news. In the correlation analysis, it is found that there are statistically significant contemporaneous correlations in the cases of observed volatility and permanent volatility. But there is statistically insignificant correlation in the case of temporary volatility. To investigate the dynamic relationship between KOSPI volatility and KTB volatility, Granger causality test is carried out. I summarize the results of empirical analysis as followings. First, observed volatility of KTB Granger causes observed volatility of KOSPI for lagged days 4 and 5. Second finding is that there is bidirectional relation between permanent volatility of KOSPI and permanent volatility of KTB for lagged days 3, 4, 5, and 6. Third finding is that, in the case of temporary volatility, there is no Granger causality for lagged days 1, 2, and 3.