• 산학경영연구
• /
• 제20권2호
• /
• pp.15-27
• /
• 2007

본 연구에서는 서울, 대전, 부산의 주택매매가격종합지수 변동성간의 상관관계에 대해 분석하였다. 기존의 연구에서는 시장에서 관찰되는 관측변동성을 이용하여 분석하였으나 본 연구에서는 통계적 방법을 이용하여 관측변동성을 내재가치의 변화에 의해 발생되는 기본적 변동성과 추종거래 등과 같은 잡음거래(noise trading)에 의해 발생되는 일시적 변동성으로 분해하여 락 변동성간의 관계를 분석하였다. 분석 결과 서울 주택매매가격 변동성과 두산 주택매매가격 변동성의 상관관계가 관측변동성 기본적 변동성, 일시적 변동성 모두 높게 나타나고 있다. 기본적 변동성의 경우는 관측변동성의 경우보다 상관관계가 놀게 나타났는데 기본적 변동성은 정보에 의해 발생하는 지속적인 변동성 부분이므로 각 시장에 공통적으로 영향을 주기 때문에 상관관계가 놀게 나타난 것으로 판단된다.

• 산학경영연구
• /
• 제19권1호
• /
• pp.81-92
• /
• 2006

본 연구에서는 기관투자자의 순매수가 주가지수 변동성에 어떤 영향을 미치는가에 대해 분석하였다. 기존의 연구들에서는 관측변동성을 가지고 분석이 이루어져 왔는데 본 연구에서는 상태공간모형과 칼만필터링을 이용하여 관측변동성을 기본적 변동성과 일시적 변동성으로 분해하여 이들 각각의 변동성에 어떠한 영향을 주었는지를 연구하였다. 분석대상기간은 2000년 1월 4일부터 2005년 6월 30일까지로 하였으며 분석대상지수는 KOSPI이다. 기관투자자 순매수와 관측변동성의 관계에 대한 분석결과 기관투자자 순매수와 관측변동성은 유의한 관계가 없는 것으로 나타났으며 상태공간모형과 칼만필터링을 이용하여 구한 기본적 변동성과 일시적 변동성에 대해 분석 결과도 기본적 변동성, 일시적 변동성 모두 기관투자자 순매수와 유의한 관계가 없는 것으로 나타났다.

• 응용통계연구
• /
• 제31권2호
• /
• pp.205-216
• /
• 2018

본 논문에서는 모형 기반 GARCH 변동성, 실현변동성(realized volatility; RV), 역사적 변동성(historical volatility), 지수가중이동평균(exponentially weighted moving average; EWMA) 등 다양한 변동성 추정 방법을 소개하고, 실현변동성에 비대칭 효과(leverage effect)를 반영한 분계점 실현변동성(threshold-asymmetric realized volatility; T-RV)을 제안하였다. 또한, 예시를 위해 KOSPI 고빈도 수익률 자료의 변동성을 분석하였다.

• 한국산학기술학회논문지
• /
• 제5권1호
• /
• pp.78-82
• /
• 2004

본 연구에서는 사이버 주식거래의 도입이 주가 변동성에 어떤 영향을 주었는가를 분석하였다. 기존 연구들이 시장에서 관찰되는 변동성을 대상으로 분석하였는데, 본 연구에서는 시장에서 관찰되는 변동성을 기본적 변동성과 일시적 변동성으로 분해하여 분석하였다. 관측변동성에 대한 분석결과 사이버 주식거래 비중이 50%를 넘어선 기간 C에서 관측변동성이 증가한 것으로 분석결과가 나타났다. 그리고 일시적 변동성은 기간 C에서 유의하게 변화하지 않은 것으로 나타났다. 즉, 관측변동성이 기간 C에서 증가한 이유가 잡음거래의 증가에 의한 현상이 아니라는 것이다 결론적으로 사이버 주식거래의 증가로 정보가 가격에 신속하게 반영되어 관측변동성이 증가한 것으로 판단할 수 있다.

• KDI Journal of Economic Policy
• /
• 제40권4호
• /
• pp.1-22
• /
• 2018

We estimate three continuous-time stochastic volatility models following the approach by Aït-Sahalia and Kimmel (2007) to compare the Korean and US stock markets. To do this, the Heston, GARCH, and CEV models are applied to the KOSPI 200 and S&P 500 Index. For the latent volatility variable, we generate and use the integrated volatility proxy using the implied volatility of short-dated at-the-money option prices. We conduct MLE in order to estimate the parameters of the stochastic volatility models. To do this we need the transition probability density function (TPDF), but the true TPDF is not available for any of the models in this paper. Therefore, the TPDFs are approximated using the irreducible method introduced in Aït-Sahalia (2008). Among three stochastic volatility models, the Heston model and the CEV model are found to be best for the Korean and US stock markets, respectively. There exist relatively strong leverage effects in both countries. Despite the fact that the long-run mean level of the integrated volatility proxy (IV) was not statistically significant in either market, the speeds of the mean reversion parameters are statistically significant and meaningful in both markets. The IV is found to return to its long-run mean value more rapidly in Korea than in the US. All parameters related to the volatility function of the IV are statistically significant. Although the volatility of the IV is more elastic in the US stock market, the volatility itself is greater in Korea than in the US over the range of the observed IV.

• 재무관리연구
• /
• 제26권2호
• /
• pp.181-201
• /
• 2009

This study is on the forecasting performance analysis of range volatility estimators(Parkinson, Garman and Klass, and Rogers and Satchell) relative to historical one using two-scale realized volatility estimator as a benchmark. American sub-prime mortgage loan shock to Korean stock markets happened in sample period(January 2, 2006~March 10, 2008), so the structural change somewhere within this period can make a huge influence on the results. Therefore sample was divided into two sub-samples by May 30, 2007 according to Zivot and Andrews unit root test results. As expected, the second sub-sample was much more volatile than the first sub-sample. As a result of forecasting performance analysis, Rogers and Satchell volatility estimator showed the best forecasting performance in the full sample and relatively better forecasting performance than other estimators in sub-samples. Range volatility estimators showed better forecasting performance than historical volatility estimator during the period before the outbreak of structural change(the first sub-sample). On the contrary, the forecasting performance of range volatility estimators couldn't beat that of historical volatility estimator during the period after this event(the second sub-sample). The main culprit of this result seems to be the increment of range volatility caused by that of intraday volatility after structural change.

• 아태비즈니스연구
• /
• 제13권2호
• /
• pp.23-37
• /
• 2022

Purpose - The purpose of this paper is to analyze the dynamic factors of the daily FX implied volatility based on the fractional integration methods focusing on long memory feature and structural changes. Design/methodology/approach - This paper uses the daily FX implied volatility data of the EUR-USD and the JPY-USD exchange rates. For the fractional integration analysis, this paper first applies the basic ARFIMA-FIGARCH model and the Local Whittle method to explore the long memory feature in the implied volatility series. Then, this paper employs the Adaptive-ARFIMA-Adaptive-FIGARCH model with a flexible Fourier form to allow for the structural changes with the long memory feature in the implied volatility series. Findings - This paper finds statistical evidence of the long memory feature in the first two moments of the implied volatility series. And, this paper shows that the structural changes appear to be an important factor and that neglecting the structural changes may lead to an upward bias in the long memory feature of the implied volatility series. Research implications or Originality - The implied volatility has widely been believed to be the market's best forecast regarding the future volatility in FX markets, and modeling the evolution of the implied volatility is quite important as it has clear implications for the behavior of the exchange rates in FX markets. The Adaptive-ARFIMA-Adaptive-FIGARCH model could be an excellent description for the FX implied volatility series

• 자원ㆍ환경경제연구
• /
• 제11권3호
• /
• pp.397-414
• /
• 2002

This paper presents an empirical analysis of exchange rate volatility, petroleum's import price and industrial production on petroleum imports. The GARCH framework is used to measure the exchange rate volatility. One of the most appealing features of the GARCH model is that it captures the volatility clustering phenomenon. We found one long-run relationship between petroleum imports, import price, industrial production, and exchange rate volatility using Johansen's multivariate cointegration methodology. Since there exists a cointegrating vector, therefore, we employ an error correction model to examine the short-run dynamic linkage, finding that the exchange rate volatility performs a key role in the short-run. This paper also apply impulse-response functions to provide the dynamic responses of energy consumption to the exchange rate volatility. The results show that the response of energy consumption to exchange rate volatility declines at the first month and dies out very quickly.

• Journal of the Korean Data and Information Science Society
• /
• 제22권6호
• /
• pp.1199-1215
• /
• 2011

Relaxing an unrealistic assumption of a representative percolation model, this paper demonstrates that herd behavior leads to a high increase in volatility but not trading volume, in contrast with information flows that give rise to increases in both volatility and trading volume. Although detecting herd behavior has posed a great challenge due to its empirical difficulty, this paper proposes a new methodology for detecting trading days with herding. Furthermore, this paper suggests a herd-behavior-stochastic-volatility model, which accounts for herding in financial markets. Strong evidence in favor of the model specification over the standard stochastic volatility model is based on empirical application with high frequency data in the Korean equity market, strongly supporting the intuition that herd behavior causes excess volatility. In addition, this research indicates that strong persistence in volatility, which is a prevalent feature in financial markets, is likely attributed to herd behavior rather than news.

• Communications for Statistical Applications and Methods
• /
• 제23권3호
• /
• pp.203-213
• /
• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.