• Communications for Statistical Applications and Methods
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• 제16권1호
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• pp.21-29
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• 2009

Engle과 Russell (1998)의 ACD 모형은 재무학에서 가격과 거래 시간의 밀접한 관계에 대한 관심을 불러 일으켰다. ACD 모형은 GARCH 모형과의 유사성을 바탕으로 Box-Cox 변환과 충격 함수 곡선(shocks impact curve)을 적용시켜 Log ACD, Power ACD, Box-Cox ACD 등과 같은 보다 유연한 모형으로 일반화될 수 있다. 본 연구에서는 이와 같이 일반화된 ACD 모형들을 국내 주식시장에서 거래되고 있는 주식의 price duration에 적용시켜 그 성능을 비교해보고자 한다.

• 한국수자원학회논문집
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• 제35권6호
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• pp.763-777
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• 2002

본 연구의 목적은 간헐 수문사상인 시간강수계열의 구조적 특성을 고찰하여 강수량 모의발생을 위한 추계학적 모형을 개발하는 것이다. 이를 위하여 본 연구에서는 강수발생과정에 대한 추계학적 모형은 이재준과 이정식(2002)이 개발한 추계학적 모형을 이용하였으며, 강수량과정을 위하여 사상내의 시간강수량을 비정상 1차 자기회귀모형으로 기술하였다. 시간강수계열의 강수발생과정과 강수량과정을 조합하면 시간강수사상의 발생패턴과 사상기간내의 강수의 종속구조를 모의할 수 있는 시간강수계열에 대한 모의모형이 얻어지며, 이 모형의 적합성을 구명하기 위해 서울을 대상으로 하여 실적강수자료를 분석하였다. Monte Carlo 모의결과는 모형이 사상기간내의 강수강도, 지속 기간, 크기의 주변 및 조건부 분포를 잘 재현하고 있음을 보여주었다. 실적 및 모의 자료에 대한 자기상관함수도 비교적 작은 시간지체에서는 유사하였다

• Journal of the Korean Data and Information Science Society
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• 제27권5호
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• pp.1203-1214
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• 2016

비만은 그 자체가 질병이면서 다른 질병의 위험인자로 사회경제학적 요인과 관련성이 높다. 급증한 국내 비만인구에 대한 사회적 차원에서의 예방을 위하여 비만과 연관성이 있는 사회경제적 요인을 파악하는 것이 중요하다. 특히, 비만과 사회경제학적 요인간의 연관성은 성별에 따라 상이할 수 있으며 지역적 변동성 역시 존재한다. 본 논문에서는 공간적 상관성을 고려하여 비만율에 영향을 미치는 사회경제적 요인의 효과를 성별에 따라 추정하고자 한다. 공간적 상관성을 설명하기 위하여 베이지안 접근법을 기반으로 한 조건부 자기회귀모형을 고려하였다. 실증예제로 2010년 서울시 25개 자치구별 비만율 자료에 대하여 제안한 공간 모형과 공간적 상관성을 고려하지 않은 모형을 적합시켜본 결과, 공간적 상관성을 고려한 모형이 모형의 적합도와 예측력 측면에서 더 우수함을 알 수 있었다.

• ETRI Journal
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• 제35권5호
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• pp.849-858
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• 2013

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non-maneuvering and maneuvering modes, and, unlike the traditional decision-based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.

• The Journal of Asian Finance, Economics and Business
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• 제2권3호
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• pp.17-22
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• 2015

This paper examines the effect of impulsiveness of euro on Indian stock market. In order to examine the problem, we select rupee-euro exchange rates and S&P CNX NIFTY and BSE30 SENSEX to represent stock price. We select euro as it considered as second most widely used currency at the international level after dollar. The data are collected a daily basis over a period of 3-Apr-2007 to 30-Mar-2012. The statistical and time series properties of each and every variable have examined using the conventional unit root such as ADF and PP test. Adopting a generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) model, the study suggests a negative relationship between exchange rate and stock prices in India. Even though India is a major trade partner of European Union, the study couldn't find any significant statistical effect of fluctuations in Euro-rupee exchange rates on stock prices. The study also reveals that shocks to exchange rate have symmetric effect on stock prices and exchange rate fluctuations have permanent effects on stock price volatility in India.

• Journal of the Korean Data and Information Science Society
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• 제24권6호
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• pp.1531-1541
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• 2013

This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.

• Communications for Statistical Applications and Methods
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• 제31권1호
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• pp.87-104
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• 2024

Most functional magnetic resonance imaging (fMRI) studies in resting state have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant. However, increased interest has recently been in quantifying possible dynamic changes in FC during fMRI experiments. FC study may provide insight into the fundamental workings of brain networks to brain activity. In this work, we focus on the specific problem of estimating the dynamic behavior of pairwise correlations between time courses extracted from two different brain regions. We compare the sliding-window techniques such as moving average (MA) and exponentially weighted moving average (EWMA), dynamic causality with vector autoregressive (VAR) model, dynamic conditional correlation (DCC) based on volatility, and the proposed alternative methods to use differencing and recursive residuals. We investigate the properties of those techniques in a series of simulation studies. We also provide an application with major depressive disorder (MDD) patient fMRI data to demonstrate studying dynamic correlations.

• 응용통계연구
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• 제29권1호
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• pp.221-230
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• 2016

주식시장 거래에서 기록되는 고빈도 자료를 사용하여 주식 수익률에 대한 변동성을 분석하였다. 변동성을 설명할 수 있는 한 요소로 주식거래에서 불규칙한 간격으로 발생하는 가격 듀레이션을 생각할 수 있는데, 실제 자료에 ACD 모형을 사용하여 듀레이션을 추정해 보았고, ACD-GARCH 모형을 사용하여 주식 수익률과 변동성에 미치는 듀레이션의 영향을 살펴보았다. 이 과정에서 ACD 모형 추정에는 ML과 EF 방법을 적용하였고, ACD-GARCH 모형에는 이중-분계점(double-threshold)을 추가하여 평균수익률의 비대칭성 및 변동성의 비대칭성을 동시에 분석해 보았다.

• The Journal of Asian Finance, Economics and Business
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• 제9권7호
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• pp.1-15
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• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.

• Communications for Statistical Applications and Methods
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• 제27권4호
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• pp.487-500
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• 2020

Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.