• Korean Journal of Cognitive Science
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• v.11 no.3_4
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• pp.33-43
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• 2000

The distinction between short-term store (STS) and long-term store (LTS) has been made in the perspective of information processing. Memory system theorists have argued that memory could be conceived as multiple memory systems beyond the concept of a single LTS. Popular memory system models are Schacter & Tulving (994)'s multiple memory systems and Squire (987)'s the taxonomy of long-term memory. Those m models agree that amnesic patients have intact STS but impaired LTS and have preserved implicit memory. However. there is a debate about the nature of the long-term memory impairment. One model considers amnesic deficit as a selective episodic memory impairment. whereas the other sees the deficits as both episodic and semantic memory impairment. At present, it remains unclear that episodic memory should be distinguished from semantic memory in terms of retrieval operation. The distinction between declarative memory and nondeclarative memory would be the alternative way to reflect explicit memory and implicit memory. The research focused on the function of frontal lobe might give clues to the debate about the nature of LTS.

• 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.

• International Area Studies Review
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• v.14 no.2
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• pp.25-40
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1053-1061
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• 2013

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.103-110
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• 2004

본 논문에서 한국선물시장의 변동성과 수익률에 대한 장기기억의 경험적 근거를 보이기 위해 일별 수익률과 변동성에 대하여 장기기억성의 추정과 검정을 실시하였다. Geweke and Porter-Hudak(1983)의 반비모수적 추정법을 이용하여 장기기억모수를 추정하였으며 추정결과 수익률은 장기기억효과가 없었으며, 변동성에서 장기기억효과가 유의한 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1213-1229
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• 2016

Cho and Shin (2016) recently demonstrated that an integrated HAR model has a forecast advantage over the HAR model of Corsi (2009). Recalling that realized volatilities of financial assets have asymmetries, we add a leverage term to the integrated HAR model, yielding the LIHAR model. Out-of-sample forecast comparisons show superiority of the LIHAR model over the HAR and IHAR models. The comparison was made for all the 20 realized volatilities in the Oxford-Man Realized Library focusing specially on the DJIA, the S&P 500, the Russell 2000, and the KOSPI. Analysis of the realized volatility data sets reveal apparent long-memory and asymmetry. The LIHAR model takes advantage of the long-memory and asymmetry and produces better forecasts than the HAR, IHAR, LHAR models.

• The Korean Journal of Financial Studies
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• v.9 no.1
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• pp.95-118
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• 2003

한 시계열의 자기상관계수의 절대값을 시차를 무한대로 접근시켜 가면서 각 시차에 대하여 구하고 이 절대값을 모두 더한 값이 무한일 때 이 시계열은 장기기억을 가진다. 이로 인하여 장기기억 모수를 추정하는데에는 자기상관을 기본으로 한다. 표본의 자기상관과 이론적 자기상관 사이의 거리를 최소하여 추정통계량을 유도하고 있는 것이 일반적이다. 이 경우에는 정상적 과정에 한하여 적용이 가능하다. 시계열은 어느 시계열이던지 간에 이 시계열에 적합한 모형이 존재할 것이고 이 모형을 시계열에 적용하면 잔차 시계열을 얻을 수 있다. 원래 시계열의 이론적 상관 대신 원래 시계열의 잔차 시계열의 자기상관과 표본의 자기상관 사이의 거리를 최소하여 추정통계량을 얻으면 통계량의 계산이 편하고 이 추정량은 정상적 시계열과 비정상적 시계열에 다같이 적용할 수 있다. 본 논문에서는 잔차의 자기상관을 이용하여 자기회귀 분수적분 이동평균 과정의 모수 추정량을 도출한다. 그리고 이 추정 통계량에 입각하여 주가의 형성과정을 살펴보고 장기기억이 옵션가격과 포트폴리오 구성에 미치는 영향을 밝힌다.

• International Area Studies Review
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• v.12 no.2
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• pp.3-26
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• 2008

This article investigates the usefulness of the skewed Student-t distribution in modeling the long memory volatility property that might be present in the daily returns of two Australian financial series; the ASX200 stock index and AUD/USD exchange rate. For this purpose we assess the performance of FIGARCH and FIAPARCH Value-at-Risk (VaR) models based on the normal, Student-t, and skewed Student-t distribution innovations. Our results support the argument that the skewed Student-t distribution models produce more accurate VaR estimates of Australian financial markets than the normal and Student-t distribution models. Thus, consideration of skewness and excess kurtosis in asset return distributions provides appropriate criteria for model selection in the context of long memory volatility models in Australian stock and foreign exchange markets.

• Environmental and Resource Economics Review
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• v.19 no.3
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• pp.485-508
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• 2010

This paper examines the long memory property and investigates cointegration in the dynamics of crude oil markets. For these purposes, we apply the joint ARMA-FIAPARCH model with structural break and the vector error correction model (VECM) to three daily crude oil prices: Brent, Dubai and West Texas Intermediate (WTI). In all crude oil markets, the property of long memory exists in their volatility, and the ARMA-FIAPARCH model adequately captures this long memory property. In addition, the results of the cointegration test and VECM estimation indicate a bi-directional relationship between returns and the conditional variance of crude oil prices. This finding implies that the dynamics of returns affect volatility, and vice versa. These findings can be utilized for improving the understanding of the dynamics of crude oil prices and forecasting market risk for buyers and sellers in crude oil markets.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.95-126
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• 2003

This paper introduces multifractal processes and presents the empirical investigation of the multifractal asset pricing. The multifractal stock price process contains long-tails which focus on Levy-Stable distributions. The process also contains long-dependence, which is the characteristic feature of fractional Brownian motion. Multifractality introduces a new source of heterogeneity through time-varying local reqularity in the price path. This paper investigates multifractality in stock prices. After finding evidence of multifractal scaling, the multifractal spectrum is estimated via the Legendre transform. The distinguishing feature of the multifractal process is multiscaling of the return distribution's moments under time-resealing. More intensive study is required of estimation techniques and inference procedures.