• East Asian Economic Review
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• v.20 no.3
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• pp.365-390
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• 2016

This paper quantitatively compares the intrinsic features of the daily USD-GBP exchange rates in two different periods, the 1920s and the 2010s, under the same freely floating exchange rate system. Even though the foreign exchange markets in the 1920s seem to be much less organized and developed than in the 2010s, this paper finds that both the long memory volatility property and the structural break appear to be the common intrigue features of the exchange rates in the two periods by using the FIGARCH model. In particular, the long memory volatility properties in the two periods are found to be upward biased and overstated because of the structural breaks in the exchange markets. Thus this paper applies the Adaptive-FIGARCH model to consider the long memory volatility property and the structural breaks jointly. The main finding is that the structural breaks in the exchange markets affect the long memory volatility property significantly in the two periods but the degree of the long memory volatility property in the 1920s is reduced more remarkably than in the 2010s after the structural breaks are accounted for; thus implying that the structural breaks in the foreign exchange markets in the 1920s seem to be more significant.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.983-990
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• 2006

In this paper, we study an asymmetric power fractionally integrated GARCH model and find a region on which the process is stationary ergodic and has long memory property.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.23-26
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• 2000

It is noted that many econometric time series have long-memory properties. A long-memory process, or strongly dependent process, is characterized by hyperbolic decaying autocorrelations and unbounded spectral density at the origin. Since the long-memory property can be observed by data obtained from rather a long period, there is some possibility of parameter change in the process. In this paper, we consider the estimation of change-point when there is a change in the variance of a long-memory process. The estimator is based on some reasonable statistic and the consistency is shown using Taqqu's strong reduction theorem

• East Asian Economic Review
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• v.18 no.4
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• pp.367-394
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• 2014

In this study, we examined the fractal structure of the Nikkei225, HangSeng, Shanghai Stock Exchange and Straits Times Index of Singapore. Empirical analysis was performed via non-parametric, semi-parametric long memory tests and also fractal dimension calculations. In order to avoid spurious long memory features, besides the Detrended Fluctuations Analysis (DFA), we also used Smith's (2005) modified GPH method. As for fractal dimension calculations, they were conducted via Box-Counting and Variation (p=1) tests. According to the results, while there is no long memory property in log returns of any index, we found evidence for long memory properties in the volatility of the HangSeng, the Shanghai Stock Exchange and the Straits Times Index. However, we could not find any sign of long memory in the volatility of Nikkei225 index using either the DFA or modified GPH test. Fractal dimension analysis also demonstrated that all raw index prices have fractal structure properties except for the Nikkei225 index. These findings showed that the Nikkei225 index has the most efficient market properties among these 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.

• 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.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.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.255-269
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• 2020

Purpose - This paper analyzes the market efficiency focusing on the long memory properties of the domestic futures market. By decomposing futures prices into yield and volatility and looking at the long memory properties of the time series, this study aims to understand the futures market pricing and change behavior and risks, specifically and in detail. Design/methodology/approach - This study analyzes KOSPI 200 futures, KOSDAQ 150 futures, 3 and 10-year government bond futures, US dollar futures, yen futures, and euro futures, which are among the most actively traded on the Korea Exchange. To analyze the long memory and market efficiency, we used the Variance Ratio, Rescaled-Range(R/S), Geweke and Porter-Hudak(GPH) tests as semi- parametric methods, and ARFIMA-FIGARCH model as the parametric method. Findings - It was found that all seven futures supported the efficiency market hypothesis because the property of long memory turned out not to exist in their yield curves. On the other hand, in futures volatility, all 7 futures showed long memory properties in the analysis, which means that if new information is generated in the domestic futures market and the market volatility once expanded due to the impact, it does not decrease or shrink for a long period of time, but continues to affect the volatility. Research implications or Originality - The results of this paper suggest that it can be useful information for predicting changes and risks of volatility in the domestic futures market. In particular, it was found that the long memory properties would be further strengthened in the currency futures and bond rate futures markets after the global financial crisis if the regime changes of the domestic financial market are taken into account in the analysis.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.1037-1044
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• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• International Area Studies Review
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• v.14 no.3
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• pp.62-84
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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.