• Journal of KIISE
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• v.42 no.8
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• pp.972-978
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• 2015

Non-volatile memory (NVM) supports both byte addressability and non-volatility. These characteristics make it feasible for NVM to be employed at any layer of the memory hierarchy such as cache, memory and disk. An interesting characteristic of NVM is that, even though it supports non-volatility, its retention capability is limited. Furthermore NVM has tradeoff between its retention capability and write latency. In this paper, we propose a novel NVM-based file cache management scheme that makes use of the limited retention capability to improve the cache performance. Experimental results with real-workloads show that our scheme can reduce access latency by up to 31% (24.4% average) compared with the conventional LRU based cache management scheme.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.9
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• pp.483-490
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• 2005

Recently, flash memory is widely used in embedded applications since it has strong points: non-volatility, fast access speed, shock resistance, and low power consumption. However, due to its hardware characteristics, it requires a software layer called FTL(flash translation layer). The main functionality of FTL is to convert logical addresses from the host to physical addresses of flash memory We present a new FTL algorithm called STAFF(State Transition Applied Fast Flash Translation Layer). Compared to the previous FTL algorithms, STAFF shows five times higher performance than basic block mapping scheme and requires less memory. We provide performance results based on our implementation of STAFF and previous FTL algorithms.

• Asia-Pacific Journal of Business
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• v.9 no.4
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• pp.85-97
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• 2018

This paper investigates factors underlying a decrease in trading values in KOSPI 200 futures/options market on the basis of the current state of the markets. Among the factors that could affect trading values in KOSPI 200 derivatives market, we focus on the market activity of underlying assets as it has an impact on the trading of financial derivatives. Trading value and volatility are designated as market activity and the empirical results confirm that the market activity of the underlying assets is significant in explaining the decrease in trading values in KOSPI 200 futures/options market. To figure out fundamental reasons of the decrease in trading values in this market, we examine mitigation of home bias and decrease in leverage incentives as they are presumed to have influence on KOSPI 200 index market. As the global and local financial environment is time-varying, the degree of home bias and the leverage demand also changes. It implies that institutional change and/or policy effort to promote the trading of KOSPI 200 financial derivatives should be made taking into account the fact that considerable portion of the change in trading values in financial derivatives market depends on the state of the market.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.171-179
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• 2021

In the international markets, financial variables can be volatile and may affect each other, especially in the crisis times. COVID-19, which began in China in 2019 and spread to many countries of the world, created a crisis not only in the global health system but also in the international financial markets and economy. The purpose of this study is to analyze the contagious effect of the COVID-19 pandemic on the volatility of selected financial variables such as Bitcoin, gold, oil price, and exchange rates and the connections between the volatilities of these variables during the pandemic. For this aim, we use the ARMA-EGARCH model to measure the impact of volatility and shocks. In other words, it is aimed to measure whether the impact of the shock on the financial variables of the contagiousness of the epidemic is also transmitted to the markets. The data was collected from secondary and daily data from September 2th 2019 to December 20th, 2020. It can be said that the findings obtained have statistically significant effects on the conditional variability of the variables. Therefore, there are findings that the shocks in the market are contaminated with each other.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.667-675
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• 2018

When a financial time series consists of daily (closing) returns, traditional volatility models such as autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) are useful to figure out daily volatilities. With high frequency returns in a day, one may adopt various multivariate GARCH techniques (MGARCH) (Tsay, Multivariate Time Series Analysis With R and Financial Application, John Wiley, 2014) to obtain intraday volatilities as long as the high frequency is moderate. When it comes to the ultra high frequency (UHF) case (e.g., one minute prices are available everyday), a new model needs to be developed to suit UHF time series in order to figure out continuous time intraday-volatilities. Aue et al. (Journal of Time Series Analysis, 38, 3-21; 2017) proposed functional GARCH (fGARCH) to analyze functional volatilities based on UHF data. This article introduces fGARCH to the readers and illustrates how to estimate fGARCH equations using UHF data of KOSPI and Hyundai motor company.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.605-614
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• 2009

TGARCH models characterized by asymmetric volatilities have been useful for analyzing various time series in financial econometrics. We are concerned with persistent volatility in the TGARCH context. Park et al. (2009) introduced I-TGARCH process exhibiting a certain persistency in volatility. This article applies I-TGARCH model to various financial time series in Korea and it is obtained that I-TGARCH provides a better fit than competing models.

• Korean Journal of Materials Research
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• v.30 no.2
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• pp.61-67
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• 2020

BiFeO₃ with perovskite structure is a well-known material that has both ferroelectric and antiferromagnetic properties called multiferroics. However, leaky electrical properties and difficulty of controlling stoichiometry due to Bi volatility and difficulty of obtaining high relative density due to high dependency on the ceramic process are issues for BiFeO₃ applications. In this work we investigated the sintering behavior of samples with different stoichiometries and sintering conditions. To understand the optimum sintering conditions, nonstoichiometric Bi_1±xFeO_3±δ ceramics and Ti-doped Bi_1.03Fe_1-4x/3Ti_xO₃ ceramics were synthesized by a conventional solid-state route. Dense single phase BiFeO₃ ceramics were successfully fabricated using a two-step sintering and quenching process. The effects of Bi volatility on microstructure were determined by Bi-excess and Ti doping. Bi-excess increased grain size, and Ti doping increased sintering temperature and decreased grain size. It should be noted that Ti-doping suppressed Bi volatility and stabilized the BiFeO₃ phase.

• Proceedings of the Korea Crystallographic Association Conference
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• 2002.11a
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• pp.19-19
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• 2002

Perovskite-type titanate dielectrics have attracted much attention in memory devices such as DRAMs or FeRAMs due to their high dielectric constants. However, low volatility of the Ba, Sr, Pb or Zr precursors with only thd ligands has limitations in obtaining high quality thin films by liquid source metal organic chemical vapor deposition (LS-MOCVD) processes. To improve the volatility of these precursors, many attempts have been made such as adding polyether ligands to satisfy the coordinative saturation. We report the synthesis of new precursors Ba(thd)₂(tmeea) and Sr(thd)₂(tmeea), where tmeea = tris[2-(2-methoxyethoxy)ethyl]amino, and LS-MOCVD of barium strontium titanate (BSTO) thin films using these precursors. Due to increased basicity of amines compared with ethers, it is expected that the nitrogen-donor ligand will make a strong bond to a metal than an analogous oxygen-donor ligand, consequently improving the volatility and thermal behavior of these precursors. Thin films of BSTO were grown on Pt(111)/SiO₂/Si(100) substrates by LS-MOCVD using a cocktail source consisting of the conventional Ti precursor Ti(thd)₂(O/sup i/Pr), and these new Ba and Sr precursors. As-grown films were characterized by XPS, SEM, XRD, XRF, and C-V and I-V measurements. BSTO films grown at 420℃ were stoichiometric barium strontium titanate with very smooth surface morphology and their dielectric constants were found to be as targe as 450. Dependence of the composition, microstructure and the electrical properties of the BSTO films on the growth temperature, annealing temperature, working pressure, and the composition of the cocktail source will be discussed.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.49-62
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• 2022

Realized volatility is well known to have long memory, strong association with other global financial markets and interdependences among macroeconomic indices such as exchange rate, oil price and interest rates. This paper proposes autoencoder factor-augmented heterogeneous autoregressive (AE-FAHAR) model for realized volatility forecasting. AE-FAHAR incorporates long memory using HAR structure, and exogenous variables into few factors summarized by autoencoder. Autoencoder requires intensive calculation due to its nonlinear structure, however, it is more suitable to summarize complex, possibly nonstationary high-dimensional time series. Our AE-FAHAR model is shown to have smaller out-of-sample forecasting error in empirical analysis. We also discuss pre-training, ensemble in autoencoder to reduce computational cost and estimation errors.