• Journal of the Korean Operations Research and Management Science Society
• /
• v.36 no.1
• /
• pp.13-26
• /
• 2011

In order to explain the U-shaped pattern of autocorrelations of stock returns i.e., autocorrelations starting around 0 for short-term horizons and becoming negative and then moving toward 0 for long-term horizons, researchers suggested the use of a state-space model consisting of an I(1) permanent component and an AR(1) stationary component, where the two components are assumed to be independent. They concluded that auto-regression coefficients derived from the state-space model follow a U-shape pattern and thus there is mean-reversion in stock prices. In this paper, we show that only negative autocorrelations are feasible under the assumption that the permanent component and the stationary component are independent in the state-space model. When the two components are allowed to be correlated in the state-space model, we show that the sign of the auto-regression coefficients is not restricted as negative. Monthly return data for all NYSE stocks for the period from 1926 to 2007 support the state-space model with correlated noise processes. However, the auto-regression coefficients of the ARIMA process, equivalent to the state-space model with correlated noise processes, do not follow a U-shaped pattern, but are always positive.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.25 no.4
• /
• pp.303-310
• /
• 2020

Estimating the accurate internal state of lithium ion batteries to increase their safety and efficiency is crucial. Various algorithms are used to estimate the internal state of a lithium ion battery, such as the extended Kalman filter and sliding mode observer. A state-space model is essential in using algorithms to estimate the internal state of a battery. Two principal methods are used to express the state-space model, namely, continuous time and discrete time. In this work, the extended Kalman filter is employed to estimate the internal state of a battery. Moreover, this work presents and analyzes the estimation performance of algorithms consisting of a continuous time state-space model and a discrete time state-space model through static and dynamic profiles.

• Journal of environmental and Sanitary engineering
• /
• v.15 no.2
• /
• pp.65-74
• /
• 2000

The purpose of this study is to develop the stochastic stream water quality model for the intake station of Chung-Ju city waterworks in the Han river system. This model was based on the theory of Box-Jenkins Multiplicative ARIMA(SARIMA) and the state space model to simulate changes of water qualities. Variable of water qualities included in the model are temperature and dissolved oxygen(DO). The models development were based on the data obtained from Jan. 1990 to Dec. 1997 and followed the typical procedures of the Box-Jenkins method including identification and estimation. The seasonality of DO and temperature data to formulate for the SARIMA model are conspicuous and the period of revolution was twelve months. Both models had seasonality of twelve months and were formulates as SARIMA {TEX}$(2,1,1)(1,1,1)_{12}${/TEX} for DO and temperature. The models were validated by testing normality and independency of the residuals. The prediction ability of SARIMA model and state space model were tested using the data collected from Jan. 1998 to Oct. 1999. There were good agreements between the model predictions and the field measurements. The performance of the SARIMA model and state space model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the state space model lead to the improved accuracy.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.173-179
• /
• 2006

In order to explain a U-shape pattern of stock returns, Fama and French(1988) suggested the state-space model consisting of I(1) permanent component and AR(1) stationary component. They concluded the autoregression coefficient induced from the state-space model follow the U-shape pattern and the U-shape pattern of stock returns was due to both negative autocorrelation in returns beyond a year and substantial mean-reversion in stock market prices. However, we found negative autocorrelation is induced under the assumption that permanent and stationary noise component are independent in the state-space model. In this paper, we derive the autoregression coefficient based on ARIMA process equivalent to the state-space model without the assumption of independency. Based on the estimated parameters, we investigate the pattern of the time-varying autoregression coefficient and conclude the autoregression coefficient from the state-space model of ARIMA(1,1,1) process does not follow a U-shape pattern, but has always positive sign. We applied this result on the data of 1 month retums for all NYSE stocks for the 1926-85 period from the Center for Research in Security Prices.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.169-172
• /
• 2006

Most studies using state-space models have been conducted under the assumption of independently distributed noises in measurement and state equation without adequate verification of the assumption. To avoid the improper use of state-space model, testing the assumption prior to the parameter estimation of state-space model is very important. The purpose of this paper is to investigate the general relationship between parameters of state-space models and those of ARIMA processes. Under the assumption, we derive restricted parameter spaces of ARIMA(p,0,p-1) models with mutually different AR roots where $p\;{\le}\;5$. In addition, the results of ARIMA(p,0,p-1) case can be expanded to more general ARIMA models, such as ARIMA(p-1,0,p-1), ARIMA(p-1,1,p-1), ARIMA(p,0,p-2) and ARIMA(p-1,1,p-2).

• Communications for Statistical Applications and Methods
• /
• v.29 no.6
• /
• pp.695-708
• /
• 2022

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying 𝛽-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated 𝛽 time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.5
• /
• pp.168-175
• /
• 2002

The hydraulic acoustic attenuator fur an automotive active suspension system is so highly nonlinear and of high order that the analysis in time-domain has been performed quite little. In this paper, a state-space representation of the dynamics for a hydraulic attenuator was presented utilizing the electrical analogy. And the results of experiment were compared with those of simulation to validate the state-space model proposed. The comparison revealed that the state-space model proposed is practically applicable to estimate the dynamic responses of the hydraulic attenuator in time-domain.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.55 no.3
• /
• pp.206-216
• /
• 2019

This study is aimed to compare stock assessment models depending on how the models fit to observed data. Process-error model, Observation-error model, and Bayesian state-space model for the Korean Western coast fisheries were applied for comparison. Analytical results show that there is the least error between the estimated CPUE and the observed CPUE with the Bayesian state-space model; consequently, results of the Bayesian state-space model are the most reliable. According to the Bayesian State-space model, potential yield of fishery resources in the West Sea of Korea is estimated to be 231,949 tons per year. However, the results show that the fishery resources of West Sea have been decreasing since 1967. In addition, the amounts of stock in 2013 are assessed to be only 36% of the stock biomass at MSY level. Therefore, policy efforts are needed to recover the fishery resources of West Sea of Korea.

• Journal of Korea Water Resources Association
• /
• v.33 no.2
• /
• pp.159-167
• /
• 2000

The purpose of this study is to estimate the stochastic monthly runoff model for the Kunwi south station of Wi-stream basin in Nakdong river system. This model was based on the theory of Box-Jenkins multiplicative ARlMA and the state-space model to simulate changes of monthly runoff. The forecasting monthly runoff from the pair of estimated effective rainfall and observed value of runoff in the uniform interval was given less standard error then the analysis only by runoff, so this study was more rational forecasting by the use of effective rainfall and runoff. This paper analyzed the records of monthly runoff and effective rainfall, and applied the multiplicative ARlMA model and state-space model. For the P value of V AR(P) model to establish state-space theory, it used Ale value by lag time and VARMA model were established that it was findings to the constituent unit of state-space model using canonical correction coefficients. Therefore this paper confirms that state space model is very significant related with optimization factors of VARMA model.

• Nuclear Engineering and Technology
• /
• v.49 no.1
• /
• pp.134-140
• /
• 2017

A well-performed core power control to track load changes is crucial in pressurized water reactor (PWR) nuclear power stations. It is challenging to keep the core power stable at the desired value within acceptable error bands for the safety demands of the PWR due to the sensitivity of nuclear reactors. In this paper, a state-space model predictive control (MPC) method was applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, the MPC model, and quadratic programming (QP). The mathematical models of the reactor core were based on neutron dynamic models, thermal hydraulic models, and reactivity models. The MPC model was presented in state-space model form, and QP was introduced for optimization solution under system constraints. Simulations of the proposed state-space MPC control system in PWR were designed for control performance analysis, and the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.