Earticle

본 논문은 DCC-MGARCH모형을 이용하여 중국 주식시장의 동태적 조건부 상관관계를 추정했다. 중국 주식시장과 한국, 홍콩 등 주식시장의 동태적 상관관계는 상대적으로 상승한 추세를 보이는 반면, 미국 주식시장과는 뚜렷한 추세를 보이지 않고 일정한 범위 내에서 변동이 지속되었으며, 중국 국내 주식시장 간 변동 폭이 제일 컸고, 중국 주식시장의 변동성과 금융위기가 상관관계에 미치는 영향은 Longel and Solnik(1995)의 두 주식시장 자체 변동성 확대는 상관관계의 증가를 초래한다는 결과와 Cheng(1998)의 두 주식시장의 상관관계는 침체기에 증가한다는 연구결과와 다름을 알 수 있었다. MA(1)-GARCH(1,1)-M모형을 이용하여 분석한 결과, 중국 주식시장의 변동성 확대는 주가수익률을 높이는 경향이 있다. 또한 중국 증시는 세계증시의 충격에 유의적인 반응을 보이지 않았다.

In this paper, we estimate the dynamic conditional correlation(DCC) coefficients for the stock markets of China versus U.S., Korea, and Hong Kong, following the DCC-MGARCH model of Engle(2002). We further identify the sources of cross-border co-movements of stock prices and volatility. The main empirical implications are as follows: First, the DCC estimate between the Shanghai and Shenzhen exchanges is shown to be the highest. Second, volatility and returns are positively correlated in the mainland Chinese stock markets, unlike the findings of Longin and Solnik(1995) and Cheng(1998). Third, the U.S. appears to significantly affect China in terms of stock price movements, however, with a relatively small magnitude per MA(1)-GARCH(1,1)-M model.

In this study we examine whether daily S&P 500 index volatility can be modeled parametrically as a long-memory process by extending an integrated process to a fractionally integrated one. The modified R/S test statistic and others are significant at the 1% level of significance, so we reject the null hypothesis of no long-term dependence. We have found that there is strong evidence for long memory in the series analyzed. We compare the out-of-sample forecasting performance of volatility models from 1962 to 2009. For various forecasting horizons, the long-memory FIGARCH model tends to make more accurate forecasts. Our empirical finding that the index volatility has long memory is consistent with prior evidence showing that an asset market volatility model such as plain GARCH puts too much weight on recent observations in the estimation process relative to those of the past. The forecasting model with the lowest MSFE and VaR forecast error among the models we consider is the FIGARCH model. In terms of forecasting accuracy, it dominates the widely accepted GARCH and rolling window GARCH models. We find that the White’s reality check p-values for the FIGARCH (1, 1) expanding window model reject the hypothesis that there exists a better model than the two benchmark models. The Hansen’s p-values report the same results.

The study explores a long memory conditional volatility model on international grain markets, demonstrating importance of modeling both temporal effects of volatility and long memory process. This study adopts six different volatility models, nested in an ARMA(p,q)- FIGARCH(P,D,Q), to capture dependence of grain cash price volatility and compares the performance of the six models. It also visits a related question about non-normal behaviors of grain prices and adopts the student-t density intended to account for fat-tailed properties of the data. We find suitability of the FIGARCH type models under the student-t distribution and competitiveness of the parsimonious FIGARCH(1,d,0) for modeling long memory volatility.

In this paper we review recent developments in nonlinear time series modeling on both conditional mean and conditional variance. Traditional linear model in conditional mean is referred to as ARMA(autoregressive moving average) process investigated by Box and Jenkins(1976). Nonlinear mean models such as threshold, exponential and random coefficient models are reviewed and their characteristics are explained. In terms of conditional variances, ARCH(autoregressive conditional heteroscedasticity) class is considered as typical linear models. As nonlinear variants of ARCH, diverse nonlinear models appearing in recent literature including threshold ARCH, beta-ARCH and Box-Cox ARCH models are remarked. Also, a class of unified nonlinear models are considered and parameter estimation for that class is briefly discussed.

Schafer and Shenker(2000) mentioned the one of analytic imputation technique involving conditional means. We derive an approximate moments of a variance estimate with imputed conditional means.

We evaluate out-of-sample forecasting performance of different prediction models using different estimation windows to account for a variety of statistical characteristics such as the long range dependence and the structural breaks of the process. We identify the timing of the deterministic shifts in the unconditional variance and evaluate the impact of accounting for the level shifts in the unconditional variance on out-of-sample volatility forecasting. The modified iterated cumulative sums of squares algorithm identifies two shifts in the unconditional variance for the KOSPI (Korea Composite Stock Price Index) returns. For the KOSPI returns process, the full sample performance of the recursive GARCH(1,1) model is worse than the competing models, which is unsurprising given two structural breaks in the process. The superiority of the competing models in forecasting performance can be attributed to the capability of the model which accommodates both the long range dependence by giving a slow hyperbolic rate of decaying weights on the past observations in forming the likelihood and the structural changes in the variance by discarding observations beyond a rolling window length distance in the past which may have come from a different regime. Although we try to improve the forecasting performance by incorporating statistical characteristics of the process into a prediction model, the out-of-sample performance of the prediction model can be tainted with uncertainties related to statistical tests and estimation methodologies.

Purpose: The main theme of this study is to determine the optimal control limit of conditional variance investigation by mathematical approach. According to the determination approach of control limit presented in this study, it is possible with only one parameter to calculate the control limit necessary for budgeting control system or standard costing system, in which the limit could not be set in advance, that's why it has the advantage of high practical application. Methods: This study followed the analytical methodology in terms of the decision model of information economics, Bayesian probability theory and Taguchi's quality loss function concept. Results: The function suggested by this study is as follows; ${\delta}{\leq}\frac{3}{2}(k+1)+\frac{2}{\frac{3}{2}(k+1)+\sqrt{\{\frac{3}{2}(k+1)\}^2}+4$ Conclusion: The results of this study will be able to contribute not only in practice of variance investigation requiring in the standard costing and budgeting system, but also in all fields dealing with variance investigation differences, for example, intangible services quality control that are difficult to specify tolerances (control limit) unlike tangible product, and internal information system audits where materiality standards cannot be specified unlike external accounting audits.