인공신경망-금융시계열 모형을 이용한 KOSPI 200주가지수의 변동성 예측 (Forecasting the volatility of KOSPI 200 Using NeuralNetwork-financial Time Series Model)

서지정보

· 발행기관 : 한국경영학회
· 수록지 정보 : 경영학연구 / 34권 / 3호 / 683 ~ 713페이지
· 저자명 : 노태협, 한인구, 이택호

초록

주가지수를 이용한 펀드 및 변동성을 이용한 다양한 종류의 파생상품이 개발됨에 따라 은행 및 투신사를 중심으로 리스크관리에 많은 관심이 집중되고 있다. 다양한 종류의 펀드에 대한 평가와 헷징, 그리고 투자 전략 수립을 위하여 정확한 변동성의 추정 및 예측은 리스크 관리에 있어서 핵심 사안이라고 할 수 있다. 변동성 예측을 위하여 전통적 금융 시계열 분석 기법이 주요 예측 기법으로 사용 되고 왔다. 이 논문에서는 KOSPI 200 지수를 이용하여 기존 선행 연구에서의 예측방법론간의 비교 및 금융시계열모형과 인공신경망의 통합모형을 제시한다. 변동성의 방향성 예측면에서 금융시계열의GARCH 모형이 인공신경망모형보다 우수한 성과를 나타내었으며, 반면 인공신경망 모형은 변동성의 예측정확성 면에서GARCH 모형보다 높은 예측정확도를 보여주었다. 따라서 이 논문에서는 인공신경망 모형이 다양한 금융시계열 모형(EGARCH 모형, GARCH 모형 및 EWMA 모형)과의 통합을 통하여 변동성의 방향성 및 예측정확성의 동시적 추구 가능성을 제시하고 있다.

영어초록

As various funds and derivatives are developed using KOSPI 200 index, many investment banks and investment trust get interests on risk management of KOSPI 200 index. Accurate volatility estimation and prediction is the core in risk management in which various portfolio’s pricing, hedging, and option strategy is exercised by estimating volatilities. Up to now, many financial institutions give more values on risk management, but the methodologies of it are not established systematically. Many researchers have tried to forecast volatilities more accurately using financial time series models. Historically, many papers for volatility forecasting have concentrated on the comparison between forecasting models, but these researches focus on improving the predictive power of models by integrating ANN and financial time series models.
In this paper, we first show that financial time series models, GARCH, outperforms existing ANN in forecasting the direction of volatility and that ANN model excels GARCH in reducing the precision error of the forecasted volatility by analyzing KOSPI 200 index time series data. Based on these results, this study propose the integrated model between ANN model and financial time series model to forecast volatilities of KOSPI 200 index time series.
For selecting input variables for ANN model, new variable can be extracted by the financial time series models through analyzing the KOSPI 200 domain time series statistically. Then, these newly selected input variables can enhance the predictive power in the perspectives of precision error and direction accuracy through ANN learning process. The ANN-financial time series integrated model can enhance the predictive power by comparing the forecasted volatilities by single models in the framework of precision error(MAE) and direction accuracy(hit ratio). Especially, the integrated NN-EGARCH model is proposed as most predictive integrated model in forecasting volatilities in the perspectives of precision error and hit ratio simultaneously.
In addition to prediction power, the integrated models can reduce time which it takes to adjust input variables by repetitive trial and error. Most of existing studies have adjusted the weight of raw volatilities by repetitive trial and error of learning process and found the optimal coefficient of input variables to produce the best results. This study finds the coefficients of input variables by financial time series process at once and extracts new variables that greatly influence the results through analyzing KOSPI 200 domain statistically without time consumption. At the same time, we consider market variables as those that adjust extracted variables to reflect true market behaviors. Therefore, integrated models can adjust variables realistically by ANN process and reduce time consumption by financial time series models.

참고자료

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