• 제목/요약/키워드: Stock index

검색결과 581건 처리시간 0.029초

Dynamic Spillover for the Economic Risk in Korea on Global Uncertainty

  • Jeon, Ji-Hong
    • 유통과학연구
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    • 제17권1호
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    • pp.11-19
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    • 2019
  • Purpose - We document the impact of economic policy uncertainty (EPU) in the US and China on the dynamic spillover effect of macroeconomics such as stock price, housing price in Korea. Research design, data, and methodology - We use the nine variables to analyze the effect which produces a result among the EPU indexes of the US and China on economic variables which is the consumer price index (CPI), housing purchase price composite index, housing lease price, the stock price index in banking industry, construction industry and distribution industry, and composite leading indicator from January 1995 to December 2016 with the Vector Error Correction Model. Result - The US EPU index has significantly a negative relation on the CPI, housing purchase price index, housing lease price index, the stock price index in banking industry, construction industry, and distribution industry in Korea. Conclusions - We find the dynamic effect of the EPU indexes in the US and China on the macroeconomics returns in Korea. This study has an empirical evidence that the economy market in Korea is influenced by the EPU index of the US rather than it of China. The higher EPU, the more risky the economy of in Korea.

Exploring Stock Market Variables and Weighted Market Price Index: The Case of Jordan

  • ALADWAN, Mohammad;ALMAHARMEH, Mohammad;ALSINGLAWI, Omar
    • The Journal of Asian Finance, Economics and Business
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    • 제8권3호
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    • pp.977-985
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    • 2021
  • The main aim of the study is to provide empirical evidence about the association between stock market exchange data and weighted price index. This research utilized monthly reported data from the Amman stock exchange market (ASE) and the Central Bank of Jordan (CBJ). The weighted price index was employed as the dependent variable and the independent variables were weighted price index (WPI), turnover ratio (TOR), number of trading days (NTD), price-earnings ratio (PER), and dividends yield ratio (DY). The time period of the study was from January 2015 to October 2020. The study's methodology follows a quantitative approach using the multiple regression method to test the hypotheses of the study. The final results of the study provided conclusive evidence that the market-weighted price index is strongly and positively correlated to three predetermined variables, namely; turnover ratio, price-earnings ratio, and dividend yield but no evidence was obtained for the effect of the number of trading days. The finding of the current study proved that the market price index is not only influenced by macro factors, but also by other variables assumed to not beneficial for the judgment of price index movements.

변동성위험프리미엄을 이용한 일중변동성매도전략의 수익성에 관한 연구 (Profitability of Intra-day Short Volatility Strategy Using Volatility Risk Premium)

  • 김선웅;최흥식;배민근
    • 경영과학
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    • 제27권3호
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    • pp.33-41
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    • 2010
  • A lot of researches find negative volatility risk premium in options market. We can make a trading profit by exploiting the negative volatility premium. This study proposes negative volatility risk premium hypotheses in the KOSPI 200 stock price index options market and empirically test the proposed hypotheses with intra-day short straddle strategy. This strategy sells both at-the-money call option and at-the-money put option at market open and exits the position at market close. Using MySQL 5.1, we create our database with 1 minute option price data of the KOSPI 200 index options from 2004 to 2009. Empirical results show that negative volatility risk premium exists in the KOSPI 200 stock price index options market. Furthermore, intra-day short straddle strategy consistently produces annual profits except one year.

퍼지시스템과 지식정보를 이용한 주가지수 예측 (Stock-Index Prediction using Fuzzy System and Knowledge Information)

  • 김해균;김성신
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2001년도 하계학술대회 논문집 D
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    • pp.2030-2032
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    • 2001
  • In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock, or other economic markets. Most previous experiments used multilayer perceptrons(MLP) for stock market forecasting. The Kospi 200 Index is modeled using different neural networks and fuzzy system predictions. In this paper, a multilayer perceptron architecture, a dynamic polynomial neural network(DPNN) and a fuzzy system are used to predict the Kospi 200 index. The results of prediction is compared with the root mean squared error(RMSE) and the scatter plot. Results show that both networks can be trained to predict the index. And the fuzzy system is performing slightly better than DPNN and MLP.

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Relationship between Tree Species Diversity and Carbon Stock Density in Moist Deciduous Forest of Western Himalayas, India

  • Shahid, Mohommad;Joshi, Shambhu Prasad
    • Journal of Forest and Environmental Science
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    • 제33권1호
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    • pp.39-48
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    • 2017
  • With the growing global concern about climate change, relationship between carbon stock density and tree species has become important for international climate change mitigation programmes. In this study, 150 Quadrats were laid down to assess the diversity, biomass and carbon stocks in each of the forest ranges (Barkot Range, Lachchiwala Range and Thano Range) of Dehra Dun Forest Division in Doon Valley, Western Himalaya, India. Community level carbon stock density was analyzed using Two Way Indicator Species Analysis. Species Richness and Shannon Weiner index was correlated with the carbon stocks of Doon Valley. Positive and weak relationship was found between the carbon stock density and Shannon Weiner Index, and between carbon stock density and Species Richness.

Mean-VaR Portfolio: An Empirical Analysis of Price Forecasting of the Shanghai and Shenzhen Stock Markets

  • Liu, Ximei;Latif, Zahid;Xiong, Daoqi;Saddozai, Sehrish Khan;Wara, Kaif Ul
    • Journal of Information Processing Systems
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    • 제15권5호
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    • pp.1201-1210
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    • 2019
  • Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

Capturing the Short-run and Long-run Causal Behavior of Philippine Stock Market Volatility under Vector Error Correction Environment

  • CAMBA, Abraham C. Jr.
    • The Journal of Asian Finance, Economics and Business
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    • 제7권8호
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    • pp.41-49
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    • 2020
  • This study investigates the short-run and long-run causal behavior of the Philippine stock market index volatility under vector error correction environment. The variables were tested first for stationarity and then long-run equilibrium relationship. Moreover, an impulse response function was estimated to examine the extent of innovations in the independent variables in explaining the Philippine stock market index volatility. The results reveal that the volatility of the Philippine stock market index exhibit long-run equilibrium relationship with Peso-Dollar exchange rate, London Interbank Offered Rate, and crude oil prices. The short-run dynamics-based VECM estimates indicate that in the short-run, increases (i.e., depreciation) in Peso-Dollar exchange rate cause PSEI volatility to increase. As for the London Interbank Offered Rate, it causes increases in PSEI volatility in the short-run. The adjustment coefficients used with the long-run dynamics validates the presence of unidirectional causal long-run relationship from Peso-Dollar exchange rate, London Interbank Offered Rate, and crude oil prices to PSEI volatility, and bidirectional causal long-run relationship between PSEI volatility and London Interbank Offered Rate. The impulse response functions developed within the VECM framework demonstrate the positive and negative reactions of PSEI volatility to unanticipated Peso-Dollar exchange rate, London Interbank Offered Rate, and crude oil price shocks.

주가지수선물의 헤징거래 (Hedging Transaction in the Stock Index Futures)

  • 윤석곤
    • 한국컴퓨터정보학회논문지
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    • 제3권4호
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    • pp.139-144
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    • 1998
  • 국내 자본시장의 개방으로 주가변동에 따른 위험분산 외국의 단기성자금에 의한 국내증권시장의 교란을 억제하고 투자활성화를 위해 도입된 주가지수선물의 헤징은 다른 종류의 금융선물 및 상품선물거래 도입을 촉진하게 될 것이고 이는 결국 국내 금융기관 국제경쟁력을 높이고 우리 금융시장 선진화를 앞당기는데 기여할 것이다. 또한 위험분산기능과가격발전기능을 통해 경제안정과 경제활동 원활화에도 큰 도움을 줄 것으로 기대된다. 결국주가지수선물시대가 열림에 따라 국내 주식시장에 따라 지수편입종목의 거래량 확대, 선물지수의 변동으로 초래될 주식시장의 변화에 대해서도 보다 높은 관심을 가져야 할 것으로 판단된다.

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거시경제변수의 호텔·레저 주가지수에 대한 정보이전효과에 관한 연구 (Information Spillover Effects from Macroeconomic Variables to Hotel·Leisure Stock Index)

  • 김수경;유서영;변영태
    • 한국조리학회지
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    • 제22권3호
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    • pp.212-223
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    • 2016
  • 본 연구의 목적은 거시경제변수의 수익률 및 변동성이 호텔 레저 주가지수 수익률 및 변동성에 대해 정보이전효과가 존재하는 지에 대해 알아보는 것이다. 실증분석을 위해 2000년 1월 4일부터 2015년 12월 31일까지 자료가 사용되었다. 연구의 주요 결과는 다음과 같다. 첫째, 시간가변 AR(1)-GARCH(1,1) 모형을 이용하여 분석한 결과, 거시경제변수으로부터 호텔 레저 주가지수로 수익률 및 변동성의 이전효과는 통계적으로 존재하지 않는 것으로 나타났다. 둘째, 환율(KOSPI)과 호텔 레저 주가지수의 수익률 간에는 음(양)의 관계를 가지는 것으로 나타났다. 마지막으로 원유(금리)와 호텔 레저 주가지수의 변동성 간에는 양(음)의 관계를 가지는 것으로 관측되었다.

은닉 마르코프 모델을 이용한 국가별 주가지수 예측 (A hidden Markov model for predicting global stock market index)

  • 강하진;황범석
    • 응용통계연구
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    • 제34권3호
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    • pp.461-475
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    • 2021
  • 은닉 마르코프 모델(hidden Markov model, HMM)은 은닉된 상태와 관찰 가능한 결과의 두 가지 요소로 이루어진 통계적 모형으로 확률론적 접근이 가능하고, 다양한 수학적인 구조를 가지고 있어 여러 분야에서 활발하게 사용되고 있다. 특히 금융 분야의 시계열 데이터에 응용되어 다양한 연구가 진행되고 있다. 본 연구는 HMM 이론을 국내 KOSPI200 주가지수와 더불어 NIKKEI225, HSI, S&P500, FTSE100과 같은 해외 주가지수 예측에 적용해 보고자 한다. 또한, 최근 인공지능 분야의 발전으로 인해 주식 가격 예측에 빈번하게 사용되는 서포트 벡터 회귀(support vector regression, SVR) 결과와 어떤 차이가 있는지 비교하여 살펴보고자 한다.