Huiwen Wang, Wenyang Huang, Shanshan Wang
arXiv 31 Mar 2021 · Econometrics · 1 citations (OpenAlex)
arXiv:2104.00581 · PDF · DOI · OpenAlex · Extracted main text
Forecasting the (open-high-low-close)OHLC data contained in candlestick chart is of great practical importance, as exemplified by applications in the field of finance. Typically, the existence of the inherent constraints in OHLC data poses great challenge to its prediction, e.g., forecasting models may yield unrealistic values if these constraints are ignored. To address it, a novel transformation approach is proposed to relax these constraints along with its explicit inverse transformation, which ensures the forecasting models obtain meaningful openhigh-low-close values. A flexible and efficient framework for forecasting the OHLC data is also provided. As an example, the detailed procedure of modelling the OHLC data via the vector auto-regression (VAR) model and vector error correction (VEC) model is given. The new approach has high practical utility on account of its flexibility, simple implementation and straightforward interpretation. Extensive simulation studies are performed to assess the effectiveness and stability of the proposed approach. Three financial data sets of the Kweichow Moutai, CSI 100 index and 50 ETF of Chinese stock market are employed to document the empirical effect of the proposed methodology.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Yin-Wong Cheung (2007) An empirical model of daily highs and lows | 0.928 | 4 | 3 | 100% |
| 2 | Javier Arroyo, Rosa Espńola, and Carlos Maté (2011) Different approaches to forecast interval time series: a comparison in finance | 0.644 | 2 | 2 | 100% |
| 3 | Norbert M Fiess and Ronald MacDonald (2002) Towards the fundamentals of technical analysis: analysing the information content of high, low and close prices | 0.511 | 2 | 1 | 100% |
| 4 | Sren Johansen (1988) Statistical analysis of cointegration vectors | 0.511 | 2 | 1 | 100% |
| 5 | Sren Johansen (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models | 0.511 | 2 | 1 | 100% |
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| 7 | Helmut Lütkepohl (2005) New introduction to multiple time series analysis | 0.511 | 2 | 1 | 100% |
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| 9 | Johannes Mager, Ulrich Paasche, and Bernhard Sick (2009) Forecasting financial time series with support vector machines based on dynamic kernels | 0.405 | 1 | 1 | 100% |
| 10 | E. L. De Faria, Marcelo P. Albuquerque, J. L. Gonzalez, J. T. P. Cav… (2009) Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods | 0.405 | 1 | 1 | 100% |
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