  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.
