rewrite this as "Conclusion and Future Research" part of a paper:
The main contribution of this paper is to introduce a new stacking machine learning model for cryptocurrency volatility forecasting. The model incorporates different volatility measures that may capture the unique characteristics of cryptocurrencies. In addition, we use a combination of different regression and classification methods to forecast both the magnitude and change direction of cryptocurrency volatility.
Our model builds on the existing literature on cryptocurrency volatility forecasting by incorporating a range of regression and classification methods. By stacking multiple models, we are able to improve the accuracy and robustness of our predictions. Moreover, the use of different volatility measures helps to capture the idiosyncratic nature of cryptocurrency markets, which differ from traditional financial markets in several ways.
Our approach has several practical applications for investors and financial institutions. The ability to forecast cryptocurrency volatility with greater accuracy can help investors to make more informed investment decisions and to manage their risk exposure more effectively. Moreover, our model can be used by financial institutions to develop hedging strategies and to manage their overall exposure to cryptocurrency markets.
The current research provide different contributions in the literature of volatility and VaR forecasting, including:
	This research proposes a new approach to VaR prediction using stacking machine learning models. The final model combines the predictions from multiple base models, taking into account their individual predictions to improve overall performance.
	Due to highly volatile nature of Bitcoin returns, most of the volatility forecasting (and especially) VaR forecasting research is based on less volatile markets like stock market. However, we provide models to cope with the high complexity task of volatility and VaR forecasting in Bitcoin market.
	We use high frequency Bitcoin data as forecasting features. We will show that this can improve forecasting performance of the models significantly. 
Table 6 shows the comparison between individual and stacked models in terms of four error measures (MSE, RMSE, MAE, and SMAE) and four machine learning models (ANN, SVR, LSTM, and RF) for different response variables (RV, AbsVol, VaR_1, and VaR_5). This comparison is also charted on Figure 5 (based on MSE), Figure 6 (based on RMSE), and Figure 7 (based on MAE). Note that SAME is just a scaled version of MAE, and thus its chart is same as that of MAE. 
Based on Table 6 and also figure 5, stacked modelling has been able to reduce the error of ANN, and RF prediction of RV volatility measure based on all the error measures. For absolute change volatility measure (AbsVol), ANN and LSTM models have lower errors in terms of MSE and RMSE values. Also based on MAE and SAME, the models ANN, LSTM, and SVR can provide better results using stacking models. 
For value-at-risk prediction at 1% risk level (VaR_1), stacking has improved MSE and RMSE values of ANN, LSTM, and RF models. However, none of the stacking model led to better MAE or SMAE. This result also holds for VaR_5 prediction.
In general, different machine learning models have different behaviors when we use stacking modelling architecture. For example, ANN model is always (in our experiments) improved using stacking modelling in terms of MSE and RMSE error measures. On the other hand, the RF and SVR models are less sensitive to stacking and (in many cases) cannot be improved this way. However, Table 7 shows that the stacking modelling has been able to provide 11.63% of MSE improvement, 5.29% of RMSE improvement, and 3.04% of MAE and SAME improvement. Additionally, it seems that stacking modelling can be more reliably used for the volatility measures (RV and AbsVol) relative to value-at-risk, because of improvement of all error measures simultaneously.