# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 1 Introduction and Review of Models  
### Introduction  
Online transactions over the Internet have depended on trusted financial institutions, which are central players for safe transactions. [PERSON] [1] proposed Bitcoin as a digital currency to provide an easy method to perform online transactions. Bitcoin is a peer-to-peer cryptocurrency system, where Bitcoin transactions occur with no central players. All Bitcoin transactions are verified by the nodes of the peer-to-peer networks and added to the blockchain as the Bitcoin ledger. The information of all historical transactions and all Bitcoin clients is stored in the blackchain. That is, Bitcoin transactions are recorded in the blockchain. The value of Bitcoin is not based on the economic condition in any country and depends on only the supply and demand of the network. Thus, Bitcoin has been utilized widely as a digital currency that can be exchanged for real products or services based on the Bitcoin market value. In fact, there are various digital currencies such as Ethereum, Ripple, Stellar, etc. However, we focus only on Bitcoin because the Bitcoin market capitalization is about 50% of the total estimated digital currency capitalization at present. As the Bitcoin market has grown over the years, there have been many studies to analyze the Bitcoin market in recent years. [PERSON] [2] studied the efficiency of Bitcoin market. In an efficient market, due to the random nature of unpredictable events, variations are random. To find the inefficiency, [PERSON] employed a battery of highly powerful tests for randomness and found evidence of inefficiency. The high-frequency multifractal properties of Bitcoin were examinedin [3]. [PERSON] et al. [4] analyzed the asymmetric multifractal cross-correlations among stock market indices, commodities and Bitcoin. [PERSON] et al. [5] also investigated the time-varying long-term memory in the Bitcoin market. [PERSON] [6,7] showed that Bitcoin has a clear role in the market for portfolio management. Some researchers studied Bitcoin as an investment vehicle [8, 9, 10]. They found out that Bitcoin investment has characteristic features such as high average return and volatility. Although the volatilities of various financial indices have an important impact on the Bitcoin market, the most important factor that affects the high volatility of Bitcoin is the speculative behavior of users. In addition, there was a study on economic analyses of Bitcoin as a currency [11]. According to [PERSON] et al. [11] and [PERSON] [12], Bitcoin may not be suitable as currency since Bitcoin has high volatility. [PERSON] et al. [13] also showed that Bitcoin is used as a speculative investment due to high volatility and large returns. In practice, since the Bitcoin market has high volatility, the study on the volatility of Bitcoin has been very important. We focus on the volatility of Bitcoin in this paper. Specifically, we study the accurate methods for forecasting of Bitcoin volatility. Many researchers have investigated the analysis and prediction of Bitcoin volatility recently. [PERSON] and [PERSON] [14] analyzed asymmetric volatility effects for Bitcoin. Other studies attempted to show that Bitcoin volatility has some properties such as chaos, randomness, multi-fractality and long-range memory [15,16]. Additionally, there have been many studies on the forecasting of Bitcoin volatility. [PERSON] et al. [17] studied the prediction of Bitcoin volatility with a quantile test based on the trading volume. [PERSON] [18] investigated several GARCH family models to find the best model for Bitcoin volatility and found that the AR-CGARCH is the optimal model. [PERSON] et al. [19] provided the best fitting models based on GARCH models for volatilities of cryptocurrencies including Bitcoin. They fit 12 GARCH models to each cryptocurrency and found that IGARCH (1,1) model provides a good fit. [PERSON] et al. [20] used the GARCH-MIDAS model to improve the prediction of long-term Bitcoin volatility.