91 research outputs found
Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
In this paper, we study the ability to make the short-term prediction of the
exchange price fluctuations towards the United States dollar for the Bitcoin
market. We use the data of realized volatility collected from one of the
largest Bitcoin digital trading offices in 2016 and 2017 as well as order
information. Experiments are performed to evaluate a variety of statistical and
machine learning approaches.Comment: Full version of the paper published at IEEE International Conference
on Data Mining (ICDM), 201
Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume
We study the problem of the intraday short-term volume forecasting in cryptocurrency multi-markets. The predictions are built by using transaction and order book data from different markets where the exchange takes place. Methodologically, we propose a temporal mixture ensemble, capable of adaptively exploiting, for the forecasting, different sources of data and providing a volume point estimate, as well as its uncertainty. We provide evidence of the clear outperformance of our model with respect to econometric models. Moreover our model performs slightly better than Gradient Boosting Machine while having a much clearer interpretability of the results. Finally, we show that the above results are robust also when restricting the prediction analysis to each volume quartile
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting
Bitcoin as a cryptocurrency has been one of the most important digital coins
and the first decentralized digital currency. Deep neural networks, on the
other hand, has shown promising results recently; however, we require huge
amount of high-quality data to leverage their power. There are some techniques
such as augmentation that can help us with increasing the dataset size, but we
cannot exploit them on historical bitcoin data. As a result, we propose a
shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data
using our proposed method to forecast bitcoin closing prices in a daily time
frame. We compare the performance with that of other forecasting methods, and
show that with the help of the proposed feature engineering method, a shallow
deep neural network outperforms other popular price forecasting models
Inferring short-term volatility indicators from Bitcoin blockchain
In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume
Time-varying volatility in Bitcoin market and information flow at minute-level frequency
In this paper, we analyze the time-series of minute price returns on the
Bitcoin market through the statistical models of generalized autoregressive
conditional heteroskedasticity (GARCH) family. Several mathematical models have
been proposed in finance, to model the dynamics of price returns, each of them
introducing a different perspective on the problem, but none without
shortcomings. We combine an approach that uses historical values of returns and
their volatilities - GARCH family of models, with a so-called "Mixture of
Distribution Hypothesis", which states that the dynamics of price returns are
governed by the information flow about the market. Using time-series of
Bitcoin-related tweets and volume of transactions as external information, we
test for improvement in volatility prediction of several GARCH model variants
on a minute level Bitcoin price time series. Statistical tests show that the
simplest GARCH(1,1) reacts the best to the addition of external signal to model
volatility process on out-of-sample data.Comment: 17 pages,11 figure
Applied Data Science Approaches in FinTech: Innovative Models for Bitcoin Price Dynamics
Living in a data-intensive environment is a natural consequence to the continuous innovations and technological advancements, that created countless opportunities for addressing domain-specific challenges following the Data Science approach. The main objective of this thesis is to present applied Data Science approaches in FinTech, focusing on proposing innovative descriptive and predictive models for studying and exploring Bitcoin Price Dynamics and Bitcoin Price Prediction. With reference to the research area of Bitcoin Price Dynamics, two models are proposed. The first model is a Network Vector Autoregressive model that explains the dynamics of Bitcoin prices, based on a correlation network Vector Autoregressive process that models interconnections between Bitcoin prices from different exchange markets and classical assets prices. The empirical findings show that Bitcoin prices from different markets are highly interrelated, as in an efficiently integrated market, with prices from larger and/or more connected exchange markets driving other prices. The results confirm that Bitcoin prices are unrelated with classical market prices, thus, supporting the diversification benefit property of Bitcoin. The proposed model can predict Bitcoin prices with an error rate of about 11% of the average price. The second proposed model is a Hidden Markov Model that explains the observed time dynamics of Bitcoin prices from different exchange markets, by means of the latent time dynamics of a predefined number of hidden states, to model regime switches between different price vectors, going from "bear'' to "stable'' and "bear'' times. Structured with three hidden states and a diagonal variance-covariance matrix, the model proves that the first hidden state is concentrated in the initial time period where Bitcoin was relatively new and its prices were barely increasing, the second hidden state is mostly concentrated in a period where Bitcoin prices were steadily increasing, while the third hidden state is mostly concentrated in the last period where Bitcoin prices witnessed a high rate of volatility. Moreover, the model shows a good predictive performance when implemented on an out of sample dataset, compared to the same model structured with a full variance-covariance matrix. The third and final proposed model, falls within the area of Bitcoin Price Prediction. A Hybrid Hidden Markov Model and Genetic Algorithm Optimized Long Short Term Memory Network is proposed, aiming at predicting Bitcoin prices accurately, by introducing new features that are not usually considered in the literature. Moreover, to compare the performance of the proposed model to other models, a more traditional ARIMA model has been implemented, as well as a conventional Genetic Algorithm-optimized Long Short Term Memory Network. With a mean squared error of 33.888, a root mean squared error of 5.821 and a mean absolute error of 2.510, the proposed model achieves the lowest errors among all the implemented models, which proves its effectiveness in predicting Bitcoin prices
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