Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies

Huang, Weige; Gao, Xiang

doi:10.1177/21582440231151652

Cited by 4 publications

(1 citation statement)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While some wallets and custodial services are decentralized, many still require users to entrust their funds to a centralized authority. In such cases, users are exposed to the risk of mismanagement, fraud, or insolvency of the service provider (W. Huang and Gao, 2023;Metaxas et al, 2023;Srbová et al, 2023). To minimize this risk, users should opt for non-custodial wallets or decentralized depository services, which allow them to maintain control over their private keys and funds.…”

Section: Introductionmentioning

confidence: 99%

Understanding the risks associated with wallets, depository services, trading, lending, and borrowing in the crypto space

Mikhaylov

2023

J. Infras. Policy. Dev.

View full text Add to dashboard Cite

The crypto space offers numerous opportunities for users to grow their wealth through trading, lending, and borrowing activities. However, these opportunities come with inherent risks that need to be carefully managed to protect your assets and maximize returns. By understanding the risks associated with wallets and depository services, trading, lending, and borrowing, users can make informed decisions and enjoy the benefits of the rapidly evolving world of cryptocurrencies. This review paper analyses 43 papers for the period of 2019–2023 and proposes recommendations for policy makers. The results confirm that international regulators expect national authorities to implement a regulatory framework for digital assets comparable to those that already exist for traditional finance. For national authorities, this means having and using the powers, tools and resources to regulate and oversee a growing market. Authorities should cooperate and coordinate with each other, at the national and international levels, to encourage consistency and knowledge sharing. Market operators (exchanges), service providers, exchanges and wallets, create effective risk management structures, as well as reliable mechanisms for collecting, storing, protecting and reporting data.

show abstract

Section: Introductionmentioning

confidence: 99%

Understanding the risks associated with wallets, depository services, trading, lending, and borrowing in the crypto space

Mikhaylov

2023

J. Infras. Policy. Dev.

View full text Add to dashboard Cite

show abstract

Relationship between the popularity of a platform and the price of NFT assets

An,

Mikhaylov,

Chang

2024

Finance Research Letters

View full text Add to dashboard Cite

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

Şener,

Terregrossa

2024

Sage Open

View full text Add to dashboard Cite

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO-based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO-based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

show abstract

Forecasting Bitcoin Futures: A Lasso-BMA Two-Step Predictor Selection for Investment and Hedging Strategies

Cited by 4 publications

References 60 publications

Understanding the risks associated with wallets, depository services, trading, lending, and borrowing in the crypto space

Understanding the risks associated with wallets, depository services, trading, lending, and borrowing in the crypto space

Relationship between the popularity of a platform and the price of NFT assets

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

Contact Info

Product

Resources

About