This paper presents a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several methods, i.e., HAR, ARFIMA, GARCH, LASSO, ridge regression, SVR, MLP, fuzzy...

This paper proposes a variant application of the Merton distance-to-default model by employing implied volatility and implied cost of capital to predict defaults. The proposed model's results are compared with predictions obtained from three popular models in different setups. We find that our...

This paper proposes a variant application of the Merton distance-to-default model by employing implied volatility and implied a cost of capital to forecast defaults. The proposed model's results are compared with predictions obtained from three popular models in different setups. We find that...

The purpose of the Special Issue "Quantitative Methods in Economics and Finance" of the journal Risks was to provide a collection of papers that reflect the latest research and problems of pricing complex derivates, simulation pricing, analysis of financial markets, and volatility of exchange...