In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

Picking one ‘winner’ model for researching a certain phenomenon while discarding the rest implies a confidence that may misrepresent the evidence. Multimodel inference allows researchers to more accurately represent their uncertainty about which model is ‘best’. But multimodel inference,...

The CRIX (CRyptocurrency IndeX) has been constructed based on approximately 30 cryptos and captures high coverage of available market capitalisation. The CRIX index family covers a range of cryptos based on di erent liquidity rules and various model selection criteria. Details of ECRIX (Exact...

Crypto-currencies have developed a vibrant market since bitcoin, the rst crypto-currency, was created in 2009. We look at the properties of cryptocurrencies as financial assets in a broad cross-section. We discuss approaches of altcoins to generate value and their trading and information...

The S&P500 or DAX30 are important benchmarks for the financial industry. The first mimics the performance of the major US on the NYSE, AMEX and NASDAQ, while the second does the same for the German Prime Share sector. These and other indices describe different compositions of certain segments of...

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

The purpose of the paper is to present and apply the accumulative one-step-ahead prediction error (APE) not only as a method (strategy) of model selection, but also as a tool of model selection strategy (meta-selection). The APE method is compared with the information approach to model selection...

It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and...