In financial decisions, model risk has been recognized as an important source of uncertainty. The revision of the Basel II suggests that financial institutions quantify and manage their model risk. Focusing on risk forecasting literature, we identify two main approaches to quantify model risk:...

Firm-level variables that predict cross-sectional stock returns, such as price-to-earnings and short interest, are often averaged and used to predict the time series of market returns. We extend this literature and limit the data-snooping bias by using a large population of the literature's...

Whether a bad mood enhances or hinders problem-solving and financial decision making is an open question. Using the Gallup Analytics survey, we test the depressive realism hypothesis in the earnings forecasts provided by Estimize users. The depressive realism hypothesis states that mild forms of...

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions,...

Finance researchers keep producing increasingly complex and computationally-intensive models of stock returns. Separately, professional analysts forecast stock returns daily for their clients. Are the sophisticated methods of researchers achieving better forecasts or are we better off relying on...

The purpose of this paper is to investigate whether a dynamic Value at Risk model and high frequency realized volatility models can improve the accuracy of 1-day ahead VaR forecasting beyond the performance of frequently used models. As such, this paper constructs 60 conditional volatility...

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We...

Using high-frequency transaction data, we evaluate the forecasting performance of several dynamic ordinal-response time series models with generalized autoregressive conditional heteroscedasticity. The specifications account for three components; leverage effects, in-mean effects and moving...

One of the largest financial markets in the world is the “global foreign exchange market” with average daily trades in trillions of dollars. The forex market is the backbone of international trade, global investing and is critical to support imports and exports. The exchange rate is one of...

We provide evidence suggesting that the assumption on the probability distribution for return innovations is more influential for Value at Risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the...