Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized...

In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the...

In financial literature, Value-at-Risk (VaR) and Expected Shortfall (ES) modelling is focused on producing 1-step ahead conditional variance forecasts. The present paper provides a methodological contribution to the multi-step VaR and ES forecasting through a new adaptation of the Monte Carlo...

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...

Fractionally integrated autoregressive moving average (ARFIMA) and Heterogeneou Autoregressive (HAR) models are estimated and their ability to predict the one-trading-day-ahead CAC40 realized volatility is investigated. In particular, this paper follows three steps: (i) The optimal sampling...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series. To this end, we introduce a novel economics-driven loss function for the generator. This newly designed loss function renders GANs more suitable for a classification task, and...

We propose a methodology to perform macroeconomic stress-testing on the probability of default of a given borrowers' population (i.e., aggregate probability of default) through simulation from a vector error correction model and entropy pooling (Meucci, 2008)