Technical paper/Forecasting
Modeling realized volatility with implied volatility for the EUR/GBP exchange rate
This paper concerns the application of implied volatility in modeling realized volatility in the daily, weekly and monthly horizon using high-frequency data for the EUR/GBP exchange rate.
The selection of predictive variables in aggregate hydroelectric generation models
This paper provides a method to identify the best predictive variables and the appropriate predictive indexes for an aggregate hydropower storage forecasting model. To this end, we use an entropy-based approach.
Neural network middle-term probabilistic forecasting of daily power consumption
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.
Forecasting Bitcoin returns: is there a role for the US–China trade war?
In this paper, the authors extend the related literature by examining whether the information on the US–China trade war can be used to forecast the future path of Bitcoin returns, controlling for various explanatory variables.
The impact of data aggregation and risk attributes on stress testing models of mortgage default
In this paper, the authors investigate how data aggregation and risk attributes affect the development and performance of stress testing models by studying residential mortgage loan defaults.
Volatility spillover along the supply chains: a network analysis on economic links
The analysis in this paper reveals that additional fundamental risk gets transferred along supply chains, and that suppliers are exposed to additional fundamental risk that is not captured by their market beta. Suppliers are therefore exposed to…
Range-based volatility forecasting: a multiplicative component conditional autoregressive range model
This paper proposes a multiplicative component CARR (MCCARR) model to capture the "long-memory" effect in volatility.
Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states
The authors present backtesting results for 1% and 2.5% VaR of six indexes from emerging and developed countries using several of the best-known VaR models, including generalized autoregressive conditional heteroscedasticity (GARCH), extreme value theory…
Measuring economic cycles in data
This paper determines if enough data is available for forecasting or stress testing, a better measure of data length is required.
Forecasting value-at-risk
Alvin Stroyny and Tim Wilding build a dynamic risk framework for multi-asset global portfolios
Rating migrations of US financial institutions: are different outcomes equivalent?
This study employs a competing risks approach to examine the rating migrations of US financial institutions (FIs) during the period 1984–2006.
Incorporating volatility in tolerance intervals for pair-trading strategy and backtesting
This paper incorporates volatility forecasting via the exponentially weighted moving average model into traditional tolerance limits for pair-trading strategies, and illustrates how the proposed method helps uncover arbitrage opportunities via the daily…
Ensemble models in forecasting financial markets
In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
Quantification of model risk in stress testing and scenario analysis
In this paper, the author's aim is to empirically analyze the numerical quantification of model risk, yielding exact buffers in currency amounts (for a given model uncertainty).
Range-based volatility forecasting: an extended conditional autoregressive range model
This paper proposes an extended conditional autoregressive range (EXCARR) model to describe the range-based volatility dynamics of financial assets.
Winning investment strategies based on financial crisis indicators
The aim of this paper is to create systematic trading strategies built around several financial crisis indicators, which are based on the spectral properties of market dynamics.
Systemic risk in the financial system: capital shortfalls under Brexit, the US elections and the Italian referendum
This paper uses SRISK to quantify the estimated capital shortfalls of financial institutions under three relevant stress events that occurred in 2016: Brexit, the Trump election and the Italian referendum.