Technical paper/Forecasting
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
The cost of mis-specifying price impact
Expected returns can be significantly affected by the wrong use of impact models
Conditional and unconditional intraday value-at-risk models: an application to high-frequency tick-by-tick exchange-traded fund data
The authors consider conditional and unconditional intraday value-at-risk models for high-frequency exchange-traded funds, providing results useful to practitioners of high-frequency trading.
Using a skewed exponential power mixture for value-at-risk and conditional value-at-risk forecasts to comply with market risk regulation
The authors investigate a method that combines two skewed exponential power distributions and models the conditional forecasting of VaR and CVaR and is in compliance with the recent Basel framework for market risk.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
Time-varying higher moments, economic policy uncertainty and renminbi exchange rate volatility
The authors investigate how time-varying higher moments and economic policy uncertainty may be used for predicting the renminbi exchange rate volatility.
Allocating and forecasting changes in risk
This paper considers time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components.
Forecasting the realized volatility of stock markets with financial stress
This paper investigates the impact of financial stress on the predictability of the realized volatility of five stock markets
Forecasting the European Monetary Union equity risk premium with regression trees
The authors use EMU data from the period between 2000 to 2020 to forecast equity risk premium and investigate Classification and Regression Trees.
Application of the moving Lyapunov exponent to the S&P 500 index to predict major declines
The authors suggest an innovative method based in econophysics that provides early warning signs for major declines in the S&P 500 Index
Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange
This paper investigates how input window length and forecast horizon affect the predictive performance of a trading signal prediction system.
The importance of window size: a study on the required window size for optimal-quality market risk models
In this paper the authors study different moving-window lengths for value-at-risk evaluation, and also address subjectivity in choosing the window size by testing change point detection algorithms.
Regularization effect on model calibration
This paper compares two methods to calibrate two popular models that are widely used for stochastic volatility modeling (ie, the SABR and Heston models) with the time series of options written on the Nasdaq 100 index to examine the regularization effect…
Forecasting volatility and market returns using the CBOE Volatility Index and its options
This paper examines the CBOE VIX, the VIX options’ implied volatility and the smirks associated with these options.
Multi-horizon forecasting for limit order books
A multi-step path is forecast using deep learning and parallel computing
Forecasting natural gas price trends using random forest and support vector machine classifiers
In this paper, different machine learning approaches are applied to forecasting future yearly price trends in the natural gas Title Transfer Facility market in the Netherlands.
Using equity, index and commodity options to obtain forward-looking measures of equity and commodity betas and idiosyncratic variance
This paper presents a means to extract forward-looking measures of equity and commodity betas, and idiosyncratic variance.
Forecasting stock market volatility: an asymmetric conditional autoregressive range mixed data sampling (ACARR-MIDAS) model
This paper proposes an extension of the classical CARR model, the ACARR-MIDAS model, to model volatility and capture the volatility asymmetry as well as volatility persistence.
Forecasting consumer credit recovery failure: classification approaches
This study proposes an advanced credit evaluation method for nonperforming consumer loans, which may serve as a new investment opportunity in the post-pandemic era.
A fractional Brownian–Hawkes model for the Italian electricity spot market: estimation and forecasting
This paper proposes a new model for the description and forecast of gross prices of electricity in the liberalized Italian energy market via an additive two-factor model.
Zone-wide prediction of generating unit-specific power outputs for electricity grid congestion forecasts
This paper explores various statistical and statistical learning methods, with the goal of adequately predicting the on/off status and power output levels of all power plants within a control zone.