Technical paper/Forecasting
Machine learning in oil market volatility forecasting: the role of feature selection and forecast horizon
This paper investigates oil market volatility prediction, showing financial variables to dominate short-horizon forecasting, while macroeconomic and sentiment factors increase in importance at longer horizons
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Forecasting India’s foreign trade dynamics: evaluation of alternative forecasting models in the post-pandemic period
The authors aim to determine how India's foreign trade will change following Covid-19 and the Russia-Ukraine conflict, comparing several forecasting models and identifying that which performs best.
An entropy-based class of moving averages
The author proposes a family of maximum-entropy-based moving averages with a framework of a moving average corresponding to a risk-neutral valuation scheme for financial time series applied to generalized forms of entropy.
The impact of economic sentiment on financial portfolios during the recent turmoil
The authors investigate the influence of economic sentiment on financial portfolios during Covid-19 and the Russia-Ukraine conflict before conducting a portfolio management analysis on their data.
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.