Forecasting and modelling the VIX using Neural Networks

This study investigates the volatility forecasting ability of neural network models. In particular, we focus on the performance of Multi-layer Perceptron (MLP) and the Long Short Term (LSTM) Neural Networks in predicting the CBOE Volatility Index (VIX). The inputs into these models includes the VIX, GARCH(1,1) fitted values and various financial and macroeconomic explanatory variables, such as the S&P 500 returns and oil price. In addition, this study segments data into two sub-periods, namely a Calm and Crisis Period in the financial market. The segmentation of the periods caters for the changes in the predictive power of the aforementioned models, given the dierent market conditions. When forecasting the VIX, we show that the best performing model is found in the Calm Period. In addition, we show that the MLP has more predictive power than the LSTM

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

Forecasting Stock Prices Volatility with Information (An ANN-GARCH Hybrid Approach)

This study compares the forecast performance of volatilities between three models for forecasting stock returns: GARCH, hybrid ANN-GARCH with only GARCH output as the ANN input, and a hybrid ANN-GARCH with information. Through the extensive evaluation, the research found out that the hybrid ANN-GARCH model with information outperforms the other two models in terms of forecasting accuracy and predictive power. This study is set to find out the improvement performance of the hybrid ANN-GARCH with information vis a vis the Univariate GARCH Keywords: Stock price forecasting, GARCH, Artificial Neural Network DOI: 10.7176/RJFA/14-17-04 Publication date:September 30th 2023

Oil price volatility and new evidence from news and Twitter

In this paper, we develop semantic-based sentiment indices through relevant news and Twitter feeds for oil market using a state-of-the-art natural language processing technique. We investigate the predictability of crude oil price volatility using the novel sentiment indices through a hybrid structure consisting of generalized autoregressive conditional heteroskedasticity and bidirectional long short-term memory models. Findings show that media sentiment considerably enhances forecasting quality and the proposed framework outperforms existing benchmark models. More importantly, we compare the predictive power of news stories with Twitter feeds and document the superiority of the news sentiment index over the counterpart. This is an important contribution as this paper is the first study that compares the impact of regular press with that of social media, as an alternative informative medium, on oil market dynamics

Time series forecasting with the WARIMAX-GARCH method

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (WARIMAX-GARCH) method, is proposed to improve predictive performance and accuracy but also to address, at least in part, the problem of unavailable EVs. Basically, the WARIMAX-GARCH method obtains Wavelet “EVs” (WEVs) from Auto-Regressive Integrated Moving Average with eXogenous variables and Generalized Auto-Regressive Conditional Heteroscedasticity (ARIMAX-GARCH) models applied to Wavelet Components (WCs) that are initially determined from the underlying time series. The WEVs are, in fact, treated by the WARIMAX-GARCH method as if they were conventional EVs. Similarly to GARCH and ARIMA-GARCH models, the WARIMAX-GARCH method is suitable for time series exhibiting non-linear characteristics such as conditional variance that depends on past values of observed data. However, unlike those, it can explicitly model frequency domain patterns in the series to help improve predictive performance. An application to a daily time series of dam displacement in Brazil shows the WARIMAX-GARCH method to remarkably outperform the ARIMA-GARCH method, as well as the (multi-layer perceptron) Artificial Neural Network (ANN) and its wavelet version referred to as Wavelet Artificial Neural Network (WANN) as in [1], on statistical measures for both in-sample and out-of-sample forecasting

Data analytics enhanced component volatility model

Volatility modelling and forecasting have attracted many attentions in both finance and computation areas. Recent advances in machine learning allow us to construct complex models on volatility forecasting. However, the machine learning algorithms have been used merely as additional tools to the existing econometrics models. The hybrid models that specifically capture the characteristics of the volatility data have not been developed yet. We propose a new hybrid model, which is constructed by a low-pass filter, the autoregressive neural network and an autoregressive model. The volatility data is decomposed by the low-pass filter into long and short term components, which are then modelled by the autoregressive neural network and an autoregressive model respectively. The total forecasting result is aggregated by the outputs of two models. The experimental evaluations using one-hour and one-day realized volatility across four major foreign exchanges showed that the proposed model significantly outperforms the component GARCH, EGARCH and neural network only models in all forecasting horizons