Artificial Neural Network Based Volatility Forecasting

Ormoneit & Neuneier (1996) used an MLP and a density estimating ANN to forecasting the volatility of the DAX Index. Their results showed that the density estimating ANN leads to better performance when compared to that of MLP. Donaldson & Kamstra (1997) proposed the use of ANNs combined with the GARCH model for capturing the impact of volatility on stock returns, applying their models to the daily log-returns of the S&P500, TSEC, Nikkei 225 and the FTSE100 Index spanning a sample period from January 1st, 1969 to December 31st, 1990. In sample and out-of-sample evaluation show that the ANN models capture volatility effects that are not captured by GARCH, EGARCH or GJR models. Additionally, they also document important differences between volatility in international markets, such as the substantial persistence of volatility effects in Japan relative to North American and European markets. Hamid & Iqbal (2004) predicted the volatility of commodities using an ANN. Then compare their results to the IV obtained by using the Barone-Adesi and Whaley options pricing model (1987) to contrast both

predictions with the actual volatility. The results indicate that the predictions made by the ANN substantially improved the predictions obtained through the IV. Aragonés et al.

(2008) analyse the use of ANNs to improve ‘traditional’ volatility forecasts models, as well as IV obtained from options on futures on the Spanish stock market index (IBEX‐35). They explore the predictive ability of ANNs that incorporate both IV and historical time‐ series data. Their results show that the general regression ANN forecasts improve the information content of IV and enhance the predictive ability of the models. Their analysis is also consistent with the results from prior research showing that IV is an unbiased forecast of future volatility and that traditional time‐series models have lower explanatory

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power than IV. Conversely, according to Mantri, Gahan, & Nayak (2010), ANNs are no different in their ability to forecast stock market volatility compared to GARCH,

EGARCH, IGARCH, and GJR-GARCH models. Using the H,L,O,C values of two Indian stock indexes (BSE and NIFTY) spanning 1995 to 2008, the ANN forecasts are less volatile but using analysis of variance (ANOVA) to compare the annual observations; no difference could be found. Mohsen et al. (2010) reported that GRNN outperformed GARCH (1,1) regarding Root Mean Squared Error (RMSE) in forecasting volatility of crude oil markets (Brent and WTI). Monfared & Enke (2014) use ANNs for volatility forecasting to enhance the ability of an asset allocation strategy based on the target

volatility. The target volatility level is achieved by dynamically allocating between a risky asset and a risk-free cash position. Given the limited availability of data during times of high volatility, such as during financial crises, a stability-oriented approach to compare data for the current period to historical data for a period of low volatility, providing a more abundant source of data for comparison was employed. To comparatively evaluate the proposed model, the VIX Index, HV, EWMA, and a GARCH model serve as benchmarks. Trading metrics are used to evaluate the performance of the models in forecasting

volatility. The sample period spans 2006 to 2016 using data from the Korea Composite Stock Price Index 200 (KOSPI 200). They concluded that ANN-based asset allocation strategies based on target volatility perform better than the fixed-allocation strategy since it generates less maximum drawdown and daily loss. Monfareda & Enkeb (2015) devise a sample portfolio consisting of five stocks from the S&P 500 Index over a sample period spanning April 2007 to December 2010, implementing noise-reducing adaptive filters to improve volatility forecasting performance. Adaptive filters hold the advantage of adapting to a changing environment, a desirable feature when incorporated with a model for error reduction purposes. The authors utilised Nonlinear Autoregressive (NAR) ANNs, and although NAR-ANNs have simple structures, they are efficient error reducing models when applied to a non-stationary or random walk noise time series. An adaptive threshold filter is designed to respond to changes in the time series when the GARCH(1,1) model generates forecasting errors. The results demonstrated that the adaptive filter can forecast the noise (errors) in the GARCH(1,1) forecasts, reducing the MSE by 42.9%. Hronec (2015) used ANNs to perform a multiple-step-ahead forecast of the Nasdaq Composite Index returns and daily range-based volatility analysed in four different time horizons; 1 day, 1 week, 2 weeks and 1month. The sample period spans January 1990 to March 2015 for a total of 6339 observations. The sample period is partitioned by a ratio of 60:20:20 to take advantage of 2-fold non-exhaustive cross-validation and uses; MSE, RMSE, MAE

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and Diebold-Mariano evaluation metrics. Based on MSE the performances of all ANNs over all time horizons are superior to a simple AR(1) model. The author suggests that the ANNs superiority is a result of their non-linearity and number of lags featured in the input variables. Lahmiri (2016) models and forecasts the volatility of currency pairs. The purpose of the study is to present a simple and effective approach for predicting HV of exchange rates. The approach utilises a limited set of technical indicators as inputs to the ANNs. The data consisted of the USD/CAD and EUR/USD exchange rates. The

forecasting results show that the simple approach outperformed the traditional GARCH and EGARCH models, and also the hybrid GARCH and EGARCH ANN models regarding MAE, MSE, and Theil’s inequality coefficient. The simplicity and effectiveness of the approach demonstrates the utility of ANN in forecasting FX volatility. Pradeepkumar & Ravia (2017) present a Particle Swarm Optimization (PSO)-trained Quantile Regression Neural Network (PSOQRNN), to forecast volatility of the following time series; USD/JPY, GBP/USD, EUR/USD, Gold, Crude Oil, and the S&P500 Index. They benchmarked the PSOQRNN with that of traditional models; GARCH and three ANNs including an MLP, General Regression Neural Network (GRNN) and Quantile Regression Neural Network (QRNN). Their results indicate that the proposed PSOQRNN outperformed the competing models in terms of Mean Squared Error (MSE). Its superior performance was further verified by the Diebold–Mariano test of statistical significance. It also performed well in terms of other important measures such as directional change and Theil's Inequality Coefficient. The superior performance of the PSOQRNN can be attributed to the role played by the PSO algorithm in obtaining better solutions. Therefore, they conclude that the PSOQRNN can be used as a viable alternative in forecasting volatility. Tana, Wang & Wanga (2017) introduce a new Adaptive-Network-Based Fuzzy Inference System

(ANFIS) which adaptively adjusts fuzzy inference rules by using the Fruit Fly

Optimization Algorithm (FOA). The research data used in this study are stock data from the Shanghai Stock Exchange from January 2006 to July 2016. Input variables are

macroeconomic variables, microeconomic variables and technical indicators. Their results demonstrate their model can accurately and successfully forecast stock market volatility.

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