Nonlinear Forecasting of the Gold Miner Spread: An Application of Correlation Filters

Dunis, Christian L.; Laws, Jason; Middleton, Peter W.; Karathanasopoulos, Andreas

doi:10.1002/isaf.1345

Cited by 14 publications

(8 citation statements)

References 22 publications

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“…Hence the literature on commodity predictability is extensive and contradicting (see amongst others Chen et al (2010), Dunis et al (2013), Chinn et al (2014) and Gargano and Timmermann (2014)).…”

Section: Introductionmentioning

confidence: 99%

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Stasinakis

Psaradellis

Verousis

2016

Quantitative Finance

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In this study a Krill Herd-Support Vector Regression (KH-vSVR) model is introduced. The Krill Herd (KH) algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The KH optimizes the SVR parameters by balancing the search between local and global optima. The proposed model is applied to the task of forecasting and trading three commodity Exchange Traded Funds (ETFs) on a daily basis over the period 2012-2014. The inputs of the KH-vSVR models are selected through the Model Confidence Set (MCS) from a large pool of linear predictors. The KH-vSVR's statistical and trading performance is benchmarked against traditionally adjusted SVR structures and the best linear predictor. In addition to a simple strategy, a time-varying leverage trading strategy is applied based on Heterogeneous Autoregressive (HAR) volatility estimations. It is shown that the KH-vSVR outperforms its counterparts in terms of statistical accuracy and trading efficiency, while the leverage strategy is found to be successful.

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Section: Introductionmentioning

confidence: 99%

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Stasinakis

Psaradellis

Verousis

2016

Quantitative Finance

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“…These liquidly traded funds aim to track the price movements of the NYSE Arca Gold Miners Index (GDX), silver (SLV), and gold bullion (GLD) respectively. These ETF pairs are also used in Triantafyllopoulos and Montana (2011) and Dunis et al (2013) for their statistical and empirical studies on ETF pairs trading.…”

Section: A Pairs Trading Examplementioning

confidence: 99%

“…For instance, Triantafyllopoulos and Montana (2011) investigate the mean-reverting spreads between commodity ETFs and design model for statistical arbitrage. Dunis et al (2013) also examine the mean-reverting spread between physical gold and gold equity ETFs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Mean Reversion Trading With Transaction Costs and Stop-Loss Exit

Leung

2015

Int. J. Theor. Appl. Finan.

107

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Motivated by the industry practice of pairs trading, we study the optimal timing strategies for trading a mean-reverting price spread. An optimal double stopping problem is formulated to analyze the timing to start and subsequently liquidate the position subject to transaction costs. Modeling the price spread by an Ornstein-Uhlenbeck process, we apply a probabilistic methodology and rigorously derive the optimal price intervals for market entry and exit. As an extension, we incorporate a stop-loss constraint to limit the maximum loss. We show that the entry region is characterized by a bounded price interval that lies strictly above the stop-loss level. As for the exit timing, a higher stop-loss level always implies a lower optimal take-profit level. Both analytical and numerical results are provided to illustrate the dependence of timing strategies on model parameters such as transaction costs and stop-loss level.

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“…To better and properly forecast financial time series while adequately dealing with their respective nonlinear, nonstationary and noisy aspects, other workers used advanced and sophisticated intelligent systems such as gene expression programming (Karathanasopoulos, 2017), case‐based reasoning systems (Li et al ., 2013), ensemble and fusion intelligent systems (Albanis and Batchelor, 2007; Lahmiri, 2014a, 2018a; Sun, 2012), artificial neural networks (Aragonés et al ., 2007; Biscontri, 2012; Dunis et al ., 2013; Fadlalla and Amani, 2014; Haefke and Helmenstein, 2002; Vojinovic et al ., 2001), hybrid neuro‐fuzzy systems (Schott and Kalita, 2011; Trinkle, 2005), hybrid systems based on artificial neural networks and econometric models (Parot et al ., 2019), and deep learning (Galeshchuk and Mukherjee, 2017; Lahmiri and Bekiros, 2019).…”

Section: Introductionmentioning

confidence: 99%

A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra‐day data

Lahmiri

2020

Intell Sys Acc Fin Mgmt

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Summary There is an abundant literature on the design of intelligent systems to forecast stock market indices. In general, the existing stock market price forecasting approaches can achieve good results. The goal of our study is to develop an effective intelligent predictive system to improve the forecasting accuracy. Therefore, our proposed predictive system integrates adaptive filtering, artificial neural networks (ANNs), and evolutionary optimization. Specifically, it is based on the empirical mode decomposition (EMD), which is a useful adaptive signal‐processing technique, and ANNs, which are powerful adaptive intelligent systems suitable for noisy data learning and prediction, such as stock market intra‐day data. Our system hybridizes intrinsic mode functions (IMFs) obtained from EMD and ANNs optimized by genetic algorithms (GAs) for the analysis and forecasting of S&P500 intra‐day price data. For comparison purposes, the performance of the EMD‐GA‐ANN presented is compared with that of a GA‐ANN trained with a wavelet transform's (WT's) resulting approximation and details coefficients, and a GA‐general regression neural network (GRNN) trained with price historical data. The mean absolute deviation, mean absolute error, and root‐mean‐squared errors show evidence of the superiority of EMD‐GA‐ANN over WT‐GA‐ANN and GA‐GRNN. In addition, it outperformed existing predictive systems tested on the same data set. Furthermore, our hybrid predictive system is relatively easy to implement and not highly time‐consuming to run. Furthermore, it was found that the Daubechies wavelet showed quite a higher prediction accuracy than the Haar wavelet. Moreover, prediction errors decrease with the level of decomposition.

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Nonlinear Forecasting of the Gold Miner Spread: An Application of Correlation Filters

Cited by 14 publications

References 22 publications

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Krill-Herd Support Vector Regression and heterogeneous autoregressive leverage: evidence from forecasting and trading commodities

Optimal Mean Reversion Trading With Transaction Costs and Stop-Loss Exit

A predictive system integrating intrinsic mode functions, artificial neural networks, and genetic algorithms for forecasting S&P500 intra‐day data

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