Temporal clustering of time series via threshold autoregressive models: application to commodity prices

Abstract

This study aimed to find temporal clusters for several commodity prices using the threshold non-linear autoregressive model. It is expected that the process of determining the commodity groups that are time-dependent will advance the current knowledge about the dynamics of co-moving and coherent prices, and can serve as a basis for multivariate time series analyses. The clustering of commodity prices was examined using the proposed clustering approach based on time series models to incorporate the time varying properties of price series into the clustering scheme. Accordingly, the primary aim in this study was grouping time series according to the similarity between their Data Generating Mechanisms (DGMs) rather than comparing pattern similarities in the time series traces. The approximation to the DGM of each series was accomplished using threshold autoregressive models, which are recognized for their ability to represent nonlinear features in time series, such as abrupt changes, time-irreversibility and regime-shifting behavior. Through the use of the proposed approach, one can determine and monitor the set of co-moving time series variables across the time dimension. Furthermore, generating a time varying commodity price index and sub-indexes can become possible. Consequently, we conducted a simulation study to assess the effectiveness of the proposed clustering approach and the results are presented for both the simulated and real data sets.

Cointegration between the Black Sea and Kansas City Wheat Futures: The impact of Russian invasion of Ukraine

“…The co-movement is still apparent, though not as strong, which is different still from the spot prices, where co-movement almost ceases. That said, a divergence between spot and futures prices of agricultural commodities is well documented in the literature (Ameur et al (2022), Aslan et al (2018), Bohl (2020), Brooks et al (2001), Kaldor (1983), Kawaller et al (1987), Turnovsky (1983), Wang et al (2017)). The explanation of this phenomenon lies in the seasonal dynamics of agricultural commodities' consumption, convenience yield, storage cost, thin trading, or lags in information transmission.…”

Imaging feature-based clustering of financial time series

“…GARCH parametric modeling of the time series is recognized for the ability to represent volatility in time series [ 14 ]. Aslan [ 15 ] classifies time series with nonlinear features based on a threshold Auto-regressive models. However, these methods usually regress among adjacent time points.…”

Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework

“…Moreover, the literature is orientated to precious metals such as gold. Some studies show that futures markets lead spot markets and play an important role in price discovery (e.g., Aslan et al 2018;Bopp et al 1987;Brooks et al 2001;Kawaller et al 1987;Stoll et al 1990;Talbi et al 2020). Some others reach a contradictory conclusion, showing that spot prices lead to futures prices (e.g., Pradhan et al 2020;Srinivasan 2012).…”