Multiscale downside risk interdependence between the major agricultural commodities

Živkov, Dejan; Đurašković, Jasmina; Gajić‐Glamočlija, Marina

doi:10.1002/agr.21749

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Wavelet theory, recognized as an exceptionally effective non-linear time series analysis tool, utilizes multi-resolution analysis to decompose original data into multiple sets across different frequencies. This theory has found extensive application in domains such as financial risk and forecasting [21]. Gürbüz and S ¸ahbaz [22], have utilized Discrete Wavelet Transform (DWT) techniques to decompose financial market return series, substantiating the presence of marked disparities in volatility spillover effects between the Turkish futures and spot markets across different time scales.…”

Section: Introductionmentioning

confidence: 99%

Risk Spillovers and Hedging in the Chinese Stock Market: An Asymmetric VAR-BEKK-AGARCH Analysis

Wang,

Huang,

Wang

2023

ATAMS

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In the present investigation, the phenomena of multi-scale volatility spillovers and dynamic hedging within the Chinese stock market are scrutinized, with particular emphasis on the implications of structural breaks. The decomposition of the returns from the CSI 300 and Hang sheng index' spot and futures is achieved through the application of the Maximum Overlap Discrete Wavelet Transform (MODWT), categorizing the data into three distinct temporal scales: short-term, medium-term, and long-term. An enhancement upon the conventional VAR-BEKK-GARCH (Vector Autoregressive -Baba, Engle, Kraft, and Kroner -Generalized Autoregressive Conditional Heteroskedasticity) model is proposed, yielding the asymmetric VAR-BEKK-GARCH Model (VAR-BEKK-AGARCH), which adeptly integrates the structural break of return volatility. A comprehensive analysis is conducted to elucidate the interactions and spillovers between the CSI 300 and Hang Seng Index, as well as their respective spot and futures markets, across the various identified time scales. Concurrently, a dynamic hedging portfolio, comprised of index spot and futures, is meticulously constructed, with its performance rigorously evaluated under the influence of the different time scales. To ensure the robustness and validity of the findings, wavelet coherence and phase difference methodologies are employed as verification tools. The results unequivocally reveal a heterogeneity in the behavior of mean spillover, volatility spillover, and asymmetric spillovers between the spot and futures markets of the CSI 300 and Hang Seng Index across the diverse scales. The inclusion of a structural break in the dynamic hedge portfolio is demonstrated to confer a marked advantage over its counterpart that omits this critical factor. Particularly in the short and medium-term scenarios, the dynamically hedged portfolio, enriched by the consideration of the structural break, exhibits superior performance in comparison to the static hedge portfolio. Additionally, it is discerned that the CSI 300 Index and Hang Seng Index, along with their spot and futures components, predominantly manifest in synchrony, with no clear indication of a consistent leader-lag relationship. An intensification of correlation is observed in the long-term analysis, underscoring the utility of the spot and futures of the two indices as efficacious hedging tools.

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Section: Introductionmentioning

confidence: 99%

Risk Spillovers and Hedging in the Chinese Stock Market: An Asymmetric VAR-BEKK-AGARCH Analysis

Wang,

Huang,

Wang

2023

ATAMS

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Multi-frequency downside risk interconnectedness between soft agricultural commodities

Živkov¹,

Kuzman²,

Subic³

2023

Agric. Econ. - Czech

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Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices

Gupta,

Pierdzioch

2024

Mathematics

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Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty.

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Multiscale downside risk interdependence between the major agricultural commodities

Cited by 3 publications

References 39 publications

Risk Spillovers and Hedging in the Chinese Stock Market: An Asymmetric VAR-BEKK-AGARCH Analysis

Risk Spillovers and Hedging in the Chinese Stock Market: An Asymmetric VAR-BEKK-AGARCH Analysis

Multi-frequency downside risk interconnectedness between soft agricultural commodities

Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices

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