Abstract
Since the financialization of commodities, portfolio investments have become an important tool for investors to diversify risks. However, due to the nonlinear fluctuations brought about by extreme events, investors face more difficulties in the choice of risk portfolio. We adopt empirical mode decomposition and STVAR model, along with the basis data of optimized original sample interval. In addition, we retain the mature research of multiscale systemic risk under frequency and divide the dimension of systemic risk into two states. When frequency is combined with states, the risk spillover center undergoes subversive changes, particularly in the longest term, and metals become the risk spillover center, substituting the energy commodity, on the condition that the compositions of extreme value add persuasive power to the perspective of long term. We proposed that the joint fluctuation of agricultural commodities and energy commodities makes the former become another important risk spillover point. For investors, holding period and portfolio both need to be considered.
Highlights
When it comes to the commodity market, early studies generally focus on fluctuations of the commodity prices from the perspective of international trade, that is, the mechanism by which imports and exports disturb commodity price and change the commodity market structure, which is a microscopic analysis of the supply and demand relationship of commodities [1, 2]
We report the literature on the study of systemic risk based on extreme value theory, mainly focusing on integrating extreme value factors into the computational systemic risk model, and we make necessary extensions to identify the characteristics of extreme value generation. en, we compile the current literature on the nonlinearity of the commodity market based on macro variables, mainly focusing on the overall effect
Taking 3 months as a group, we found that, at a significance level of 10%, more possible subsamples will be generated, which meets the need for more samples for subsequent state decomposition. e entire data can be divided into 63 subsamples which are denoted as Si, i 1, 2,..., 63, respectively. e samples that meet the definition of precursor data are the bold subsamples in Table 2, and there are 24 groups, for 72 observable months
Summary
When it comes to the commodity market, early studies generally focus on fluctuations of the commodity prices from the perspective of international trade, that is, the mechanism by which imports and exports disturb commodity price and change the commodity market structure, which is a microscopic analysis of the supply and demand relationship of commodities [1, 2]. Data with high volatility has become the main research object toward the current commodity market, and the higher the volatility, the higher the requirements for the portfolio risk control strategies [14, 15]. E extensive use of high-frequency data has allowed systemic risk in the frequency domain to gain much attention, and the trend toward diversified risk has been applied in different investment periods, just like long term and short term. We use the one-way variance decomposition to apply the precursor data widely used in earthquake prediction to identify irregular fluctuations in the commodity market, and examine the relevance of internal “system,” which makes the research more pertinent and reasonable. We examine the joint volatility between different commodity markets and provide effective decision-making guidance for investors in diversifying risks in time and portfolio. E rest of the paper is organized as follows. e section describes the recent emerging literature on commodity market, including theoretical and applied perspectives. e subsequent section describes the methodology and data, and we present the results of our empirical analysis thereafter. e final section draws the main conclusions
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