lover represent contagion or these are due to increase in volatility during crashes and crisis, that is, heteroskedasticity.
Table 3. Parameter estimates of HAR model.
# and * mean significant at 1% and 5% levels of significance.
4.2. Volatility Spillover from Crude Oil to Agricultural Commodities over Time
We make use of moving window (of 250 observations) to re-estimate the HAR model so as to extract the spillover parameter estimates from crude oil to agricultural commodities. These time-varying spillover coefficients are plotted with 95% confidence band. We make use of robust stand errors to generate 95% confidence band. Figure 2 reports the plots for time varying volatility spillover effect from crude oil to the given agricultural commodities with 95% confidence band.
The solid line represents the volatility spillover parameter estimates and corresponding dashed lines represent the 95% confidence band. The straight lines represent whole spillover parameter estimate with 95% confidence band. Results clearly indicate that the time varying volatility spillover effect from crude oil to agricultural commodities do not remain stable and exhibit wider variations over the given time period. We observe significant jumps in volatility spillover mechanism during the period 2010-2011 for wheat; during 2008, 2010 and 2014 for corn; during 2007-08 and 2014 for cotton; and during 2008 and 2013-14 for soybeans. The structural breaks in volatility spillover mechanism are observed during the periods when time varying volatility spillover confidence band violates the whole sample volatility spillover confidence band. The periods of 2007-08 can be related to the period of global financial crisis. The period of 2010-2014 can be related to European debt crisis and wars in various Middle East countries. Overall, our findings indicate the presence of structural breaks in volatility spillover from crude oil to the agricultural commodities during various turbulent periods. These structural breaks in volatility spillover parameters can be related to the presence of contagion.
Figure 2. Time varying volatility spillover.
4.3. Do Sudden Changes in Volatility Impact the Sudden Changes in Volatility Spillover?
Next, we test whether the presence of structural breaks in volatility explains the structural breaks observed in volatility spillover from crude oil to agricultural commodities. We apply Inclan and Tiao’s  test to detect structural break dates in Log(RS) estimator. Figure 3 presents the Log(RS) of all commodities with volatility regimes. We obtain two volatility regimes in nearly all the given commodities.
Next, we incorporate the impact of these structural breaks in volatility and generate break adjusted Log(RS) estimator. Next, we apply HAR framework using moving windows to examine the impact of structural breaks in volatility on observed structural breaks in volatility spillover. Figure 4 presents the plots of time varying volatility spillover from crude oil to agricultural commodities based on the break adjusted Log(RS). Results indicate that the evolution of volatility spillover from crude oil to agricultural commodities for the break adjusted data exhibit similar pattern and characteristics as shown by Log(RS). This indicates that the structural breaks in volatility series do not explain the structural breaks in volatility spillover pattern from crude oil to agricultural commodities. This
Figure 3. Log(RS) with volatility regimes.
supports the evidence of contagion from crude oil to the given agricultural commodities during the period of crashes and crisis in markets.
4.4. Policy Implications
Volatility spillover from crude oil to agricultural commodities has several policy implications from the perspective of policy makers, government, investors, portfolio managers and risk managers. From the perspective of policy makers and
Figure 4. Time varying volatility spillover for Log(RS) adjusted for breaks.
government, predicting sudden changes in volatility spillover from crude oil prices to agricultural commodities can help in designing and implementing the subsidy measures for a particular commodity. During the periods of turbulence in crude oil prices, the structure of volatility spillover deviates from its common behaviour. This can be helpful to avoid impact of increase in commodities prices on general public of the country. The findings of the study also have implications towards portfolio management by optimally including crude oil and agricultural commodities in portfolio to get benefit of diversification. The findings of the study also have implications towards risk management in the sense of generating more accurate measure of market risk, that is, Value-at-Risk or expected shortfall measures.
The main objective of this paper is to examine what impacts the observed structural breaks in volatility spillover mechanism from crude oil to agricultural com- modities. Using daily data, we first examine the impact of volatility in crude oil on volatility of agricultural commodities (wheat, corn, cotton and soybeans) based on heterogeneous autoregressive (HAR) model for whole sample. We find evidence of significant volatility spillover from crude oil to agricultural commodities based on whole sample analysis. The period of study is influenced by various periods of turbulence and evolution of volatility spillover from crude to agricultural commodities may not remain stable. Next, we estimate time varying volatility spillover parameters from crude oil to agricultural commodities and find that indeed the volatility spillover from crude oil to agricultural commodities does not remain stable but exhibit multiple structural breaks which can be related to the evidence of contagion from crude oil to agricultural commodities. Next, we test whether the structural breaks in volatility can explain the observed structural breaks in measuring volatility spillover mechanism. Our findings indicate that the structural breaks in volatility do not explain observed structural breaks in volatility spillover which support the evidence of significant contagion from crude oil to agricultural commodities during the periods of crashes and crisis. Further research can be conducted to understand the reasons of structural breaks in volatility spillover from crude oil agricultural commodities.
Conflicts of Interest
The authors declare no conflicts of interest.
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