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The paper examines volatility transmission from crude oil market to agricul tural commodities like wheat, corn, cotton and soybeans. We find that the volatility transmission from crude oil to agricultural commodities exhibits sudden changes over a study period. We also examine whether the sudden changes in volatility influence the observed sudden changes in volatility trans mission from crude oil to agricultural commodities. Our results indicate the observed sudden change in volatility transmission mechanism is not influ enced by sudden changes in volatility series.

Crude oil prices have shown wider fluctuations and have experienced higher volatility in last many decades. Crude oil plays important role in industrial production, transportation, and many other sectors and indirectly influences the economy as well. The inflation-adjusted crude oil prices exhibit sudden change during 2005 due to Iraq war. The behaviour of crude oil prices experiences sudden change soon after 2005. In 2006, events like Iraq war, Israel war, Lebanon war and other geographical tensions pushed up the crude price to $75 per barrel. In 2007, the ongoing problems in Turkey, subprime crisis in the US took up the price to $92.22 per barrel. The crude oil prices reached its peak of $147.02 per barrel in mid of 2008. However, in next few months, crude oil prices exhibited heavier decline and price dropped to around $100 per barrel by the end of December 2008. During 2010, crude oil prices exhibit fluctuations between $70 and $88 per barrel. The political and macroeconomic events linked to oil producing countries like Libya, Yemen, Egypt and Bahrain again pushed the oil prices above $100 in 2011 and 2012. From 2013 onwards, high production of shale by the US, low demand of the oil in China and Europe and uninterrupted production of the oil by OPEC members put the oil price on the downturn and in 2015 it was fluctuating around $60 per barrel.

Crude oil is considered to be the most important commodity in term of its daily traded value and consumption and is known to be the life-blood of the given economy. Hence, it is important to examine the characteristics of crude oil price changes. Crude oil is part of the production function of many commodities including agricultural commodities. In one way or the other, crude oil prices also influence the price changes in agricultural commodities (Mitchell [

The prices of many important agricultural commodities have shown an upward trend during the period 2006 to mid of 2008. In the mid of 2008 when the crude oil prices were at the peak, the prices of major agricultural commodities were also at the record high level. This also highlights the presence of inter-linkages between crude oil prices and agricultural commodities prices. Moreover, the fluctuating agricultural commodities prices will always remain a cause of concern to regulators, government, consumers, and traders.

The fluctuating crude oil prices significantly influence the economy of both oil exporting and oil importing countries by impacting different sectors of the economy. The growth in commodity markets around the globe has also provided immense opportunities to global investors, speculators and traders. Now, investors and other market participants have started using commodities in their portfolios for hedging and risk management (Baffes and Hanitis [

The core objective of this study is to examine the behaviour of volatility spillover between agricultural commodities (wheat, corn, cotton and soybeans) and crude oil. We estimate the dynamic volatility spillover coefficients to highlight the evolutionary characteristics of the volatility spillover and to examine the impact of market crashes and crises on sudden changes in this evolutionary behaviour of volatility spillover. The sudden changes in volatility spillover effect may be related to the presence of contagion from crude oil to agricultural commodities. In this paper, we also test whether the sudden changes in volatility spillover from crude oil to agricultural commodities is actually contagion or not.

The rest of the paper is structured as follow: Section 2 presents a literature review. Section 3 provides data description, research methodology used in the paper and some preliminary analysis of data. Sections 4 and 5 present empirical results and final conclusions, respectively.

Various studies have been conducted to analyze the co-movements in agricultural commodities prices and crude oil prices. Using monthly data of Crude oil, copper, gold, wheat, cotton, cocoa, lumber and cocoa, Pindyck and Rotemberg [

However, Wu and Li [

Most of the previous studies examine the volatility and information spillover from crude oil to agricultural commodities. Under the influence of various crashes and crises, this interrelationship may not remain structurally stable and most of the earlier studies fail to highlight this. In this study, we highlight that the time varying volatility spillover from crude oil to agricultural commodities does not remain statistically constant but exhibit occasional sudden changes which highlights the evidence of contagion.

We use open, high, low and close prices data to estimate unbiased Rogers and Satchell [

Rogers and Satchell [_{t}, H_{t}, L_{t} and C_{t} are the opening, high, low and closing prices of an asset on day t. Define:

b t = log ( H t O t )

c t = log ( L t O t )

x t = log ( C t O t ) .

Suppose var x denotes the usual estimator of s^{2}, i.e.

var x = 1 N − 1 ∑ n = 1 N ( x n − μ ^ ) 2 (1)

where

μ ^ = 1 N ∑ n = 1 N x n .

Let u t = 2 b t − x t and v t = 2 c t − x t , define the extreme value estimator var u x and var v x :

var u x = 1 N ∑ n = 1 N ( u n 2 − x n 2 2 ) (2)

var v x = 1 N ∑ n = 1 N ( v n 2 − x n 2 2 ) . (3)

Hence the unbiased extreme value estimator of variance as proposed by Rogers and Satchell [

var u x v x = avg { var u x , var v x } = var u x + var v x 2 . (4)

In this paper, we propose the use of var u x v x in place of ε t 2 to detect struc- tural breaks in the variance of the time series.

We apply Inclan and Tiao [_{T} and N_{T} is the total number of variance changes in T observations, and 1 < k 1 < k 2 < ⋯ < k N T < T are the change points.

σ t 2 = τ 0 2 for 1 < t < κ 1

σ t 2 = τ 1 2 for κ 1 < t < κ 2

σ t 2 = τ N T 2 for κ N T < t < T .

In order to estimate the number of changes in variance and the time point of each variance shift, a cumulative sum of squares procedure is used. The cumulative sum of the squared observations from the start of the series to the k^{th} point in time is given as:

C k = ∑ t = 1 k ε t 2 (5)

where k = 1 , ⋯ , T . The D_{k} (IT) statistics is given as:

D k = ( C k C T ) − k T , k = 1 , ⋯ , T with D 0 = D T = 0 (6)

where C_{T} is the sum of squared residuals from the whole sample period.

If there are no sudden changes in the variance of the series then the D_{k} statistic oscillates around zero and when plotted against k, it looks like a horizontal line. On the other hand, if there are sudden changes in the variance of the series, then the D_{k} statistics values drift either above or below zero.

The HAR model proposed by Corsi [

Log ( R S ) t ( d ) = α 0 + α d Log ( R S ) t − 1 ( d ) + α w Log ( R S ) t − 1 ( w ) + α m Log ( R S ) t − 1 ( m ) + ε t (7)

where Log ( X _ R S ) t − 1 ( d ) is the lagged daily Log (RS) estimator of the given agricultural commodity, Log ( X _ R S ) t − 1 ( w ) = 1 5 ∑ i = 1 5 Log ( X _ R S ) t − i ( d ) is the lagged weekly volatility component and Log ( X _ R S ) t − 1 ( m ) = 1 22 ∑ i = 1 22 Log ( X _ R S ) t − i ( d ) is the

lagged monthly volatility component. We include lagged volatility component of WTI in the above model to examine the spillover effect.

Log ( X _ R S ) t ( d ) = α 0 + α d Log ( X _ R S ) t − 1 ( d ) + α w Log ( X _ R S ) t − 1 ( w ) + α m Log ( X _ R S ) t − 1 ( m ) + β d Log ( W T I _ R S ) t − 1 ( d ) + ε t . (8)

We have used MATLAB software to perform the analysis.