Volatility in Live Calf, Live Sheep, and Feed Wheat Return Markets: A Threat to Food Price Stability in Turkey

Abstract

The volatility of meat prices affects the accessibility and even food security of some consumers in Turkey. This study analyses the prices of selected livestock and a major feed component, wheat, as well as the exchange rate of the domestic currency in Turkey because imports augmented the domestic live calf and sheep supply. The analysis applies 470 price observations from January 2005 to October 2019 for each of the following price series: live calf, live sheep, feed wheat, and exchange rate of Turkish lira to US dollar. The series are analyzed by using the VAR-Asymmetric BEKK-GARCH technique. The results show that the elicited conditional variances of the return series were significantly affected by both short-term shocks and shocks across the return series. The uncertainties in the live calf, live sheep, and feed wheat markets were affected by both long-term volatilities and long-term swings in their own and the other markets, but their own market-induced effects were stronger. Similarly, the conditional variances of the returns of live calves, live sheep, and feed wheat were significantly affected by the rapid price ascent in the exchange rate and the periods of livestock imports as compared to the periods when imports were absent. The unfavorable news exerted particularly negative effects on persistent volatility in markets. Additionally, the live sheep market faced greater risks than the live calf or wheat markets and was greatly affected by the limited domestic sheep supply. Results provide knowledge useful in augmenting policy, assuring sustained accessibility to animal protein in Turkey and eliminating food insecurity.

The volatility of beef and lamb prices in Türkiye: The role of COVID–19, livestock imports, and energy prices

“…The individual ARCH-LM and multivariate ARCH-LM tests detect heteroskedasticity in the price series and indicate clustering in the ARCH residues of the series. The clustering of residuals shows that increases in return series follow increases and decreases follow decreases [39,103], supporting earlier findings [104][105][106].…”

“…Table 3 shows parameter estimates from the mean and variance equations obtained using the VAR(1)-asymmetric BEKK BGARCH model for both types of red meat. The mean equations were established in different forms of an AR model than in some earlier studies [39,102].…”

“…As a result, the model was defined as VAR(1)-asymmetric BEKK BGARCH. This model includes conditional vector autoregression equation and the bivariate GARCH in the conditional variable variance equation Based on the assumption that positive and negative shocks are of equal magnitude, the model is superior to other models because it adds asymmetric effects to the conditional variance equation and this feature has been preferred by researchers [38,39,98,99]. The VECH model, known as the vector stacking covariance matrix, has lost its attractiveness because the covariance matrix, known as H, does not meet the semi-definite positive condition and has problems with convergence in the log-maximum likelihood function with excessive parameter loading.…”

“…The calculated LB-Q and McLeod-Li test values for each return series and multivariate HM-Q statistics reject the null hypothesis that PLOS ONE the series are not autocorrelated. Results imply that increasing returns often follow increasing returns, and decreasing returns follow decreasing returns for the next reported observation [39,102]. The tests were followed by testing for heteroskedasticity (ARCH-Lagrange multiplier, hereafter ARCH-LM), and HM-Q 2 and multivariate ARCH-LM hereafter MARCH-LM).…”

“…According to Metropoll [31], 61.8% of the respondents stated they stopped eating meat, while 50.3% emphasized they reduced their meals. Connors, Malan [32] also reported that respondents in their study stated they have been consuming less meat due to price increases caused by Although most of the studies conducted in recent years have tried to link the transmission of price volatility between agricultural markets to the energy market [33][34][35], some researchers, albeit a small number, focused directly on the diffusion of price volatility among agricultural markets [36][37][38][39][40]. Among agricultural commodity markets, little is known about how instability in prices is transmitted along the food chain in Tu ¨rkiye, particularly before the outbreak and during the COVID-19 pandemic, and its link to red meat imports.…”