Empirical analysis on volatility (19-Page PhD Literature Review)

| July 5, 2019

Question:

Write a literature review of papers from journals that explain factors that contribute to price volatility especially in agriculture and food in different countries

Answer:

Title: Empirical analysis on volatility

Discussion on the meaning of volatility using ARCH and GARCH models

Financial economists the world over are interested in modeling volatility with regard to asset returns. They consider this a crucial undertaking since volatility is an excellent measure of risk. Moreover, investors are interest in a premium before they can invest in very risky assets (Teräsvirta, 2006). Moreover, banks as well as financial institutions frequently refer to value-at-risk models whenever they are assessing their risks. For this reason, it is important to model and forecast volatility as part of efforts towards determining asset returns’ covariance structure.

There is a long-established fact to the effect that volatility in returns keeps fluctuating over time. Initially, financial economists dwelt largely on the leptokurtic nature of marginal distributions as far as volatility trends were concerned (Teräsvirta, 2006). In other words, returns were being viewed as independent entities that were distributed in the same way over a long period of time. Such an approach is commonly associated with the classical model of Paretian distributions. This approach has been widely used in the analysis of volatility in finance and economics.

However, subsequent studies have questioned the independence of return series with regard to financial assets that are monitored at high frequencies, for example, on a weekly basis Bauwens, Laurent, and Rombouts, 2006). To define the parameters of this dependence, Autoregressive Conditional Heteroskedasticity (ARCH) models have been found to be useful tools.

The ARCH models were developed by Engle (1982) in efforts to analyze volatility. The rationale for developing this model was accounting for variances in volatility at different times as well as uncertainty in inflation rates (Bollerslev, 1992). Bollerslev (1986), on the other hand, developed the GARCH (Generalized Autoregressive Conditional Heteroscedasticity model, which is in essence a more generalized form of the ARCH model.

In the ARCH model, three main tenets are discerned (Bollerslev, 1994). First, conditional variance is always seen to change over a specific period of time, and sometimes the changes can be quite substantial. The second tenet is on volatility clustering in which case large or small changes in completely unpredictable returns are normally followed by subsequent large or small changes in different areas of the economy. Thirdly, unconditional distribution of returns creates ‘fat’ tails that lead to a high probability of ‘outliers’ in relation to the normal distribution.  On the contrary, in the GARCH model, today’s conditional variance is viewed as the function of squared past unexpected returns and their past values. In this model, all past forecasted errors are squared and their infinite weighted average determined. In this case, attention is geometrically declining weights.

The ARCH model remains the first approach to the analysis of conditional heteroskedasticity. As Engle (2004) points out, the initial plan entailed the development of a model that economists could use with ease in assessing Friedman’s (1977) assertion that business cycles are primarily caused by unpredictable inflation. Friedman (1977) argued that unpredictable economic conditions triggered a feeling of uncertainty, thereby affecting the behavior of investors. For this idea to be pursued there was need for a model whereby the feelings of uncertainty kept changing with time. This led to the formation of a wage-price equation that was first applied in UK’s economic setting.

The ARCH model was characterized by many generalizations, which were used to model exchange rates, interest rates, and stock index returns. To this effect, Bollerslev (1992) has enumerated the various ways in which this model has been used since its development. However, the ARCH model does not quite solve the problem of how volatility ought to be measured. This is because forecasting volatility differs remarkably from forecasting conditional mean of specific economic processes. In this regard, volatility, which is the object under forecast, ends up not being observed. Nevertheless, there is no doubt that financial economists are best positioned to resort to high-frequency data in their analysis of volatility.

The failure by the ARCH model to define volatility is one of the reasons for the creation of an alternative, the Generalized ARCH model. This model was proposed by Taylor (1986) and Bollerslev (1986), and each economist did so in his own independent manner. In other words, the model was not born out of collaborative efforts. In the GARCH model, new terms were introduced, including the decay rate and maximum lag. However, due to the complicated nature of the GARCH model, many scholars prefer to use the ARCH model.

In other situations, some scholars have chosen to generalize the GARCH model with the aim of extending its application in varied directions. The underlying motive has been to make the original model increasingly flexible. An excellent example is the effort to extend the application of the model to account for asymmetrical economic responses. In this case, it should be borne in mind that originally, in the GARCH model, it was assumed that whenever there are variations in responses to shocks, such variances are independent of the signs of these shocks. Such responses were originally being viewed as simply a function of the magnitude of the shocks. This extension of the GARCH model creates room for the sort of flexibility that had been lacking in the GARCH model as described by Taylor (1986) and Bolerslev (1986). This trend towards extension of the model has inspired many economists to create many more models that can be accommodated within the GARCH ‘family’ (Hentschel, 1995).

From a layperson’s perspective, volatility is defined as the amount of risk or uncertainty with regard to the degree and nature of changes in the total value of an investment, security, market, or market sector. When volatility is high, the implication is that the value of a security has the potential of spreading out to a wider range of financial values. In other words, whereas in a state of volatility, the price of a security can either increase or decrease dramatically within a short period. When volatility is low, the value of a security does not exhibit dramatic, steady-pace fluctuation over a short period of time.

Volatility is used to measure risk on the basis of the extent of an asset return’s standard deviation. In the contemporary world, economists use different tools to communicate volatility trends to the world (Rosen, 2010). One such volatility index that is widely used is the scale of 1-9, whereby a high rating is used to denote an increase in risk (Valenzuela, 2007). At other times, percentages are used to denote the level of volatility. For example, a security carrying a 50% volatility is regarded as highly risky because of its potential to decrease or increase by half of its total value.

The measurement of food volatility of different food stocks (Barley, Wheat, Maize, Butter, Chicken, and Beef  

Numerous efforts have been made in the recent past to measure food volatility in different foods for instance, maize, beef, barley, wheat, and beef (Hilliard, 1999). During this time, and indeed over the past century, significant changes have been seen to have taken place with regard to primary commodity prices relative to those of manufactured goods (Han & Jansen, 1990). Specifically, primary commodity prices have been seen to exhibit a downward trend (Sarris, 2006). This downward trend has particularly been recorded in individual commodity markets relating to agriculture. To exemplify this, Sarris (2006) carries out an analysis of 21 agricultural prices using the data provided by the World Bank between 1960 and 2002. The percentage trend indicated in the World Bank statistics averages 2%, much faster than the 1.3% estimated through the commodity price index of the IMF (Sarris, 2006).

Upon analysis of this information, the main question that begs for an answer is what could have caused the declining price trend. From the way the statistics have been presented, it is clear that any plausible explanation of this volatility trend should ideally refer to all the general characteristics of these markets as opposed to features that are market-specific (Barnett, 1983). It should be borne in mind that the data obtained between 1960 and 2002 showed a downward price trend not only in 21 agricultural commodities but also in metal and mineral items. Therefore, it would be rather unusual for market-specific features to bring about trends that apply in the same way to both agricultural and mineral-related products.

According to Singer (1950) and Prebisch (1962), it is plausible to pursue the hypothesis that primary commodity export prices ought to fall in relation to manufactured imports as a result of income elasticities of demand, lower price, and increase in the assertiveness of trade unions. Singer (1950) and Prebisch (1962) argue that such a situation would bring about a decline in net barter trade terms for countries that produce the commodities. However, this argument appears weak. The main source of weakness is that the supposed increase in union power assertiveness in the manufacturing sector would most likely bring about a manufactures mark-up that surpasses that of primary commodities, yet fail to create a continuing trend. On the long term, lower elasticities would lead to a drop in commodity production as opposed to lowering of prices.

Some scholars have taken the debate on measurement of food volatility a notch higher by adopting a case-study approach (Combes & Guillaumont, 2008). For instance, Apergis & Rezitis (2003) assessed the issue of agricultural price volatility in Greece, where they were specifically interested in the spillover effects of this phenomenon. Apergis & Rezitis (2003) used GARCH models to measure the volatility of agricultural input and output prices as well as retail food prices. These researchers found out that volatility of both retail food prices and agricultural inputs tended to exert far-reaching positive a spillover influence on agricultural output-price volatility. Additionally, agricultural output prices were found to be much more volatile in comparison with both retail food prices and agricultural input.

In a different study, Sekhar (2009) dwells on the effects of liberalization of agricultural trade in India. Here, volatility of food item prices is assessed in the context of major domestic and international markets. According to Sekhar (2009), international markets are characterized by high price volatility, and this alone forms a firm basis for arguments against the liberalization of agricultural trade. However, in his findings, Sekhar (2009) finds little evidence indicating that international prices of agricultural commodities vary more uniformly in comparison to domestic prices. Moreover, inter-year price variability is higher within international markets compared to the domestic markets, for which intra-year variability remains higher (Sekhar, 2009). These findings apply for all food commodities except soya bean and sugar (Sekhar, 2009).

Indeed, the issue of volatility in agricultural products has been variously described in the international context. Anderson & Nelgen (2012) uses the term ‘trade barrier volatility’, in reference to the impact of international trade barriers on the price volatility of various food items. Essentially, the main reason why individual countries put in place agricultural trade barriers is to insulate the domestic market from variability in international prices. This is especially the case in situations where a sudden spike has recently been experienced (Roberts, 2001). In the research paper by Anderson & Nelgen (2012), focus is on the behavior of 75 countries to engage in agricultural price distortions. In this case, agricultural-importing countries are seen to respond in the same way as agricultural-exporting countries to price distortion mechanisms. On the larger picture, this trend normally has the effect of weakening the price-stabilization measures at the domestic level. Anderson & Nelgen (2012) suggest that instilling discipline in export restrictions through the provisions of the World Trade Organization can greatly help in alleviating erratic government responses that end up leading to an upward trend in agricultural commodity prices.

Yanga & Haigh (2001) take the discussion on the issue of agricultural liberalization a notch higher by discussing the ways in which the GARCH application can be used to measure agricultural commodity price volatility. The authors note that the recent radical liberalization policy affecting the agricultural sector, that is, the 1996 Fair Act has greatly influenced the trend towards an increase in the volatility of corn, soybeans, and wheat. However, the act has had a little impact on the price of oats, although it has led to a price drop in the case of cotton. These findings contrast sharply with those of Crain & Lee (1996). In their study, Crain & Lee (1996) dwelt on wheat market, and they found out that government-driven, market-oriented policies tend to bring about a drop in the volatility of agricultural prices.

Similarly, Giot (2003) uses the GARCH model to measure volatility of prices in agricultural products. The most significant difference is the use of the notion of ‘implied volatility’. In Giot’s (2003) analysis, focus was on the case study of New York Board of Trade. In this case, the authors sought to show that past squared returns could only lead to marginal improvement of the information content offered through lagged implied volatility. Another finding was that value-at-risk models that are dependent upon lagged implied volatility are better performers than those for which conditional variance modeling is done in accordance with the provisions of GARCH models.

In efforts to understand the process of measuring food volatility, it is imperative to focus on specific food types (Apergisa, 2003, Dua & Yu, 2011)). Koekebakker (2004) focuses on wheat, specifically in relation to agricultural futures price movements. The issue of wheat is used to gather evidence of the factors contributing to price volatility. In this case, two main factors are identified: maturity effect (time to maturity) and seasonal effect (calendar-time). The notions of maturity effect and seasonal effect were first used by Bates (1991) when they included them in the jump-diffusion model of pricing. In Bates’ findings, this model was found out to outperform all previously published pricing models.

Regarding variations in pricing models, it is possible to explain dramatic changes in food commodity prices in a variety of ways. As Trostle (2008) points out, in recent times, the global market prices of major food items such as vegetable oils and grains have continued to increase dramatically, reaching 60% highs within a duration of just two years. Trostle (2008) observes that the factors that have brought about price volatility are a reflection of a slow production pace and a rapid increase in demand. These dynamics have eventually tightened up world balances with regard to oilseeds and grains throughout the 2000s. This tightening up of world balances is expected to continue being experienced during the second decade of this millennium.

With regard to these food commodity items, some of the factors that have led to price volatility at the global level in recent times include adverse weather conditions between 2006 and 2007, and increase in demand for feedstocks for biofuel production. Other factors include rising energy prices, decline in the US dollar value, increase in the cost of production for agricultural commodities, and a growth in the foreign exchange holdings owned by countries that are major food importers (Schwartz, 1997). Additionally, some food-exporting and importing countries have taken to adopting measures aimed at reducing the level of inflation in food prices (Yang, 2009, Regnier, 2007). The main weakness with Trostle’s (2008) study is that it only addresses one side of food price volatility, that is, food price inflation; it fails to look into factors that may lead to a sudden drop in food prices.

Importantly, Trostle notes food price volatility should never be attributed to just one factor. This is indeed true, considering that some of the factors involved are a reflection of underlying dynamics in the supply and demand of these agricultural commodities. Whereas some factors are a reflection of key structural changes in demand-supply interactions, others manifest themselves in the form of short-term shocks in the demand and supply of agricultural food commodities (Langley, 2000, Tweeten, 1999, Anderson, 2009).

The short-term shocks in the demand and supply manifest themselves both in the domestic markets and in the international markets. In this regard, it is possible for the shocks affecting one country to become contiguous and to spread over to a neighboring country. A case in point is the impact of British barley prices on the prices of the same commodity in Ireland, a small open economy (Roche, 2003). In this case study, Roche (2003) makes use of the multivariate ARCH model in efforts to investigate this effect. Roche (2003) find out that the law of one price perfectly holds in the long run while salient feature of barley prices in Ireland are captured in the short run. The multivariate model was found to produce more superior forecasts than the model of autoregressive conditional mean.

In a related study, Shively (1996) used the ARCH model to analyze maize price variability in Ghana. Shively (1996) zeroed in on wholesale price determination, whereby traders store maize gain stocks in a speculative manner as well as with the aim of exporting it to the neighboring countries. In this study, Shively gives a fresh insight into the measurement of volatility by applying the ARCH model to monthly maize data for 2 key markets between 1978 and 1993. Shively (1996) argues that on this basis, it is possible to use regression to determine changes in price volatility in the Ghanaian maize market. Moreover, this facilitates the process of inferring the importance of domestic price control measures, regional production trends, past prices, and commodity storage in explaining volatility (Shively, 1996).

In the case of measurement of volatility of dairy products such as milk and butter, numerous studies have been carried out (Adubi, 1999, Bessembinder, 1993, Balcombe, 2009, Valenzuela, 2007, Kenyon, 1987, Benirschka, 1994, Jin, 2004, Tsakok, 1990). In one such study, O’Connor & Keane (2009) review the policy environment within which the EU dairy industry thrives. O’Connor & Keane attribute the ongoing rapid changes in EU’s dairy industry to CAP (Common Agricultural Policy) and WTO (World Trade Organization) reforms. O’Connor & Keane (2009) predict that if a more liberal system for global agricultural trade is embraced, it will be impossible to avoid an increase in the volatility of dairy products. Generally, world prices tend to be lower but much more volatile when compared to those of the EU bloc. The assumption in the study is that it is just a matter of time before this volatility is ultimately transmitted into milk, butter, and skim milk powder within the EU.  Unlike Shively (1996) and Roche (2003), though, O’Connor & Keane quantify volatility using both GARCH and ARMA (Autoregressive Moving Average) models.

In the case of the poultry market, the case study research done by Uchezuba (2010), which focuses on the South African market, is highly relevant. Uchezuba notes that in the course of the last decade, the country underwent a period of increasing food prices. During this time, prices remained highly volatile, thus posing a formidable threat to the country’s food security. However, there was also evidence of certain players taking advantage of the prevailing market structure to manipulate poultry prices. Uchezuba (2010) investigated the ways in which price transmission takes place in various agro-food chains to under the country’s pricing behavior in greater detail, particularly with regard to the poultry market.

In terms of methodology, Uchezuba chose to focus on the broiler industry owing to the continued increase in demand across South Africa between 2001 and 2009. The main issues analyzed included volatility spillover across the value chain, asymmetric prices, direction of information flow between the country’s producers and retailers, quantification of volatility spillover in farm prices and retail prices, as well as spillover of volatility with regard to feed materials. Regarding data, market monthly prices were used and analyzed using M-TAR (Momentum Threshold Autoregressive) and TAR (Threshold Autoregressive) models. This marks a clear shift away from the mainstream ARCH and GARCH models. Nevertheless, a variant of the ARCH model, the EGARCH (exponential generalized autoregressive conditional heteroskedasticity) was employed in the measurement of volatility and spillover between farm and retail prices. The spillover analysis was also extended into poultry feed ingredients.

After the analysis, the findings of the M-TAR model turned out to be largely similar to those obtained through the EGARCH model (Uchezuba, 2010). Moreover, an asymmetrical relationship was found to exist between retail and farm prices. This asymmetry signifies that market power is poorly distributed across the value chain, whereby it remains concentrated only in a few areas. Uchezuba (2010) suggests that in situations of this nature, there is a need for stringent anti-competition laws aimed at discouraging anti-competitive behaviors. Moreover, if agricultural information systems were increased among key stakeholders, this may help in the reduction of information bottlenecks within the poultry market system.

In the beef markets, the dynamics of price volatility appear somewhat different from those of other agricultural commodities (Saghaian, 2002). However, some fears expressed in this sector appear to resonate across agricultural markets. For instance, in the US for instance, the past few decades have witnessed significant structural changes and a rise in market concentration with regard to beef packaging (Natcher, 1999). This issue of market concentration has also dominated scholarly debate on poultry markets in South Africa (Uchezuba, 2010, Hudson, 1987, Aizenman, 2005, Sekhar, 2004). In the case of the US, this concentration has led scholars to wonder whether industry participants are exercising market power, in efforts to ascertain whether the market is operating competitively. Nevertheless, Natcher (1999) asserts that industry concentration in itself is not an indication of non-competitive behavior.

As Feather & Sherrick point out, sometimes firms opt to engage in vertical integration in efforts to reduce supply uncertainty risk and to increase the firms’ efficiency through reduction of cost of production. For this reason, vertical integration can be employed as a way of reducing uncertainty and not to engage in any anti-competitive behavior. All on all, too much concentration triggers concerns regarding the subsequent emergence of natural barriers to corporate entry as well as occurrence of noncompetitive pricing.

The measurement of food volatility in Asia: Cases of South Korea and China

In most Asian countries, governments have in recent times been pursuing efforts to achieve grain price stabilization. This goal has indeed taken the form of a policy instrument of facilitating the achievement of the Green Revolution (Ralph, 2005). In typical scenarios, issues of policymaking revolve a lot around matters of interventions as well as withdrawal strategies for these interventions (Merton, 1976). However, one mistake that recurs in the course of these efforts is to forget the aspects of withdrawal (Myers, 1993). This leads to high costs that are unsustainable, a dilemma that many countries in Asia have to face today.

Nevertheless, there are many lessons to be learnt from the experiences of various Asian countries as far as policies of dealing with price volatility are concerned. These lessons are best synthesized through an analysis of price stabilization efforts that have spanned more than three decades across the continent. Indeed, all along, times have been changing, such that policies that were relevant 30 years ago are no longer applicable today (Pagano, 2009). Unlike in the past, the private institutions of today have recorded growth in terms of both financial strength and influence. This influence affords them the power to undertake the functions that have traditionally been the reserve of government departments and parastatals. This means that countries that hold onto age-old practices only bring about delays in the realization of benefits that are possible through current policies.

To put some focus within the continent, it is imperative to highlight the circumstances of specific regions. In East Asia, for example, one of the most recent causes of concern in recent times was the rapid increase in international food prices between 2007 and 2008 (Brahmbhatt, 2008). These price increases, particularly in the case of rise, have ended up significantly affecting the living standards of many Asians, particularly those living below the poverty line.

In the face of the volatility challenge, many East Asian countries have had to engage in collaboration efforts both regionally and internationally (Chambers, 1984). They have also had to put in place domestic measures that boost food security while offering viable solutions to the very poor. One of the main economic blocs in this region, the ASEAN (Association of Southeast Asian Nations) has a great potential of playing a key role in ensuring that there is coordination with regard to international responses.

South Korea

On the issue of measurement of food price volatility, the case study of South Korea has been focused on severally in literature (Shively, 2001, Swaray, 2010, Khoury, 1993, Gruen, 2001, Headey, 2008). For several decades, this East Asian country has been endeavoring to achieve self-sufficiency in food and agricultural products through high administrative prices and high tariffs. These measures have been targeted at key food and agricultural markets.

In efforts to measure volatility in the context of South Korea, Beghin, Bureau, & Park (2003) used a dual approach of analyzing indices on trade restrictiveness. From this perspective, they assessed the ways in which these market distortions affect trade volume and welfare. The study then moved on to the computation of second-best distortions. These second-best distortions are assumed to have the impact of minimizing the cost of attaining the desired levels of food production and self-sufficiency. In the end, Beghin, Bureau, & Park (2003) rationalized about the legitimacy of the second-best distortions with regard to the ‘food security’ box within WTO negotiations. In this regard, it is apparent that Beghin, Bureau, & Park (2003) avoided using both the ARCH and GARCH models in measuring price volatility in South Korea. Nevertheless, they succeeded in showing that pursuing ‘food security’ through reliance on imports and production targets can bring about mutual benefits to both traders and consumers while at the same time leading to the preservation of the transfer of income within the farm sector.

China

For China, a similar case of volatility has been recorded in recent times in many agricultural products, including food grains, vegetable oils, and oilseeds. This increase has resulted in an increase in domestic food prices. In terms of policymaking, China has largely been adopting macroeconomic perspectives in measuring the level of food price volatility. Incidentally, there is abundance of literature in the form of empirical studies documenting the relationship between relative prices of specific food products and the expected rate of inflation in China (Stockton, 1988, Mizon, 1991, Smith & Lapp, 1993, Tyers, 1992).

Yet in China, just as in other countries of the world, high food prices are an indicator of a high rate of inflation (Taylor & Spriggs, 1989). This is largely because in Consumer Price Index, more weight is given to food prices. For this reason, whenever inflation is high, food prices will automatically keep on increasing. In this case, there may be a tendency for wages to be increased in the economy, further fuelling expectations of an even higher scale of inflation (Tyers, 1992).

During the recent economic recession, the impressive economic growth statistics of China were frequently being compared with the strategies employed by the central banks of G7 countries, whose aim was to deal with excess liquidity in global economies. Against the backdrop of these comparisons, more attention was being directed at macroeconomic forces at the expense of ARCH and GARCH models in measuring volatility both nationally and internationally.

A particularly widespread practice in China has been the use of commodities as hedging portfolios as a way through which investment portfolios are hedged against the risk of inflation (Attie & Roache, 2009). However, the viability of this strategy remains in question largely because of lack of accuracy in predicting future volatility trends in food and agricultural products (Attie & Roache, 2009). In essence, this is clear evidence of the ways in which food price volatility in China serve to affect the choice of portfolios among financial investors. The main problem, though, is on determining with accuracy the likelihood of various food items becoming volatile. This is especially the case in today’s increasingly globalized world, where it is virtually impossible to rule out the influence of international trade in food price volatility.

Being a communist society, China’s economy is one where monetary factors exert an influence that differs slightly from that exerted on small, open economies like, say, Greece (Timmer, 2005). Since the Chinese government exerts tremendous influence on economic decisions, monetary factors, that is, money supply tend to be only remotely linked to food prices. This is unlike in open economies, where mechanisms such as foreign exchange rates would be a key determinant of food price volatility. Owing to the strict government control of real exchange rates, it may be difficult to determine the real level of the economy’s external competitiveness.

The measurement of food volatility in GCC member countries

In recent years, there has been an increasingly observable correlation between agricultural commodity prices and oil prices (Basher, 2010). The effects of oil prices manifest themselves both directly and indirectly on agricultural input prices. Owing to this correlation, it may be profitable for stakeholders in OECD countries to engage in the production of biofuels, even without support from the government. Financial investment in food commodities also appears to have made a contribution to the increasingly visible correlation between oil commodity and non-oil commodity prices (Al-Qudsi, 2008). This is largely because there is normally a significant share of such investments that track indices containing baskets of a wide range of commodities.

The influence of GCC (Gulf Cooperation Council) countries is clear with regard high, volatile oil prices, largely because of their high oil and natural gas output at the global scale (Arouri, 2010). When these countries choose to sell their oil at higher prices, this translates into volatile agricultural prices, owing to a subsequent rise in input costs. The volatility is also fuelled by a subsequent increase in demand for commodities that are normally used to produce biofuels, such as maize, vegetable oils, and sugar (Lampietti & Michaels, 2011). The volatility is also normally contributed to by competition for land with commodities that stakeholders do not use directly to produce fuel. There is also a possibility of this influence taking the form of increased financial investments in various food commodity baskets (Lampietti & Michaels, 2011).

Other than having an influence on food inflation in other countries, GCC member states have also had to contend with a rapid rise in inflation in recent times (Fasano & Iqbal, 2009). For instance, between 2008 and 2008, GCC member states experienced a rapid rise in inflation (Woertz, Pradhan, Biberovic, & Koch, 2008). Food inflation has been second in dominance after rent inflation. Food price volatility has specifically become a regional concern in the wake of price increases across the world.

The food price volatility in the GCC countries continues to have a great impact across the region. An excellent case in point is Saudi Arabia, where inflation hiked from 0.6% in 2003 to 6.5% in 2007 (Hasan, 2008). In this country, most of the people who been adversely affected are those of lower income brackets. This is because they have had to spend almost all of their disposable incomes on agricultural food commodities.

The problem of volatility was so serious in Saudi Arabia that in 2008, the government expedited an anti-inflation plan (Al-Yousif, 2011). One of the elements of this plan was a supply policy, whereby sources of supply for food and agricultural commodities would be diversified. This would ensure that local demand would be met at very reasonable prices. The government also moved in to prevent private sector stakeholders from engaging in anti-competitive behavior, price control, and monopolistic practices.

 

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