Using Mixtures-of-Distributions models to inform farm size selection decisions in representative farm modelling

The selection of �representative� farms in farm level modelling where results are aggregated to the sector level is critically important if the effects of aggregation bias are to be reduced. The process of selecting representative farms normally involves the use of cluster analysis where the decision regarding the appropriate number of clusters (or representative farm types) is largely subjective. However, when the technique of fitting mixtures of distributions is employed as a clustering technique there is an objective test of the appropriate number of clusters. This paper demonstrates the MDM approach to cluster analysis by classifying dairy farms in Northern Ireland, based on the number of cows in each farm. The results indicate that four representative farms are needed, with a view to minimising aggregation bias, to describe the dairy sector in Northern Ireland

Using Mixtures-of-Distributions models to inform farm size selection decisions in representative farm modelling.

The selection of ‘representative’ farms in farm level modelling where results are aggregated to the sector level is critically important if the effects of aggregation bias are to be reduced. The process of selecting representative farms normally involves the use of cluster analysis where the decision regarding the appropriate number of clusters (or representative farm types) is largely subjective. However, when the technique of fitting mixtures of distributions is employed as a clustering technique there is an objective test of the appropriate number of clusters. This paper demonstrates the MDM approach to cluster analysis by classifying dairy farms in Northern Ireland, based on the number of cows in each farm. The results indicate that four representative farms are needed, with a view to minimising aggregation bias, to describe the dairy sector in Northern Ireland.Mixtures of distributions, cluster analysis, representative farms

Estimating the probability of large negative stock market

Correct assessment of the risks associated with likely economic outcomes is vital for effective decision making. The objective of investment in the stock market is to obtain positive market returns. The risk, however, is the danger of suffering large negative market returns. A variety of parametric models can be used in assessing this type of risk. A major disadvantage of these techniques is that they require a specific assumption to be made about the nature of the statistical distribution. Projections based on this method are conditional on the validity of this underlying assumption, which itself is not testable. An alternative approach is to use a non-parametric methodology, based on the statistical extreme value theory, which provides a means for evaluating the unconditional distribution (or at least the tails of this distribution) beyond the historically observed values. The methodology involves the calculation of the tail index, which is used to estimate the relevant exceedence probabilities (for different critical levels of loss) for a selection of food industry companies. Information about these downside risks is critically important for investment decision making. In addition, the tail index estimates permit examination of the stable Paretian hypothesis.

Do Chinese stock markets share common information arrival processes?

According to the Mixture of Distributions Hypothesis (MDH), returns volatility and trading volume are driven by a common news arrival variable. Consequently, these two variables should be correlated. This paper extends, and to some extent, globalises the concept of a common information arrival process by hypothesising that this variable drives daily price (returns) volatility and trading volume changes in different financial markets. An implication is that returns volatility in one stock market should show positive and contemporaneous correlation with returns volatility in another stock market. This paper tests this implication using data from three separate, but geographically close, stock markets (Shenzhen, Shanghai and Hong Kong). A problem in the usual testing procedure is the likelihood that the news arrival process has long memory. This means that both volatility and volume (or external volatility) will have long memory and consequently, contemporaneous correlation between these variables is likely to be incorrectly rejected in cases where the test equation does not account for long memory. This paper uses fractionally integrated GARCH (FIGARCH) to test and account for long memory. The analysis finds that there is contemporaneous correlation between returns volatility in these stock markets and confirms the presence of long memory effects.mixture of distributions hypothesis, news arrival process, FIGARCH, volatility, long memory

Impact of Risk within the Northern Ireland Dairy Sector

This paper examines farmers’ decision making under risk and uncertainty. In particular, the study identifies the type of risk preference (averse, neutral or seeking) and measures the magnitude of risk preference before and after the introduction of the Single Farm Payment (SFP). The analysis therefore provides an insight into the impact of this fundamental policy change on farmer risk behaviour. Furthermore, it examines agricultural production decision making under price uncertainty. Empirically, it evaluates the impact of risk and uncertainty using Farm Business Survey (FBS) data of NI dairy farms. Using an econometric approach (maximize expected utility), a comprehensive methodology is employed that enables price uncertainty and risk preference under the Common Agricultural Policy (CAP) to be analysed simultaneously. The methodology permits the testing of common risk aversion theoretical hypotheses, including Arrow’s hypothesis on the effect of wealth on the measures of risk aversion. Also, this methodology enables the identification of the impact of farmers’ attitude to risk, price uncertainty and other criteria (e.g. age, education, and family labour) on their production decisions. The results are relevant to both policy makers and farmers. With regards to the former, the results reveal the factors which influence famers’ decision making, therefore, enabling policy makers to evaluate the effectiveness and efficiency of agricultural policies and thus introduce improvements in future policies. With regards to farmers, the results demonstrate the consequences of risk and uncertainty on their operational environment.Agricultural and Food Policy,

Does Gibrat's law hold amongst dairy farmers in Northern Ireland?

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight from emerging markets) prior to and during the 2008 financial crisis. In addition to widely used VaR and ES models, we also study the behavior of conditional and unconditional extreme value (EV) models to generate 99 percent confidence level estimates as well as developing a new loss function that relates tail losses to ES forecasts. Backtesting results show that only our proposed new hybrid and Extreme Value (EV)-based VaR models provide adequate protection in both developed and emerging markets, but that the hybrid approach does this at a significantly lower cost in capital reserves. In ES estimation the hybrid model yields the smallest error statistics surpassing even the EV models, especially in the developed markets

THE EFFICIENCY OF THE FUTURES MARKET FOR AGRICULTURAL COMMODITIES IN THE UK

This paper uses cointegration procedures to test for agricultural commodity futures market efficiency in the UK. Cointegration between spot and futures prices is a necessary condition for market efficiency where these prices are characterised by stochastic trends (Lai and Lai 1991). In addition, acceptance of the 'unbiasedness hypothesis' requires that the spot and lagged futures prices are cointegrated with the cointegrating vector (1, -1). Alternatively, Brenner and Kroner (1995) use a no-arbitrage cost-of-carry model to argue that the existence of cointegration between spot and futures prices depends on the time series properties of the cost-of-carry. According to Brenner and Kroner (1995), a tri-variate cointegrating relationship (the BK hypothesis) should exist among the spot price, the lagged futures price and the lagged interest rate (that component of cost-of-carry most likely to be non-stationary). These variables should be cointegrated with a cointegrating vector (1, -1, 1). Kellard (2002) finds that both bi-variate and tri-variate cointegrating relationships are found in a sample from the wheat futures market in the UK, and thus the so-called "cointegration paradox" emerges. As Kellard (2002) points out this paradox exists because it is theoretically impossible for two variables to be cointegrated with each other while simultaneously being cointegrated with a third variable. Using a larger sample of wheat futures market prices from LIFFE both the 'unbiasedness hypothesis' and the 'BK hypothesis' are examined. The results indicate that the 'BK hypothesis' should be rejected.Marketing,

Does Gibrat's Law Hold Amongst Dairy Farmers in Northern Ireland?

This paper tests whether the Law of Proportionate Effects (Gibrat, 1931), which states that farms grow at a rate that is independent of their size, holds for the dairy farms in Northern Ireland. Previous studies have tended to concentrate on testing whether the law holds for all farms. The methodology used in this study permits investigation of whether the law holds for some farms or all farms according to their size. The approach used avoids the subjective splitting of samples, which tends to bias results. The finding shows that the Gibrat law does hold except in the case of small farms. This is in accordance with previous findings that Gibrat's law tends to hold when only larger farms are considered, but tends to fail when smaller farms are included in the analysis. Implications and further extensions, as well as some alternatives to the proposed methodology are discussed.Gibrat's law, quantile regression, sample selection bias, Integrated Conditional Moments test, Agricultural and Food Policy, C12, C14, O49, Q19,

The Long-run Impact of Different Exchange Rates on the Projected Agricultural Income of an Export Dependent Region of the UK.

This paper evaluates the effects of different exchange rate scenarios on projections for agricultural incomes and prices in a small highly export dependent region, NI. The modelling system used in the analysis is designed to capture the complexities of the relationship between exchange rates and agricultural prices and incomes. The system models not only the main agricultural sectors in NI but also the demand for and supply of agricultural commodities in the EU and beyond. This is important, given that NI is a price taker and the EU is the main export destination for its agricultural production. The analysis serves to underline the importance of exchange rates for the NI agricultural economy. When the euro is weak against sterling then agricultural sector incomes are substantially lower than when the euro is strong against sterling. Approximately, a one per cent weakening/strengthening of the euro against sterling is projected to reduce/increase aggregate net receipts in the dairy, beef and sheep sectors by one per cent. This means that exchange rate movements, which are outside the control of the agricultural community, have a dramatic affect on agricultural incomes in NI. This conclusion should be considered against the backdrop of a 28% drop (approx.) in the value of the euro against the pound that has occurred since 1995. The impact of exchange rate movements on producer prices appears to be less pronounced.