Noise sensitivity of portfolio selection under various risk measures

We study the sensitivity to estimation error of portfolios optimized under various risk measures, including variance, absolute deviation, expected shortfall and maximal loss. We introduce a measure of portfolio sensitivity and test the various risk measures by considering simulated portfolios of varying sizes N and for different lengths T of the time series. We find that the effect of noise is very strong in all the investigated cases, asymptotically it only depends on the ratio N/T, and diverges (goes to infinity) at a critical value of N/T, that depends on the risk measure in question. This divergence is the manifestation of a phase transition, analogous to the algorithmic phase transitions recently discovered in a number of hard computational problems. The transition is accompanied by a number of critical phenomena, including the divergent sample to sample fluctuations of portfolio weights. While the optimization under variance and mean absolute deviation is always feasible below the critical value of N/T, expected shortfall and maximal loss display a probabilistic feasibility problem, in that they can become unbounded from below already for small values of the ratio N/T, and then no solution exists to the optimization problem under these risk measures. Although powerful filtering techniques exist for the mitigation of the above instability in the case of variance, our findings point to the necessity of developing similar filtering procedures adapted to the other risk measures where they are much less developed or non-existent. Another important message of this study is that the requirement of robustness (noise-tolerance) should be given special attention when considering the theoretical and practical criteria to be imposed on a risk measure.     

Is there a viable alternative to ordinary least squares regression when security abnormal returns are the dependent variable?

Regression analysis is often used to estimate a linear relationship between security abnormal returns and firm-specific variables. If the abnormal returns are caused by a common event (i.e., there is “event clustering”) the error term of the cross-sectional regression will be heteroskedastic and correlated across observations. The size and power of alternative test statistics for the event clustering case has been evaluated under ideal conditions (Monte Carlo experiments using normally distributed synthetic security returns) by Chandra and Balachandran (J Finance 47:2055–2070, 1992) and Karafiath (J Financ Quant Anal 29(2):279–300, 1994). Harrington and Shrider (J Financ Quant Anal 42(1):229–256, 2007) evaluate cross-sectional regressions using actual (not simulated) stock returns only for the case of cross-sectional independence, i.e., in the absence of clustering. In order to evaluate the event clustering case, random samples of security returns are drawn from the data set provided by the Center for Research in Security Prices (CRSP) and the empirical distributions of alternative test statistics compared. These simulations include a comparison of OLS, WLS, GLS, two heteroskedastic-consistent estimators, and a bootstrap test for GLS. In addition, the Sefcik and Thompson (J Accounting Res 24(2):316–334, 1986) portfolio counterparts to OLS, WLS, and GLS, are evaluated. The main result from these simulations is none of the other estimator shows clear advantages over OLS or WLS. Researchers should be aware, however, that in these simulations the variance of the error term in the cross-sectional regression is unrelated to the explanatory variable.

Productivity and Convergence in European Agriculture

We investigate relative productivity levels and decompose productivity change for European agriculture between 2004 and 2013. Specifically (i) we contribute to the debate on whether agricultural Total Factor Productivity (TFP) has declined or not in the European Union (EU); (ii) we compare the relative TFP level across EU Member States and investigate the difference between ‘old’ Member States (OMS, i.e. the EU‐15) and ‘new’ Member States (NMS); and (iii) we test whether TFP is converging or not among Member States. The empirical analysis applies an aggregate quantity framework to country‐level panel data from the Economic Accounts for Agriculture for 23 EU Member States. The results imply that TFP has slightly decreased in the EU over the analysed period; however there are significant differences between the OMS and NMS and across Member States. Finally, our estimates suggest that productivity is generally converging over this period, albeit slowly.     

Quality Upgrades of EU Agri‐Food Exports

We investigate determinants of quality upgrades in EU agri‐food exports using panel data models for the period 2000–2011. By employing highly disaggregated data we show that the unit value of exports is positively correlated to level of economic development and size of population. Our results highlight the negative impacts of comparative advantage and trade costs on upgrades in export quality. Our analysis partly confirms the role of income distribution in quality specialisation, that greater income inequality increases specialisation in quality upgrades. Findings are robust when applied to alternative subsamples, including vertically specialised and final agri‐food products.     

Are farms in less favored areas less efficient?

This article investigates farm technical efficiency (TE) and the effect of heterogeneity on production among farms using the Slovenian Farm Accountancy Data Network sample of farms in the period 2007–2013. We model production technology with a random parameter model that allows us to examine both the direct effect of heterogeneity on production and the indirect effect through the interaction of unobserved heterogeneity with time and input variables. Additionally, we consider intersectoral heterogeneity among types of farming. Results confirm the importance of all these sources of heterogeneity. The second contribution of the article is that, in addition to using conventional statistical methods, we examine the differences between less favored area (LFA) and non‐LFA farms using matching techniques. Results indicate that there is only a minor and statistically nonsignificant difference in TE between these groups. However, the difference is highly significant in terms of heterogeneity and technology. In other words, results show that farms in LFAs are not more inefficient but rather use different, production–environment‐specific technologies. These findings call attention to the fact that omitting the effect of heterogeneity on production technology leads to biased TE estimates and, in turn, leads to potentially imperfect policy choices.     

An extended production/recycling model with stationary demand and return rates

A production–recycling system is investigated. A constant demand can be satisfied with production and recycling. The used items are bought back and then recycled. The non-recycled products are disposed of. Two types of models will be analyzed. The first model examines the EOQ-related costs and minimizes the relevant costs. The second model generalizes the first model with the introduction of the cost function with linear waste disposal, recycling, production and buyback costs. It is asked whether the pure (either production or recycling) or mixed strategies are optimal and it will be shown that under these circumstances the mixed strategies are dominated by the pure strategies. The paper generalizes a former model proposed by the authors for the case of one recycling and one production batch to the case of arbitrary batch numbers.     

MEASURING DISTRIBUTION MODEL RISK

We propose to interpret distribution model risk as sensitivity of expected loss to changes in the risk factor distribution, and to measure the distribution model risk of a portfolio by the maximum expected loss over a set of plausible distributions defined in terms of some divergence from an estimated distribution. The divergence may be relative entropy or another f‐divergence or Bregman distance. We use the theory of minimizing convex integral functionals under moment constraints to give formulae for the calculation of distribution model risk and to explicitly determine the worst case distribution from the set of plausible distributions. We also evaluate related risk measures describing divergence preferences.     

The growth of family farms in Hungary

The article investigates the validity of Gibrat's Law for Hungarian family farms using FADN data collected between 2001 and 2007. Gibrat's Law states that the growth rate of firms will be independent of their initial size. Regression results allow us to reject Gibrat's Law for all quantiles. Evidence suggests that smaller farms tend to grow faster than larger ones. Results do not support the hypothesis of “disappearing middle” in Hungarian agriculture. We study a number of socio‐economic factors that can help to explain farm growth. We find that total subsidies received by a farm and the farm operator's age are the most significant factors correlated with farm growth.     

The analysis of bullwhip effect in a HMMS-type supply chain

The aim of the paper is to investigate the well-known bullwhip effect of supply chains. Control theoretic analysis of bullwhip effect is extensively analyzed in the literature with the Laplace transform. This paper tries to examine the effect for an extended Holt-Modigliani-Muth-Simon model. A two-stage supply chain (supplier-manufacturer) is studied with quadratic costs functional. It is assumed that both firms minimize the relevant costs. The order of the manufacturer is delayed with a known constant. Two cases are examined: supplier and manufacturer minimize the relevant costs decentralized, and a centralized decision rule. The question is answered, how to decrease the bullwhip effect.