933 resultados para Empirical risk


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The aim of this paper is to analyse vulnerability and robustness of small and medium size enterprises (SMEs) supply chains and to consider contextual factors that might influence the success of their disturbance management: Risky product and business environment. By using an exploratory case study it is shown how these contextual factors attribute vulnerability sources, contribute to the robustness of a company’s performance and supply chain vulnerability, as well as how a company seeks to manage internal and external vulnerability sources. The exploratory case is based on a fresh food supply chain of a manufacturing SME operating in a developing market.
Case findings suggest that fresh food supply chains of a manufacturing SME in developing markets are prone to disruptions of their logistics and production processes due to ‘riskiness’ of fresh food products, the ‘riskiness’ of developing markets, as well as ‘riskiness’ of SMEs themselves. However, this does not necessarily indicate the vulnerability of an SME and its entire supply chain. Findings indicate that SMEs can be very successful in disturbance management by selective use of redesign strategies that aim to prevent or reduce the impact of disturbances. More precise, it is likely that an SME can achieve robust performance by employing preventive redesign strategies in managing disturbances that result from internal, company related vulnerability sources, while impact reduction strategies are likely to contribute to robust performance of an SME if used to manage disturbances that result from internal, supply chain related vulnerability sources, as well as external vulnerability sources.

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Enterprise Risk Management (ERM) is gaining relevance among financial and non-financial companies but its benefits still are uncertain. This paper aims at investigating the relationship between ERM adoption and firm performance based on a sample of 1130 non-financial companies belonging to the STOXX® index. A content analysis of individual accounts is performed to distinguish adopters, and a regression analysis explores the effect of ERM adoption on firm performance, proxied by Tobin’s Q. The findings suggest that there is a statistical significant positive effect of ERM adoption on firm performance, meaning that firms are benefiting from the implementation of this process.

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Since the financial crisis, risk based portfolio allocations have gained a great deal in popularity. This increase in popularity is primarily due to the fact that they make no assumptions as to the expected return of the assets in the portfolio. These portfolios implicitly put risk management at the heart of asset allocation and thus their recent appeal. This paper will serve as a comparison of four well-known risk based portfolio allocation methods; minimum variance, maximum diversification, inverse volatility and equally weighted risk contribution. Empirical backtests will be performed throughout rising interest rate periods from 1953 to 2015. Additionally, I will compare these portfolios to more simple allocation methods, such as equally weighted and a 60/40 asset-allocation mix. This paper will help to answer the question if these portfolios can survive in a rising interest rate environment.

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Rapport de recherche

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Two experiments compared people's interpretation of verbal and numerical descriptions of the risk of medication side effects occurring. The verbal descriptors were selected from those recommended for use by the European Union (very common, common, uncommon, rare, very rare). Both experiments used a controlled empirical methodology, in which nearly 500 members of the general population were presented with a fictitious (but realistic) scenario about visiting the doctor and being prescribed medication, together with information about the medicine's side effects and their probability of occurrence. Experiment 1 found that, in all three age groups tested (18 - 40, 41 - 60 and over 60), participants given a verbal descriptor (very common) estimated side effect risk to be considerably higher than those given a comparable numerical description. Furthermore, the differences in interpretation were reflected in their judgements of side effect severity, risk to health, and intention to comply. Experiment 2 confirmed these findings using two different verbal descriptors (common and rare) and in scenarios which described either relatively severe or relatively mild side effects. Strikingly, only 7 out of 180 participants in this study gave a probability estimate which fell within the EU assigned numerical range. Thus, large scale use of the descriptors could have serious negative consequences for individual and public health. We therefore recommend that the EU and National authorities suspend their recommendations regarding these descriptors until a more substantial evidence base is available to support their appropriate use.

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Developing models to predict the effects of social and economic change on agricultural landscapes is an important challenge. Model development often involves making decisions about which aspects of the system require detailed description and which are reasonably insensitive to the assumptions. However, important components of the system are often left out because parameter estimates are unavailable. In particular, measurements of the relative influence of different objectives, such as risk, environmental management, on farmer decision making, have proven difficult to quantify. We describe a model that can make predictions of land use on the basis of profit alone or with the inclusion of explicit additional objectives. Importantly, our model is specifically designed to use parameter estimates for additional objectives obtained via farmer interviews. By statistically comparing the outputs of this model with a large farm-level land-use data set, we show that cropping patterns in the United Kingdom contain a significant contribution from farmer’s preference for objectives other than profit. In particular, we found that risk aversion had an effect on the accuracy of model predictions, whereas preference for a particular number of crops grown was less important. While nonprofit objectives have frequently been identified as factors in farmers’ decision making, our results take this analysis further by demonstrating the relationship between these preferences and actual cropping patterns.

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In this paper, we extend the debate concerning Credit Default Swap valuation to include time varying correlation and co-variances. Traditional multi-variate techniques treat the correlations between covariates as constant over time; however, this view is not supported by the data. Secondly, since financial data does not follow a normal distribution because of its heavy tails, modeling the data using a Generalized Linear model (GLM) incorporating copulas emerge as a more robust technique over traditional approaches. This paper also includes an empirical analysis of the regime switching dynamics of credit risk in the presence of liquidity by following the general practice of assuming that credit and market risk follow a Markov process. The study was based on Credit Default Swap data obtained from Bloomberg that spanned the period January 1st 2004 to August 08th 2006. The empirical examination of the regime switching tendencies provided quantitative support to the anecdotal view that liquidity decreases as credit quality deteriorates. The analysis also examined the joint probability distribution of the credit risk determinants across credit quality through the use of a copula function which disaggregates the behavior embedded in the marginal gamma distributions, so as to isolate the level of dependence which is captured in the copula function. The results suggest that the time varying joint correlation matrix performed far superior as compared to the constant correlation matrix; the centerpiece of linear regression models.

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DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT This thesis is a cross-disciplinary study of the empirical impact of real options theory in the fields of decision sciences and performance management. Borrowing from the economics, strategy and operations research literature, the research examines the risk and performance implications of real options in firms’ strategic investments and multinational operations. An emphasis is placed on the flexibility potential and competitive advantage of multinational corporations to explore the extent to which real options analysis can be classified as best practice in management research. Using a combination of qualitative and quantitative techniques the evidence suggests that, if real options are explored and exploited appropriately, real options management can result in superior performance for multinational companies. The qualitative findings give an overview of the practical advantages and disadvantages of real options and the statistical results reveal that firms which have developed a high awareness of their real options are, as predicted by the theory, able to reduce their downside risk and increase profits through flexibility, organisational slack and multinationality. Although real options awareness does not systematically guarantee higher returns from operations, supplementary findings indicate that firms with evidence of significant investments in the acquisition of real options knowledge tend to outperform competitors which are unaware of their real options. There are three contributions of this research. First, it extends the real options and capacity planning literature to path-dependent contingent-claims analysis to underline the benefits of average type options in capacity allocation. Second, it is thought to be the first to explicitly examine the performance effects of real options on a sample of firms which have developed partial capabilities in real options analysis suggesting that real options diffusion can be key to value creation. Third, it builds a new decision-aiding framework to facilitate the use of real options in projects appraisal and strategic planning.

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The first part of the study examined the effect of industry risk changes on perceived audit risk at the financial statement level and whether these changes depended on individual differences such as experience and tolerance for ambiguity. ^ Forty-eight auditors from two offices of one of the “Big 5” CPA firms participated in this study. The ANOVA results supported the effect of industry risk in the assessment of audit risk at the financial statement level. Higher industry risk was associated with higher perceived audit risk. Tolerance for ambiguity was also significant in explaining the changes in the assessment of audit risk. Auditors with a high tolerance for ambiguity perceived lower audit risk than auditors with a low tolerance for ambiguity. Although ANOVA results did not find experience to be significant, a t-test for experience showed it to be marginally significant and inversely related to audit risk. ^ The second part of this study examined whether differences in perceived audit risk at the financial statement level altered the extent, nature or timing of the planned auditing procedures. The results of the MANOVA suggested an overall audit risk effect at the financial statement level. Perceived audit risk was significant in explaining the variation in the number of hours planned for the total cycle and the number of hours p1anned for the tests of balances and details. Perceived audit risk was not significant in determining the analytical review procedures planned, but assessed inherent risk at the cycle level was significant. The higher the inherent risk the more analytical procedures were planned. Perceived audit risk was not significant in explaining the timing of the procedures, but individual differences were significant. The results showed that experienced auditors and those with a high tolerance for ambiguity were less likely to postpone the performance of the interim procedures or the time at which the majority of audit work would be done. ^