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This paper contributes to the promotion of multidisciplinary research on ethical consumerism by providing experimental evidence on consumer's willingness to reward sellers by paying higher wages to their workers. We analyze repeated interactions occurring between workers, sellers, and consumers within the framework of an experimental market. By successfully performing a task, workers allow sellers to offer a good through a market. Sellers set the price of goods and decide the wages of workers. Consumers enter the market sequentially and decide whether to accept one of the offers or to leave the market. Our data show that, especially in the first periods of the experiment, some sellers opt to pay high wages to their workers. However, this behavior is not rewarded by consumers, whose purchasing choices are almost exclusively driven by self-interest. In our interpretation, the connection between workers and sellers that connotes our experimental design, with workers who allow sellers to enter the market, may induce consumers to believe that eventual sacrifices for paying high wages to workers must be entirely on sellers. Our result suggests that the more salient is made the importance of some stakeholders in allowing the firm's activity, the fewer consumers' may be willing to sacrifice their monetary payoff to improve these stakeholders' condition. 相似文献
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Giacomo Bormetti Maria Elena De Giuli Danilo Delpini Claudia Tarantola 《Quantitative Finance》2013,13(5):769-780
In this paper we propose a novel Bayesian methodology for Value-at-Risk computation based on parametric Product Partition Models. Value-at-Risk is a standard tool for measuring and controlling the market risk of an asset or portfolio, and is also required for regulatory purposes. Its popularity is partly due to the fact that it is an easily understood measure of risk. The use of Product Partition Models allows us to remain in a Normal setting even in the presence of outlying points, and to obtain a closed-form expression for Value-at-Risk computation. We present and compare two different scenarios: a product partition structure on the vector of means and a product partition structure on the vector of variances. We apply our methodology to an Italian stock market data set from Mib30. The numerical results clearly show that Product Partition Models can be successfully exploited in order to quantify market risk exposure. The obtained Value-at-Risk estimates are in full agreement with Maximum Likelihood approaches, but our methodology provides richer information about the clustering structure of the data and the presence of outlying points. 相似文献
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In this work we detail the application of a fast convolution algorithm to compute high-dimensional integrals in the context of multiplicative noise stochastic processes. The algorithm provides a numerical solution to the problem of characterizing conditional probability density functions at arbitrary times, and we apply it successfully to quadratic and piecewise linear diffusion processes. The ability to reproduce statistical features of financial return time series, such as thickness of the tails and scaling properties, makes these processes appealing for option pricing. Since exact analytical results are lacking, we exploit the fast convolution as a numerical method alternative to Monte Carlo simulation both in the objective and risk-neutral settings. In numerical sections we document how fast convolution outperforms Monte Carlo both in speed and efficiency terms. 相似文献
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