房地产行业收益风险的提取与分析(基于马尔可夫法的风险值)

M. Zolfaghari, F. Faghihiyan
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摘要

今天,任何对任何经济活动进行投资的企图都需要对其活动的某些组成部分有所了解和了解。投资的一个重要组成部分是根据该活动的预期回报了解投资风险。该国投资的主要领域之一是住房投资,这可以直接或间接(通过金融市场)进行。在这方面,考虑到金融市场的性质,更有必要认识到间接投资对住房部门的风险。但是,尽管认识到风险的重要性,过去几年发展起来的量化方法并没有太大的发展。因此,本文提出了一个衡量房地产企业股票收益风险的新模型。该模型不仅能够弥补当前方法的许多不足,而且能够提取不同状态下股票收益的风险。该模型是基于“风险值”和参数方法上的马尔可夫过程设计的。该机制除了考虑了风险制度转移之外,还基于一组模型进行了设计,这些模型基于对称和非对称行为得到了各种正态和异常分布函数。结果表明:住房行业公司股票收益遵循制度转移,并得到了基于不对称模型的GED分布。
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An extraction and Analysis of the Return Risk of Real Estate Industry, (Based on Value at Risk Based on Markov Approach)
Today, any attempt to invest in any economic activity, requires the knowledge and access to some components of its activities. One of the important components of investment is knowledge about investment risk according to the expected return in that activity. One of the main areas of investment in the country is investment on housing, which could take place directly or indirectly (through financial markets). In this regard, the importance of awareness of the risk in indirect investment on the housing sector considering to the nature of the financial markets is more necessary. But despite the importance of knowledge about risk, quantification methods developed for the past few years have not had much development. Therefore in this research we proposed a new model for measuring the share return risk of companies in real estate industry. This model is not only able to cover much of the shortcomings of current methods, but also able to extract the risk of stock returns in different states. The present model has been designed based on the "value at risk" and using the Markov process on parametric methods. This mechanism, in addition to taking into account the risk regime transfers is designed based on a set of models that they have got a variety of normal and abnormal distribution functions based on symmetric and asymmetric behavior. The results showed that the return of the company’s stock in housing sector follows form regime transfers and has got the GED distribution based on asymmetrical models.
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