本文選題：特質(zhì)風(fēng)險 + 分位數(shù)回歸��； 參考：《浙江工商大學(xué)》2012年碩士論文

【摘要】：根據(jù)經(jīng)典的資本資產(chǎn)定價理論,特質(zhì)風(fēng)險作為非系統(tǒng)風(fēng)險可通過投資組合進(jìn)行分散。Merton(1987)基于信息不對稱理論認(rèn)為：投資者只對自己熟悉的投資機(jī)會進(jìn)行投資選擇,公司特質(zhì)風(fēng)險難以被分散。特質(zhì)風(fēng)險的定價問題也隨之成為當(dāng)前金融領(lǐng)域研究的熱點之一。 然而,實證結(jié)論的不一致導(dǎo)致該問題爭議頗多。這些爭議可能是由定價模型本身的假定所引起,也可能是檢驗工具的不同所致。本文嘗試從定價模型選取和檢驗方法的改進(jìn)兩方面對特質(zhì)風(fēng)險的定價問題進(jìn)行探討。 具體而言,定價模型選取方面,定價模型本身的設(shè)定問題可能會影響檢驗結(jié)果。Fama-French三因子模型模型解釋了金融市場中不能被傳統(tǒng)資產(chǎn)定價模型所解釋的大量金融異象,并且成為特質(zhì)風(fēng)險定價研究的通用基準(zhǔn)依據(jù)。然而定價模型不同,特質(zhì)波動率是否會產(chǎn)生大的差異?本文嘗試采用Carhart四因子模型來計算特質(zhì)波動率,衡量特質(zhì)風(fēng)險。并比較基于不同定價模型,計算得到的特質(zhì)波動率的差異。另外,在檢驗特質(zhì)風(fēng)險定價情況的過程中,筆者認(rèn)為常用的均值回歸模型難以全面衡量特質(zhì)風(fēng)險的溢價情況。本文首次嘗試采用分位數(shù)回歸的方法研究對于不同收益率部分特質(zhì)風(fēng)險與橫截面收益的關(guān)系,探討對于不同收益率部分特質(zhì)風(fēng)險的定價是否存在顯著差別。 本文以A股市場為研究對象,從以上兩個新的視角檢驗中國股票市場特質(zhì)風(fēng)險是否被定價。主要結(jié)論如下：1.在特質(zhì)風(fēng)險的估算階段,動量因子的加入使特質(zhì)風(fēng)險的估計結(jié)果略小于采用FF.3模型的估計結(jié)果。這說明信息傳遞速度對特質(zhì)風(fēng)險的估算有一定的影響。2.根據(jù)不同分位點的回歸結(jié)果發(fā)現(xiàn),與均值回歸模型估計結(jié)果不同,特質(zhì)風(fēng)險并不是簡單的折價或者溢價。特質(zhì)風(fēng)險不僅伴隨時變性,而且對于不同收益率部分特質(zhì)風(fēng)險的定價情況存在顯著差別。隨著分位點的變化,特質(zhì)風(fēng)險與橫截面收益的回歸系數(shù)出現(xiàn)了在低分位點不顯著或顯著為負(fù)的情況；而在高分位點,特質(zhì)風(fēng)險在1%的顯著性水平下存在溢價情況。這說明,在低收益率部分特質(zhì)風(fēng)險的定價情況不穩(wěn)定,特質(zhì)風(fēng)險溢價、折價情況都有存在：在高收益率部分特質(zhì)風(fēng)險不存在異常收益之謎。這也表明,相較單一的均值回歸模型,分位數(shù)回歸的方法在解釋特質(zhì)風(fēng)險定價情況時更為全面。 另外本文分析發(fā)現(xiàn)規(guī)模、賬面市值比、換手率、流動性、反轉(zhuǎn)效應(yīng)等公司特征變量對特質(zhì)風(fēng)險的定價問題沒有顯著性影響。
[Abstract]:According to the classical capital asset pricing theory, idiosyncratic risk as a non-systematic risk can be dispersed by portfolio. Merton 1987. based on the information asymmetry theory, it is believed that investors only make investment choices to their familiar investment opportunities. Corporate trait risk is difficult to spread out. The pricing of idiosyncratic risk has become one of the hotspots in the field of finance. However, the inconsistency of empirical conclusions leads to a lot of controversy on this issue. These disputes may arise from the assumptions of the pricing model itself or from the differences in the testing tools. This paper attempts to discuss the pricing of idiosyncratic risk from two aspects: the selection of pricing model and the improvement of test methods. In particular, in the aspect of pricing model selection, the problem of pricing model itself may affect the test results. Fama-French three-factor model explains a large number of financial anomalies that cannot be explained by traditional asset pricing models in financial markets. And become the general benchmark basis for the study of idiosyncratic risk pricing. However, if the pricing model is different, will the volatility of traits vary greatly? This paper attempts to use the Carhart four-factor model to calculate the volatility of trait and measure the risk of idiosyncrasy. And compare the differences of idiosyncratic volatility based on different pricing models. In addition, in the process of testing the pricing of trait risk, the author thinks that the commonly used mean regression model is difficult to measure the premium of trait risk. This paper first attempts to use the quantile regression method to study the relationship between the cross-section return and the partial trait risk of different returns, and to explore whether there are significant differences in pricing the partial trait risk with different rates of return. In this paper, A-share market is taken as the research object to test whether idiosyncratic risk of Chinese stock market is priced from the above two new perspectives. The main conclusions are as follows: 1. In the stage of estimating trait risk, the addition of momentum factor makes the estimation result of trait risk slightly smaller than that of FF.3 model. This shows that the speed of information transfer has a certain impact on the estimation of trait risk. 2. According to the regression results of different loci, it is found that trait risk is not a simple discount or premium, which is different from that estimated by the mean regression model. Trait risk is not only accompanied by time-varying, but also has significant difference in pricing of partial trait risk with different yield. With the change of loci, the regression coefficient between trait risk and cross-sectional income was not significant or significantly negative at the low score, while at the high score, there was a premium at the significant level of 1%. This shows that in the low yield part of idiosyncratic risk pricing is unstable, the trait risk premium and discount exist: in the high yield part of the trait risk there is no abnormal return mystery. It also shows that the quantile regression method is more comprehensive in explaining the pricing of trait risk than the single mean regression model. In addition, this paper finds that the scale, book market value ratio, turnover rate, liquidity, reverse effect and other company characteristics have no significant impact on the pricing of idiosyncratic risk.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：F224;F830.91

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