發(fā)布時(shí)間：2018-12-12 21:24

【摘要】：隨著近年我國經(jīng)濟(jì)的高速騰飛,國民收入的迅猛增長,居民對保險(xiǎn)的依賴程度逐步加強(qiáng),我國保費(fèi)收入也呈現(xiàn)逐年增長的趨勢。因此,我們必要尋找科學(xué)的方法對保費(fèi)收入進(jìn)行準(zhǔn)確的預(yù)測。由于保費(fèi)收入具有明顯的季節(jié)性特征,本文采用先定季節(jié)指數(shù)方法和X-12方法對保費(fèi)收入進(jìn)行分析,結(jié)果表明X-12方法更應(yīng)用于此數(shù)據(jù)的季節(jié)性特征分析。為更好地預(yù)測保費(fèi)收入的增長,本文首先建立季節(jié)性差分自回歸滑動(dòng)平均模型(SARIMA)和基于X-12乘法模型的自回歸滑動(dòng)平均模型(X-12-ARIMA乘法模型)以及基于X-12加法模型的自回歸滑動(dòng)平均模型(X-12-ARIMA加法模型)。通過此三個(gè)模型之間的比較,表明SARIMA模型和X-12-ARIMA乘法模型明顯優(yōu)于X-12-ARIMA加法模型。然后,本文提出SARIMA和X-12-ARIMA乘法模型相結(jié)合的組合模型對保費(fèi)收入進(jìn)行最終預(yù)測。在此組合模型中,我們采用粒子群優(yōu)化算法對兩模型的權(quán)重進(jìn)行優(yōu)化。最后我們對中國主要保險(xiǎn)公司1999年1月至2013年6月間月度保費(fèi)總收入時(shí)間序列進(jìn)行實(shí)證分析,并對我國保費(fèi)收入變化趨勢進(jìn)行預(yù)測,從而為我國保險(xiǎn)業(yè)以及國家對于保險(xiǎn)業(yè)的監(jiān)管提供必要的支持。
[Abstract]:With the rapid development of Chinese economy and the rapid growth of national income in recent years, the reliance of residents on insurance is gradually strengthened, and the premium income of our country is also increasing year by year. Therefore, we need to find a scientific method to accurately predict premium income. Due to the obvious seasonal characteristics of premium income, the predefined seasonal index method and X-12 method are used to analyze the premium income. The results show that the X-12 method is more suitable for the seasonal characteristic analysis of this data. In order to better predict the growth of premium income, In this paper, we first establish seasonal differential autoregressive moving average model (SARIMA), autoregressive moving average model (X-12-ARIMA multiplication model) based on X-12 multiplication model and autoregressive sliding model based on X-12 addition model. Dynamic average model (X-12-ARIMA addition model). The comparison between the three models shows that the SARIMA model and the X-12-ARIMA multiplication model are obviously superior to the X-12-ARIMA addition model. Then, the combined model of SARIMA and X-12-ARIMA multiplication model is proposed to predict the premium income. In this combined model, particle swarm optimization algorithm is used to optimize the weights of the two models. Finally, we analyze the time series of monthly premium income of major Chinese insurance companies from January 1999 to June 2013, and predict the trend of premium income in China. So as to provide the necessary support for the insurance industry and the national supervision of the insurance industry.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F842

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