應用X-12-ARIMA與SARIMA模型及其組合模型對中國保費收入的預測研究
發(fā)布時間:2018-12-12 21:24
【摘要】:隨著近年我國經(jīng)濟的高速騰飛,國民收入的迅猛增長,居民對保險的依賴程度逐步加強,我國保費收入也呈現(xiàn)逐年增長的趨勢。因此,我們必要尋找科學的方法對保費收入進行準確的預測。由于保費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方法對保費收入進行分析,結果表明X-12方法更應用于此數(shù)據(jù)的季節(jié)性特征分析。為更好地預測保費收入的增長,本文首先建立季節(jié)性差分自回歸滑動平均模型(SARIMA)和基于X-12乘法模型的自回歸滑動平均模型(X-12-ARIMA乘法模型)以及基于X-12加法模型的自回歸滑動平均模型(X-12-ARIMA加法模型)。通過此三個模型之間的比較,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乘法模型相結合的組合模型對保費收入進行最終預測。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重進行優(yōu)化。最后我們對中國主要保險公司1999年1月至2013年6月間月度保費總收入時間序列進行實證分析,并對我國保費收入變化趨勢進行預測,從而為我國保險業(yè)以及國家對于保險業(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.
【學位授予單位】:蘭州大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:F224;F842
本文編號:2375267
[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.
【學位授予單位】:蘭州大學
【學位級別】:碩士
【學位授予年份】:2014
【分類號】:F224;F842
【參考文獻】
相關期刊論文 前10條
1 包慧敏;王戎;;ARMA模型在人身險業(yè)增長預測中的應用研究[J];北方經(jīng)濟;2009年08期
2 尹成遠;趙桂玲;周穩(wěn)海;;中國人身保險保費收入的實證分析與預測研究[J];保險研究;2008年01期
3 張鳴芳;中國季度GDP季節(jié)調整分析[J];財經(jīng)研究;2005年07期
4 傅庚,,唐小我,曾勇;廣義遞歸方差倒數(shù)組合預測方法研究[J];電子科技大學學報;1995年02期
5 高春玲;;時間序列分解模型在壽險保費收入預測的應用[J];北方經(jīng)貿;2010年06期
6 張云;高壘;;基于乘積季節(jié)模型的我國保費收入的預測研究[J];金融經(jīng)濟;2009年14期
7 張積林;;基于灰色理論的中國保險業(yè)保費規(guī)模預測[J];技術經(jīng)濟與管理研究;2010年01期
8 楊寧琳;;保險規(guī)模分析與預測方法述評[J];科技信息(科學教研);2007年33期
9 尹成遠;李兆濤;;基于ARIMA模型對我國保費收入的預測[J];求索;2012年01期
10 王永宏;饒繼廣;;基于ARIMA模型自動預測我國保險行業(yè)保費收入的應用和實踐[J];軟件產(chǎn)業(yè)與工程;2010年06期
本文編號:2375267
本文鏈接:http://sikaile.net/jingjilunwen/bxjjlw/2375267.html
最近更新
教材專著