Probabilities of Default RMI PDs CVI 7-8 KMV 9 KMV KMV KMV 1. KMV KMV DPT DD DD DD DPT Step 1 V E = V A N d 1 - e rt DN d 2 1 d 1 = ln V A

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1 KMV KMV CVI KMV CVI ARIMA F A CVI KMV KMV Black Scholes Merton BSM BSM Ozge Gokbayrak Lee Chua KMV 1 Douglas Dwyer Heather Russell KMV EDF KMV KMV 4 KMV RMI Corporate Vulnerability Index CVI RMI J. - C. Duan BTJ030 SJZZ15_

2 Probabilities of Default RMI PDs CVI 7-8 KMV 9 KMV KMV KMV 1. KMV KMV DPT DD DD DD DPT Step 1 V E = V A N d 1 - e rt DN d 2 1 d 1 = ln V A /D + r + σ2 A 2 T d 2 = d 1 - σ A 槡 T σ A = V A N d σ A 槡 T V 1 σ A E N V E D r T V E σ A 1 Step 2 DD EDF DD = E VT A - DPT σ A 2 DD DPT E V T A DPT σ A KMV EDF EDF = N - DD = 1 - N DD 3 N 2. KMV 72

3 JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS KMV 1 KMV ST * ST ST * ST DPT = SD LD t = R 2 = R 2 DW = DW t / = * / 1 - * = 1-3. CVI CRI CVI 8 1 CVI CVIvw CVIvw 2 CVI CVIew CVIew 3 CVI CVItail 5% CVItail CRI 4. CVI ARIMA ARIMA 1 ARIMA Y t d Z t Z t = Δ d y t = 1 - B d y t 4 ARIMA p q Z t = c + φ 1 z t φ p z t-p + ε t + θ 1 ε t θ q ε t -p 5 d ARIMA p d q p q ε t 2 ARIMA 73

4 Akaike information criterion AIC p q d AIC = ln e 2 t n + 2 k + 1 n AIC R AIC AIC KMV KMV KMV EDF EDF EDF DD EDF EDF EDF

5 JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS 1 10 EDF 75

6 EDF EDF EDF EDF EDF KMV CVI CVI 10 CRI 1. CVI CVIvw CVIvw 5 CVI CVIvw CVIvw CVIvw bp 2. CVI CVIew CVI CVIew CVIew CVIew CVIvw 3. CVI CVItail 10 5% 76

7 JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS CVI 5% 4 CVItail CVIew bp 5 CVI 4 CVItail bp bp R CVIvw CVIew CVItail ARIMA AIC 2 2 CVIvw AIC ARIMA CVIew CVItail AIC ARIMA 1 0 ARIMA

8 2. 2 CVIvw CVIvw CVIew CVItail CVIew CVItail ARIMA Inf Inf Inf ARIMA ARIMA ARIMA ARIMA ARIMA ARIMA ARIMA 2 1 Inf Inf Inf ARIMA CRI ARIMA ARIMA ARIMA CRI CVIvw CVIew CVItail CRI CVI CRI 6 CRI CVItail CRI 3 3 CVItail CRI CVItail 78

9 JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS CVItail ARIMA CRI CRI CVIew CVItail CVIvw CRI CVIew CVItail CVIvw CVItail ARIMA KMV 2008 EDF EDF CVItail ARIMA

10 1. 2. KMV 1 OZGE GOKBAYRAK LEE CHUA. Validating the public EDF TM model during the credit crisis in asia and europe J. Moody's analytics DOUGLAS DWYER HEATHER RUSSELL. CDS implied EDF credit measures and fair value spreads J. Moody's analytics KMV J KMV J KMV J J-C DUAN W MIAO T WANG. Stress testing with a bottom-up corporate default prediction model J. Under revision J-C DUAN T WANG. Measuring distance-to-default for financial and non-financial firms J. Global credit review NUS-RMI CVI. White paper credit research initiative technical report R. Global credit review CROSBIE P BOHN J. Modeling default risk-modeling methodology J. Moody's KMV company J J

11 JOURNAL OF NANJING UNIVERSITY OF FINANCE AND ECONOMICS Enterprise vulnerability measure and application research based on C hinese listed real estate enterprise data CHEN Yaohui ZHANG Jun ZHANG Yaqi ZHAO Tiantian School of Economics Nanjing University of Finance and Economics Nanjing China Abstract This paper has made improvements on the KMV model default point and the future value of the assets of the company. The default rate of Chinese small-sized and medium-sized listed real estate enterprises is obtained based on the improved KMV model which has constructed the index systems of three measures of the real estate industry enterprise vulnerability. The results show that in the third quarter of 2008 and the second quarter of 2015 China's real estate enterprise vulnerability were higher facing a higher default risk. The index system of CVI tail with the highest sensitivity is adopted to forecast China's real estate enterprise vulnerability from September 1to December 31st in 2016 whose result can become the standard and formulate the effective defense measures for the future real estate enterprise vulnerability in China. Key words KMV model defaultrate enterprise vulnerability CVI ARIMA 檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪 70 A study on the dynamic competitive relationship between the different stock index futures markets in China LIN Xiangyou YI Fanqi CHEN Chao School of Business Chengdu University of Technology Chengdu China Abstract Under the background of the launch of the CSI 300 stock index futures the SSE 50 stock index futures and the CSI 500 stock index futures with the data from the different stock index futures including the CSI 300 stock index futures the SSE 50 stock index futures and the CSI 500 stock index futures using the T test and Wilcoxon test to examine the differences before and after the changing of the trading rules for the stock index futures implemented on the September 7th 2015 adopting the Lotka-Volterra model to analyze the dynamic competitive relationships between the different stock index futures markets and to analyze the effects of the new trading rules of the stock index futures on the competitive relationships this paper has drawn the conclusions as follows from the angle of trading volume to analyze the competitive relationships the new trading rules of the stock index futures has significant effects on the competitive relationships between the different stock index futures markets and from the angle of open interest to analyze competitive relationships the new trading rules of the stock index futures has no significant effects on the competitive relationships between the different stock index futures markets. Key words stock index futures competitive relationship trading rules Lotka-Volterra model 81

θ 1 = φ n -n 2 2 n AR n φ i = 0 1 = a t - θ θ m a t-m 3 3 m MA m 1. 2 ρ k = R k /R 0 5 Akaike ρ k 1 AIC = n ln δ 2

θ 1 = φ n -n 2 2 n AR n φ i = 0 1 = a t - θ θ m a t-m 3 3 m MA m 1. 2 ρ k = R k /R 0 5 Akaike ρ k 1 AIC = n ln δ 2 35 2 2012 2 GEOMATICS & SPATIAL INFORMATION TECHNOLOGY Vol. 35 No. 2 Feb. 2012 1 2 3 4 1. 450008 2. 450005 3. 450008 4. 572000 20 J 101 20 ARMA TU196 B 1672-5867 2012 02-0213 - 04 Application of Time Series