Quantitative measurement and analysis of FinTech risk in China

Figures & data

Table 1. Assessment of changes in overall market capital risk.

	Mean	Standard deviation	Confidence level
			90%	95%	99%	99.5%
Net capital inflow loss (100 million yuan)	117.82	243.49	−213.82	−463.06	−528.64	−667.18
Proportion of total outstanding balance in the market (%)	4.34	4.81	−2.60	−4.53	−−5.85	−6.98
Volume loss (100 million yuan)	15.32	153.90	−173.85	−254.27	−309.69	−391.95
Proportion of total outstanding balance in the market (%)	1.17	2.97	−2.44	−3.24	−3.73	−4.07
Investment profit and loss (100 million yuan)	−1.08	2.26	−3.73	−4.43	−5.18	−9.00
Investment profit and loss (%)	−0.16	0.38	−0.60	−0.68	−0.81	−1.06

Source: Wangdaizhijia.

Table 2. Descriptive statistics of value at risk of different types of platforms.

Platform type	Mean	Median	Standard deviation	Skewness	Kurtosis	Jarque-Bera	ADF
Private	−0.858	−0.575	2.284	−1.149	7.061	44.46	−4.404
Banking	0.0207	−0.00176	1.074	−0.718	8.901	75.3	−5.838
Public	−0.0931	−0.0591	1.819	−0.271	5.977	18.7	−7.167
State-owned	0.0613	−0.0800	0.716	1.764	7.504	66.85	−6.569
Venture capital	−0.0138	−0.306	2.591	0.185	5.722	15.41	−5.844

Source: results calculated based on the data from Wangdaizhijia.

Table 3. The measurement of VaR and risk spillover effects of platforms.

	Platforms VaR	Platforms CoVaR	Whole industry CoVaR	Platforms △CoVaR
Private	−6.4905	−10.8664	−9.6936	−4.3760
Banking	−1.7251	−2.8889	−6.9709	−1.1638
Public	−2.7265	−4.1754	−7.2487	−1.4489
State-owned	−0.9248	−0.9706	−6.1404	−0.0458
Venture capital	−5.7491	−6.3247	−8.1790	−0.5756
	Whole industry △CoVaR	Risk spillover direction	Platforms %CoVaR	Whole industry %CoVaR
Private	−4.1131	−0.2629	67.42	73.70
Banking	−1.3904	0.2265	67.47	24.91
Public	−1.6682	0.2192	53.14	29.89
State-owned	−0.5599	0.5140	4.96	10.03
Venture capital	−2.5985	2.0229	10.01	46.56

Source: results calculated based on the data from Wangdaizhijia.

Figure 1. Changes in the number of risk platforms and transition platforms in China’s FinTech market from July 2011 to January 2019.

Source: Wangdaizhijia.

Table 4. Difference between actual quantity and predicted quantity of exited platforms.

		Mean	Median	SEM	T statistic	P
Risk platforms	Actual quantity	36.00	24	4.218
	Forecast quantity	35.82	24	3.312
	Actual quantity－Forecast quantity	0.18	0	5.363	0.0337	0.9732
Transition platforms	Actual quantity	37.82	34	4.426
	Forecast quantity	37.35	37	4.088
	Actual quantity－Forecast quantity	0.47	−3	6.026	0.0784	0.9376

Source: results calculated based on the data from Wangdaizhijia.

Figure 2. Comparison of risk contagion between risk platforms and transition platforms.

Source: Wangdaizhijia.

Table 5. Measurement of FinTech systemic risk contagion.

Total exited platforms	Risk probability (%)	Number of naturally exited platforms	Number of exited platforms due to risk contagion
Risk platforms
$0\le X<50$	71.795	$0\le X_N<37$	$0\le X_C<13$
$50\le X<100$	24.359	$37\le X_N<72$	$13\le X_C<28$
$100\le X<150$	2.564	$72\le X_N<90$	$28\le X_C<60$
$150\le X<200$	1.282	$71<X_N\le 90$	$60\le X_C<129$
Transition platforms
$0\le X<50$	67.949	$0\le X_N<24$	$0\le X_C<26$
$50\le X<100$	25.641	$24\le X_N<60$	$26\le X_C<40$
$100\le X<150$	5.128	$60\le X_N<91$	$40\le X_C<59$
$150\le X<200$	1.282	$91\le X_N<111$	$59\le X_C<89$

Source: results calculated based on the data from Wangdaizhijia.