1. Introduction
The spectacular growth of financial technology (FinTech), which describes the advances in technology that have the potential to transform the provision of financial services [
1,
2,
3,
4], has been altering the economic and financial landscape in recent years. Some well-known sectoral FinTech innovations include crowdfunding, mobile banks, digital currencies, and robo-advice [
5,
6,
7]. Despite the COVID-19 pandemic, global FinTech investment reached USD 105.3 billion in 2020, nearly doubling the amount of investments in 2018 [
8].
FinTech has shown great potential in financial inclusion [
9,
10] and production efficiency [
11,
12], but it may also amplify contagion and volatility in the market, which could threaten financial stability [
13]. Abundant evidence has been found for contagion within the FinTech industry. For example, Cheng, et al. (2022) [
14] confirmed the information contagion effect among crowdfunding platforms in China and revealed that all participants in crowdfunding are worse off due to the contagion. Caporale, et al. (2021) [
15] found a significant dynamic linkage between three mainstream cryptocurrencies and asserted that cyber-attacks affected the contagion mechanism between cryptocurrency returns and volatilities.
It is not strange to witness FinTech companies expanding into traditional financial services. For instance, the micropayment market in China is dominated by several FinTech companies. The intermarket linkages between FinTech and the traditional financial industry (TFI) have also been investigated in several recent studies [
16,
17,
18,
19], which can be explained by the following reasons: (I) FinTech can compete with the TFI because they conduct similar business [
20,
21]. (II) An increasing number of traditional financial institutions employ FinTech innovations in the operational process [
22,
23]. (III) A growing trend in the merger and acquisitions between traditional financial and FinTech sectors has been observed [
8,
24]. Consequently, because of the interconnections, the risk inherent to the FinTech industry is likely to transmit to the TFI, possibly entailing systemic risk or even threatening financial stability [
25], which has become an international phenomenon because of globalization resulting in increasingly close connections between financial markets in different countries, amplifying global systematic risks and calling for international cooperation on regulation. That means the intermarket linkages are worthy of the attention of regulators, which has been pointed out in previous studies. For instance, Li et al. (2020) [
26] examined the risk spillovers between FinTech firms and traditional financial institutions and proposed to enhance the supervision and regulation of FinTech companies in order to maintain financial stability. Haddad, C. and Hornuf, L. (2022) [
27] suggested that legislators and financial supervisory authorities should closely monitor the development of FinTech start-ups, because FinTech not only has a positive effect on the financial sector’s performance but also can improve financial stability.
FinTech is a complex and systematic project involving the participation of governments, markets, intermediary institutions, and other parties. It is necessary to understand the law of FinTech development from an ecological perspective, which calls for the construction of the FinTech ecosystem. According to Soloviev (2018a, 2018b) [
28,
29], the FinTech ecosystem is a collaboration among governments, financial institutions, and entrepreneurs. Lee et al. (2018) [
30] highlighted that the basic elements of the FinTech ecosystem include FinTech startups, technology developers, government organizations, clients, and traditional financial institutions. Although the FinTech ecosystem is characterized as being transformative [
31], the pattern of FinTech is still dependent on sudden shocks. The COVID-19 pandemic has affected the financial system dramatically and reshaped the landscape of finance [
32,
33,
34,
35,
36]. Ji, et al. (2022) [
37] proved the existence of contagion effects across financial markets in crisis. Bao and Huang (2021) [
38] revealed that the delinquency rate of FinTech loans tripled after the pandemic, whereas no obvious delinquency rate surges were observed for commercial banks. Lucey, et al. (2021) [
39] established a new cryptocurrency uncertainty index that reflects the uncertainty of cryptocurrency prices. They further revealed that the uncertainty of cryptocurrency policy and the index exhibited distinct movements around the outbreak of COVID-19. Some scholars examined the implications of COVID-19 on hedge and portfolio diversification for some FinTech products. Concretely, Diniz-Maganini, et al. (2021) [
40], Belhassine and Karamti (2021) [
41], and Kamran, et al. (2022) [
42] found that FinTech products such as Bitcoin can offer safe haven and hedging benefits in certain circumstances. Such studies indicate that the COVID-19 pandemic provided the discussion on the safe-haven property of FinTech assets with a new context and framework.
Similar research on this topic has been conducted, such as Chen, et al. (2022) [
43], who explored the volatility spillover dynamics between a FinTech ETF and the ETFs of the TFI with an empirical network model, and Li, et al. (2020) [
26], who estimated pairwise volatility spillover between FinTech firms and traditional financial institutions in the U.S. However, both employed a sample of the U.S. market and ignored China’s financial market, even though China is emerging as a leading FinTech market globally [
44]. In terms of the overall ranking of FinTech Development Index released by Sinan Institute of Academy of Internet Finance in Zhejiang University, China, with its huge advantage in application dimension, surpassed the United States, taking the first place in the world, which shows a strong growing trend of China’s FinTech industry globally [
45]. In addition, a study of British think tank
New Financial reveals that China is the third largest financial center in the world in 2021 [
46], indicating that China’s financial system has become an important part of the global financial system. As China’s FinTech industry and financial system occupy important places worldwide, volatility spillover dynamics and determinants between FinTech and traditional financial industry in China is worthy of further exploration. Moreover, the existing literature failed to cover the period of the COVID-19 pandemic, during which FinTech has developed rapidly and the landscape of traditional finance has been reshaped, possibly influencing the spillover patterns between them. In addition, previous studies hardly discussed the underlying determinants of volatility spillovers, making it difficult to distinguish the potentially different spillover patterns between FinTech and TFI. To bridge these research gaps, our study contributes to the existing literature in three aspects: first, we analyze the volatility spillover effect between FinTech and the TFI in China during the COVID-19 crisis. This study complements prior studies by covering a different set of assets and times. Second, instead of solely providing the total volatility spillover index [
43], this study integrates two variance–decomposition-based spillover measurement frameworks, namely Diebold and Yilmaz (2012) (DY) [
47] and Baruník and Křehlík (2018) (BK) [
48] to present a comprehensive measurement of volatility spillover, which is a methodological innovation. Finally, we examine the determinants of the total and decomposed dynamic volatility spillover. Specifically, we consider three types of determinants, namely economic fundamentals, risk contagion, and market attention, from theoretical and empirical perspectives.
To summarize what has been discussed above, we aim to explore two research questions in this paper: (I) What are the spillover patterns between FinTech and the TFI? That is to say, we want to figure out whether FinTech is a volatility exporter or receiver to TFI and the frequency-based spillover dynamics between FinTech and TFI; (II) what are the potential determinants of volatility spillover? To answer these questions, we initially employ DY and BK frameworks to explore the effects of volatility spillover between FinTech and TFI based on stock returns of a sample of financial institutions and FinTech firms in China’s A-Shares market, which is the largest and the most representative stock market of China. The research period is from June 2017 to October 2021, covering the fast growth of FinTech and the COVID-19 crisis. We then employ regression models to examine the role of possible underlying determinants in volatility spillover between FinTech and the TFI.
The remainder of this paper is organized as follows:
Section 2 introduces the methodology.
Section 3 describes the data with some preliminary analysis.
Section 4 presents the empirical findings. Finally,
Section 5 concludes the paper and presents some implications for market participants and policymakers.
4. Data
We use daily data from 9 June 2017 to 30 October 2021 to examine the spillover effect between FinTech and the TFI in China. As for FinTech parameters, we employ the CNI Xiangmi Lake FinTech Index (399699, abbreviated as FinTech) as FinTech parameters, which is the first publicly released and representative stock index that reflects the development of China’s FinTech industry. It takes FinTech enterprises whose stocks traded on the Shanghai and Shenzhen Stock Exchange that meet the criteria to constitute the index samples, and mainly selects listed companies whose business fields are in the FinTech industry and subsectors such as distributed technology, Internet technology, financial safety, and Internet finance. In terms of the industry distribution of constituent stocks of the CNI Xiangmi Lake FinTech Index, the information technology industry covers the largest proportion, accounting for 77.44% by the end of June 2023. Of all the constituent stocks, only four relate to finance, whose companies’ main business domains include information technology, digital finance, and software service, different from traditional finance.
We also need to define the scope of the TFI and classify it into sub-sectors to perform a comprehensive analysis. According to official statistics of the People’s Bank of China, traditional financial industry mainly consists of banking industry, insurance industry, and security industry [
91]. Diversified finance is a mixed system of multiple different forms and types of financial products, services, and institutions, experiencing rapid growth in recent years. Therefore, we select four indices referring to the second-level SWS index, namely SWS Banking Subsector Index (801192, abbreviated as Banking), SWS Insurance Subsector Index (801194, abbreviated as Insurance), SWS Securities Subsector Index (801193, abbreviated as Security) and SWS Diversified Financials Subsector Index (801191, abbreviated as Diversified) to track the performance of the TFI. There is hardly any overlap between the constituents of the indices representing TFI and the CNI Xiangmi Lake FinTech Index, separating FinTech from traditional finance. All these daily data on these indices are collected from Wind Financial Terminal.
Following Diebold and Yilmaz (2012) [
47] and Le, et al. (2021) [
18], we use daily variance as a proxy of daily volatility. For asset
on day
, the daily variance
is formulated as follows:
where
and
indicate the highest and lowest prices in asset
on trading day
, respectively.
can be transformed to annualized daily percent standard deviation (volatility) by
. The volatilities of FinTech, Banking, Insurance, Security, and Diversified are plotted in
Figure 2, and
Figure 3 displays the overall distribution together with the pairwise correlation between the volatilities under examination. We also present descriptive statistics for volatilities under examination in
Table 1. Because the spillover index is computed within a VAR framework, the augmented Dickey–Fuller (ADF) test is performed to examine the stationarity for the data. We can summarize several interesting facts, including (1) each index exhibits great variability during the sample period, especially facing a crisis (e.g., COVID-19 pandemic); (2) volatility persistence is observed, which is consistent with recent studies, such as Abakah, et al. (2022) [
92]; (3) the Security and Diversified indices are the most volatile, and FinTech index is less volatile. This finding is in line with the finding revealed in Le, et al. (2021) that KBW NASDAQ financial technology index is less volatile than the US dollar and oil [
11]; (4) volatilities under examination do not follow Gaussian distribution as shown in
Figure 3 and
Table 1; (5) the highest correlation between FinTech and other indices is the pair of FinTech and diversified financials, achieving 0.66 followed by the pair of FinTech and Security index; and (6) the results shown in the last row of
Table 1 imply that all volatility series are stationary.
We collect a variety of proxy variables of three types of potential determinants from the perspective of economic fundamentals, risk contagion, and market attention determinants to identify the determinants of volatility spillover between FinTech and the TFI. It is noteworthy that the economic fundamental used in some prior studies are typically collected at a monthly or even lower frequency [
56,
93]. We consider daily variables to better reflect the dynamics of volatility spillover and meet the requirement of daily NSI. The determinants are as follows:
- (I)
Economic fundamental determinants. The first type of economic fundamental determinant is the macroeconomic variable, which captures the status of the economic and financial environment. The following variables are selected after considering the limited number of macroeconomic variables available in China.
USD to CNY exchange rate (ER). Theoretically, changes in exchange rates tend to influence a firm’s foreign operation and profit, which will finally affect its stock price. The dynamic relationship between the exchange rate and the stock market has also been confirmed in emerging countries [
94,
95].
China financial condition index (CFCI). CFCI reflects the financial condition, financing accessibility, and measures that the monetary policy is either expansionary or contractionary. A high CFCI indicates a contractionary monetary policy, whereas a low CFCI implies the opposite case. Abundant evidence has shown that monetary policy is a critical driver of volatility spillover across markets [
96,
97]. We use the daily CFCI developed by CBN Research Institute.
The second type of economic fundamental determinant is related to major events (ME). The COVID-19 pandemic is a very major event between 2017 and 2021 and was found to cause an increase stock market volatility dramatically [
67]. We use a dummy variable to represent the COVID-19 pandemic. Concretely, China’s COVID-19 outbreak period (1 December 2019 to 28 April 2020, defined by Fighting COVID-19: China in Action) is denoted as 1, and the remaining observations are labeled as 0.
- (II)
Risk contagion determinant. Risk contagion determinant is valid when increments and decrements in the same direction are observed in markets volatility and volatility spillovers. Inspired by Jiang, et al. (2022) [
98], we employ weighted average volatility (WAV) of FinTech, Banking, Security, Diversified, and Insurance indices as a proxy of risk contagion.
- (III)
Market attention determinant. The growing literature has built theoretical framework and empirical models to demonstrate market attention, which is uncorrelated with fundamentals and has a great effect on the volatility spillover of a financial asset [
81,
87]. Market attention is typically measured by the Google search volume index (GSVI) in previous studies, which was proposed by Da et al. (2011) [
99], who argued that search activity is a revealed attention measure. For instance, if an individual searches for a certain stock in Google, she is interested in the stock and pays attention on it. However, because local retail investors account for a major part of trading volume in China’s stock market, we employ the Baidu index, a type of search volume index similar to GSVI, which is powered by the most used search engine in China. The Baidu index shows the search volumes for certain keywords over a given period. Because we focus mainly on the volatility spillover between the TFI and FinTech in China, we search keywords of “banking” , “security” , “insurance”, “diversified financials” and “FinTech” in Chinese and sum up the daily search volumes (DSV) of the keywords as a proxy of market attention.
Table 2 shows the daily correlation matrix for regression variables. Some highlights of the correlation between TSI and determinants can be found. Consistent with the theoretical analysis, the three types of determinants are correlated with TSI at a 5% significance level.
6. Conclusions and Implications
This paper explores the dynamics and determinants of volatility spillover between FinTech and the TFI. We initially measure the volatility spillover indices using the methods proposed by Diebold and Yilmaz (2012) [
47] and Baruník and Křehlík (2018) [
48]. Subsequently, three potential types of determinants, namely economic fundamentals, risk contagion, and market attention variables, are considered from theoretical and empirical perspectives. The key findings are presented as follows. First, the total spillover between FinTech and the TFI is time-varying and exhibits an inverse U-shape between 2017 and 2021. Second, FinTech is a net volatility spillover receiver in most cases, and exports volatility to the TFI during the outbreak of the COVID-19 pandemic. Third, the long-term components are the main driving force of volatility spillovers based on the frequency domain spillover decomposition. Fourth, the economic fundamental determinants are the main contributing factors of volatility spillovers, explaining over 60% of total spillover between FinTech and the TFI.
In light of the above findings, this study has meaningful implications for market participants and policymakers. For investors, stocks in financial sectors have long been regarded as risk aversion choices because of their stable return and high liquidity. However, we witness a volatility spillover from FinTech to the TFI, especially encountering major exogenous shocks. This finding may challenge the stereotype of holding stocks in financial stocks when building risk-aversion portfolios. Investors can adjust their investment strategies in time accordingly.
These results also have important policy implications, as we underscore the importance of enhancing the supervision and regulation of financial markets and FinTech companies to maintain financial stability since volatility spillover has been found to be bidirectional. We should maintain and reasonably promote the level of financial openness, and gradually improve the exchange rate system, as exchange rate was proved to be a significant determinant of volatility spillover. Our findings also highlight the necessity of considering financial stability when performing monetary policy. Concretely, during the economic resurgence in the post-COVID-19 period, expansionary monetary or fiscal policy is routinely employed by policymakers. However, expansionary monetary or fiscal policy may also lead to a loose financial condition, which increases the volatility spillover between FinTech and the TFI based on our empirical analysis. Consequently, policymakers must strike a balance between financial stability and economic recovery in the post-pandemic period. Since China has become the second largest economy in the world, with its FinTech industry advancing rapidly, the case study of China can provide referential experience for other countries.
This study has some shortcomings that could be addressed in further studies. To begin with, we conducted the research by using macro-level data instead of corporate-level data. It is predictable that corporate-level data can have better variability and further studies can use them to analyze corporate-level earnings or volatility connectedness. Second, the methodology can be improved. Concretely, the VAR-based model can be extended to the quantile VAR-based model to explore the characteristics of the returns or volatility connectedness under different decimal places in further research. Moreover, we only focused on China’s market and further studies can go beyond the case of China since this is a hot topic globally.