Global Financial Crisis: Dynamics of Liquidity Risk in Emerging Asia

Abstract

This study investigates the dynamic impact of global and regional liquidity along with volatility on the liquidity of emerging Asian equity markets. Further, we empirically disentangle the effects of volatility and liquidity. We find that the external liquidity factors have a higher impact on domestic liquidity as compared to volatility. The impact of global volatility shocks was witnessed only during the Global Financial Crisis. Global factors have a higher influence on developed markets such as Japan and Singapore, while regional factors have a higher influence on emerging markets. These results indicate that liquidity serves as the channel of regional integration in Asia. The findings of this study provide useful insights to cross-sections of stakeholders in the investment industry.

JEL Classification: G15, F21, F36

Keywords

Liquidity volatility commonality Asia global regional

1. Introduction

Emerging markets attract cross-border investments due to higher asset valuations, supported by their economic growth. Yet global investors are often concerned about the lower liquidity and higher volatility of these markets. Apart from the domestic liquidity risk, these markets often receive liquidity shocks not only from their regional counterparts but also from the developed markets. This article examines the impact of regional and global factors on the liquidity of Asian equity markets.

Liquidity is the critical factor that determines the quality of the emerging Asian markets. It is a key driver of growth and development of the security markets in the emerging countries. It affects stock prices and returns (Amihud & Mendelson, 1986; Chordia et al., 2008) and thus forms the basis of the capital inflows into the region. Financial market liberalisation coupled with increasing capital inflows requires an in-depth investigation for one to understand the impact of external factors on domestic market liquidity in Asia.

Previous research, especially after the Global Financial Crisis, discussed financial market integration mainly in terms of stock returns and volatilities (Diebold & Yilmaz, 2009; Engle, Gallo, & Velucchi, 2012; King & Wadhwani, 1990). Recent research, however, has heightened focus on market liquidity, which, apart from volatility, is an equally important risk factor for understanding market interdependencies.

During the 2008 Global Financial Crisis, there was a sharp rise in liquidity commonality, which engrossed both the investors and policymakers (Chang et al., 2016). Liquidity was also noted as a critical factor that contributed to the spiralling effect of the financial crisis (Brunnermeier, 2009; Gorton, 2009). Liquidity commonality increases when markets witness higher volatility (Karolyi, Lee, & Van Dijk, 2012). Such a phenomenon gets magnified when the emerging markets encounter correlated trading activity by international investors. Contemporary research is yet to examine the external effects on the domestic liquidity of the Asian markets. This article addresses the unexplored effects of external liquidity and volatility on the liquidity of Asian markets.

This study examined the liquidity of nine Asian markets, which include China, Japan, Singapore, India, South Korea, Philippines, Malaysia, Thailand and Taiwan. Global markets are represented by G5 countries, but since Japan lies in Asia we replaced it with Australia based on its trade relationship with Asia. Hence, the global market represents the USA, the UK, Germany, France and Australia. The study period is from January 2006 to December 2016, which enabled us to analyse the liquidity of these markets, both during tranquil and crisis periods.

Using the cross-country data of 14 markets, we found that both global and regional liquidity factors are critical to explain the time-varying liquidity risk in emerging Asia. We also documented many critical differences between the impact of volatility and liquidity. First, liquidity factors have a higher impact on domestic liquidity as compared to volatility. Second, the impact of global volatility shocks was witnessed only during the Global Financial Crisis. During this time, the regional volatility impact faded away. Third, there was a differential impact of global liquidity and volatility across the developed and emerging markets. Global factors have a higher influence on developed markets such as Japan and Singapore, while regional factors have a higher influence on emerging markets. Fourth, liquidity commonality increased in emerging markets in the post-crisis period.

Our findings provide useful insights to a cross-section of stakeholders in the investment industry. Traders and investors will benefit from understanding the differential impact of liquidity and volatility across the Asian markets. These findings will facilitate effective asset allocation and hedging strategies for cross-border investors.

Liquidity and volatility linkages are also of particular interest to policymakers and market regulators in understanding the inter-relationships across countries and the level of regional integration. They are equally concerned about cross-border linkages since liquidity shocks from one market may spread to others and have a devastating effect on the region.

The rest of the article proceeds as follows. The second section discusses the literature review, the third section describes the methodology, the fourth section reports the empirical results and the fifth section concludes the study.

2. Literature Review

Liquidity plays a major role in investment risk management . Contemporary literature has focused on liquidity commonality and linkages of individual countries with other markets (Acharya & Pedersen, 2005; Fabre & Frino, 2004; Galariotis & Giouvris, 2007; Koch et al., 2016). Liquidity commonality refers to the extent to which each country’s illiquidity premium co-varies with that of the global and regional average (Amihud, Hameed, Kang, & Zhang, 2015). Liquidity commonality is measured using alternative liquidity proxies, both within a country and across countries. While emerging Asian markets exhibit commonality in spread, North American markets have commonality in depth (Brockman, Chung, & Pérignon, 2009; Karolyi et al., 2012; Zhang, Cai, & Cheung, 2009). Risk premium for illiquidity is also found to be common across 45 financially integrated stock markets (Amihud, 2002; Amihud et al., 2015).

However, global investment decisions do not consider liquidity risk in isolation; they also consider other factors that affect market liquidity, such as, market activity, depth and return variance (Copeland & Galai, 1983).

Portfolio managers are specifically concerned about volatility at the market level and its effects on liquidity, while investing in emerging markets. Apart from investors, market regulators and policymakers are also concerned about market-wide liquidity and volatility linkages. The relationship between liquidity and volatility is well postulated. The market microstructure theory proposes a positive relationship between illiquidity and volatility, which has been empirically confirmed (Amihud & Mendelson, 1989; Bao & Pan, 2013; Statman, Thorley, & Vorkink, 2006; Stoll, 1978, 2000). Higher volatility leads to higher inventory risk and higher bid-ask spread (Benston & Hagerman, 1974). Tighter risk management during higher volatility reduces market liquidity and increases liquidity commonality (Garleanu & Pedersen, 2007). Uncertainty in the market impacts liquidity, which in turn leads to liquidity co-movements (Chung & Chuwonganant, 2014). Volatility-induced liquidity risk cannot be diversified and hence has to be built into the required return of investors. Thus, stock market liquidity and volatility are noted to be highly correlated factors as both are driven by common factors (Chordia, Sarkar, & Subrahmanyam, 2005). Thus, the present study investigated the dynamic impact of both liquidity and volatility on the liquidity of emerging Asian equity markets and empirically disentangled both the effects.

Liquidity and volatility exhibit cross-market linkages due to the lead-lag effects of trading activities. Given the linkages, the trading activity in one market helps predict the liquidity and volatility in other markets. The trading volume in the USA, which indicates domestic market liquidity, influences the return volatility of the UK and Japan (Lee & Rui, 2002). Similarly, the volatility of US markets exerts a significant influence on the trading volume (liquidity measure) of Asian markets (Gębka, 2012). It is also noted that higher liquidity commonality in emerging markets is due to higher co-movements in stock volatility (Bai & Qin, 2015).

Compared to other regions, Asia is culturally and economically more diverse. After the 1997–1998 Asian financial crisis, Asian countries have consciously taken many initiatives to strengthen financial cooperation and integration in the region (Yu et al., 2010). Many studies have explored the channels through which the Asian markets are integrated. Regional integration has implications for the regulators to ensure growth and market stability. While integration helps emerging Asia to increase trade and investment linkages, it also intensifies the risk of financial contagion. This research study provides insights about the integration of the Asian markets through the liquidity channel.

3. Methodology

This study has explored the relative importance of regional (within Asia) versus global factors on the Asian equity market liquidity.

3.1 Data Description

Following Morgan Stanley Capital International (MSCI) market classification (May 2017), China, India, South Korea, Philippines, Malaysia, Thailand and Taiwan have been considered as emerging Asian markets. In addition, Japan and Singapore, which are considered as developed markets in the Asian region, have been included in the study so as to extract regional volatility and liquidity factors. For the purpose of obtaining global market liquidity and volati-lity factors, the USA, the UK, Germany, France and Australia have been considered on the basis of their trade linkages and capital inflows with the Asian region.

For obtaining market-level liquidity and volatility, all the constituent stocks of the liquid index of the respective country were taken into consideration. The indices considered for each country are reported in Table 1.

Table 1.
Market and Indices

Country Index

The UK FTSE AIM UK

Australia ASX 50

France CAC

Germany DAX

The USA DJIA

Singapore Straits Times Index

Japan TOPIX

Malaysia FTSE Bursa Malaysia KLCI

South Korea KOSPI

India NIFTY

Philippines PCOMP

Thailand SET 50

China SSE

Taiwan TWSE

Country	Index
The UK	FTSE AIM UK
Australia	ASX 50
France	CAC
Germany	DAX
The USA	DJIA
Singapore	Straits Times Index
Japan	TOPIX
Malaysia	FTSE Bursa Malaysia KLCI
South Korea	KOSPI
India	NIFTY
Philippines	PCOMP
Thailand	SET 50
China	SSE
Taiwan	TWSE

Source: The authors.

Note: The table presents the liquid index considered in the study in respect of each country.

3.1.1 Liquidity Measures

The Amivest measure is used in the analysis as the liquidity proxy. The daily Amivest measure was calculated for each individual stock of the index, and then the daily value weighted average of the index was computed. The weekly average of this measure was considered to represent liquidity at the market level. The data represent 574 weekly observations for 2006–2016 for each country. The end-of-day stock price data in US dollars for all the countries were sourced from the Bloomberg database.

Amivest is the inverse of the Amihud illiquidity ratio and is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ , where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t, and it is multiplied by 10⁶ to scale up the value. In addition to this measure, inverse of quoted spread was also used for the analysis. Quoted spread is calculated at the end of each trading day, taking the difference of ask and bid prices and dividing the same by its midpoint. Since the results were similar for both the liquidity proxies, the results of the Amivest liquidity measure are reported in the article for brevity.

3.1.2 Volatility Measure

We have measured the weekly volatility of the market from their respective daily stock price data. The high and low prices of a stock in a week were considered to calculate volatility. We measured it as v_t = ln(P_t^H/P_t^L × 100), where P_t^H and P_t^L are high and low prices of the week, respectively. Then, the value weighted average of all the stocks in the given index was considered as volatility at the market level. This is a model-free estimator of volatility and has been reported as an efficient estimator (Alizadeh, Brandt, & Diebold, 2002). Parkinson (1980) showed that $\frac{1}{4 \ln 2} v_{t}^{2}$ is an unbiased estimator of true variance.

3.2 Data Summary

Table 2 reports the descriptive statistics of weekly volatility and liquidity measures. The table shows that Japan had the highest liquidity level in Asia, while China, Korea and Taiwan had the higher liquidity levels among the emerging markets. Among the developed countries, the USA exhibited higher liquidity, while the UK had exceptionally low liquidity levels during the study period, a fact that was noted in a European study too (Smimou & Khallouli, 2016). All the markets were negatively skewed which indicates lower liquidity during the study period. Extremely down market increases liquidity skewness (Hsieh, Li, McKillop, & Wu, 2018). Negative skewness indicates asymmetric nature of markets (Wu & Xiao, 2002), which is more apparent during market crashes. Markets also exhibited higher kurtosis, which indicates fatter tails and frequent occurrences of extreme values. Drift and turns in the markets caused more frequent surges in liquidity.

Table 2.
Descriptive Statistics of Liquidity and Volatility Measure

Amivest Mean Median Std Dev. Skewness Kurtosis Jarque-Bera

A. Liquidity

Australia 6.23 6.47 1.60 −1.05 4.48 158

The UK 0.20 0.32 1.54 −0.68 3.56 51

The USA 9.65 9.66 0.47 −0.57 3.78 46

France 8.46 8.56 0.62 −0.96 4.58 148

Germany 8.54 8.67 0.68 −1.26 4.73 225

Japan 8.58 8.55 0.77 −0.09 2.11 20

Singapore 6.66 6.87 0.95 −1.27 4.83 233

China 7.76 8.02 1.16 −1.02 4.47 148

India 5.87 6.02 0.95 −1.04 6.14 338

Korea 7.64 7.69 0.49 −1.17 6.02 350

Malaysia 5.43 5.72 1.05 −1.33 5.14 278

Philippines 1.52 1.62 1.66 −0.30 2.45 16

Taiwan 6.82 6.96 0.67 −1.21 5.11 243

Thailand 4.44 4.75 1.92 −0.69 2.71 47

B. Volatility

Australia 1.18 1.03 0.58 2.34 11.74 2352

The UK 1.31 1.13 0.69 3.57 22.77 10573

The US 1.29 1.03 0.90 3.51 21.58 9442

France 1.61 1.40 0.84 2.31 11.60 2279

Germany 1.64 1.45 0.91 2.73 14.77 4025

Japan 1.48 1.27 0.81 3.16 18.21 6490

Singapore 1.40 1.17 0.83 4.49 38.15 31481

India 1.80 1.40 1.17 2.66 13.15 3140

China 2.23 1.97 1.14 1.60 6.97 609

Korea 1.40 1.17 0.83 4.49 38.15 31481

Malaysia 0.80 0.68 0.42 2.31 11.30 2161

Philippines 1.18 1.08 0.51 2.11 11.44 2124

Thailand 1.50 1.28 0.78 2.93 17.01 5510

Taiwan 1.54 1.33 0.72 2.00 9.14 1263

Amivest	Mean	Median	Std Dev.	Skewness	Kurtosis	Jarque-Bera
A. Liquidity
Australia	6.23	6.47	1.60	−1.05	4.48	158
The UK	0.20	0.32	1.54	−0.68	3.56	51
The USA	9.65	9.66	0.47	−0.57	3.78	46
France	8.46	8.56	0.62	−0.96	4.58	148
Germany	8.54	8.67	0.68	−1.26	4.73	225
Japan	8.58	8.55	0.77	−0.09	2.11	20
Singapore	6.66	6.87	0.95	−1.27	4.83	233
China	7.76	8.02	1.16	−1.02	4.47	148
India	5.87	6.02	0.95	−1.04	6.14	338
Korea	7.64	7.69	0.49	−1.17	6.02	350
Malaysia	5.43	5.72	1.05	−1.33	5.14	278
Philippines	1.52	1.62	1.66	−0.30	2.45	16
Taiwan	6.82	6.96	0.67	−1.21	5.11	243
Thailand	4.44	4.75	1.92	−0.69	2.71	47
B. Volatility
Australia	1.18	1.03	0.58	2.34	11.74	2352
The UK	1.31	1.13	0.69	3.57	22.77	10573
The US	1.29	1.03	0.90	3.51	21.58	9442
France	1.61	1.40	0.84	2.31	11.60	2279
Germany	1.64	1.45	0.91	2.73	14.77	4025
Japan	1.48	1.27	0.81	3.16	18.21	6490
Singapore	1.40	1.17	0.83	4.49	38.15	31481
India	1.80	1.40	1.17	2.66	13.15	3140
China	2.23	1.97	1.14	1.60	6.97	609
Korea	1.40	1.17	0.83	4.49	38.15	31481
Malaysia	0.80	0.68	0.42	2.31	11.30	2161
Philippines	1.18	1.08	0.51	2.11	11.44	2124
Thailand	1.50	1.28	0.78	2.93	17.01	5510
Taiwan	1.54	1.33	0.72	2.00	9.14	1263

Source: The authors.

Notes: The table presents the descriptive statistics of weekly liquidity in panel A and volatility in panel B for each country. Daily Amivest measure is averaged for a week and is used as proxy of liquidity. Liquidity (Amivest) of each stock is calculated as ln, ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then, the value weighted average of all the stocks in the given index was considered as liquidity and volatility at the market level for each day which was then averaged for a week. Volatility of each stock in the index was calculated as v_t = ln (P_t^H/P_t^L × 100), where P_t^H and P_t^L are high and low prices of the week, respectively.

China and India exhibited higher volatility among the Asian markets. Higher standard deviation also reveals higher dispersion for both the countries. Skewness and kurtosis are higher for volatility. J-B statistics confirms that the both the variables are non-normal.

Figure 1 presents Amivest liquidity measure of all the countries. The graph depicts liquidity co-movement across the countries despite varied levels of liquidity of each market. Further, it reveals that market-level liquidity exhibits the lag effect of the Global Financial Crisis since a drop in the liquidity levels across all the markets is witnessed for 2009. The same observation is also noted in Table 3 that presents structural breaks in the liquidity level of equity markets.

Figure 1.

Source: The authors.

Table 3.

Structural Breaks in Market Liquidity from 2006 to 2016

Year	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015
The UK			4/13		3/14	01/09		5/19
Australia	–	–	–	–	–	–	–	–	–	–
France		12/30		9/20
Germany				12/06			12/02			5/17
The USA			3/16	11/08				10/27
Malaysia		09/02			12/05
Korea				08/02			08/12		11/23
India	–	–	–	–	–	–	–	–	–	–
Philippines				7/26
Thailand				4/26		2/20
China		9/16		5/17			11/18		07/06
Singapore				7/19			3/18
Japan			1/20					3/17
Taiwan				4/12		07/10		03/03

Source: The authors.

Note: This table reports the significant structural break points in the Amivest liquidity measure during the sample period (from January 2006 to December 2016) based on the Bai and Perron (1998) test. The daily Amivest measure is averaged for a week and is used as the proxy of liquidity. Liquidity (Amivest) of each stock is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ where R_itis the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then, value weighted average of all the stocks in the given index is considered as liquidity at the market level for each day which is then averaged for a week.

3.2.1 Global and Regional Factors

Following the method used by Donadelli and Paradiso (2014) and Rughoo and You (2016), global and regional liquidity and volatility factors were obtained from the principle component analysis (PCA) of cross-country weekly data. Using weekly liquidity measure of all the five developed markets representing the USA, the UK, Australia, Germany and France, PCA was implemented. The first extracted component was considered to represent the global liquidity factor and the factor loadings have been used as input data for further analysis. In the same way, the global volatility factor was also obtained.

Similarly, Asian countries also share a common factor specific to this region, labelled as the regional factor. While extracting the regional factor for each country using PCA, that particular country was excluded in the analysis. To cite an example, while extracting regional factors relevant for Japan, all the other eight Asian markets were included and Japan was excluded in the PCA analysis (Bai & Qin, 2015; Chordia, Roll, & Subrahmanyam, 2000; Hameed, Kang, & Viswanathan, 2010). Figure 2 presents the time-varying global and regional liquidity factors which represent the first principle component. The figure clearly demonstrates the liquidity commonality in the Asian region along with the global markets.

Figure 2.

Source: The authors.

Table 4 reports the total variance explained by the first principal component in the cross-country analysis. In the analysis of both the global and regional contexts, the total variance explained by the first principle component was higher for volatility, as compared to liquidity. The regional analysis (Table 4) shows that the regional factors explain higher variance and co-movement, when the sample data exclude Philippines.

Table 4.

Total Variance Explained by First Principal Component

	Liquidity	Volatility
Global (the USA, the UK, Australia, Germany and France)	56.70	61.10
Regional
Japan	61.12	62.52
Singapore	58.62	60.00
China	62.78	66.79
India	60.44	61.58
Korea	60.25	61.21
Malaysia	62.37	62.78
Philippines	65.08	67.00
Taiwan	60.40	60.67
Thailand	58.76	63.06

Source: The authors.

3.2.2 Model Specifications

The impact of global and regional factors on the liquidity of individual countries was analysed using the following estimation models:

L_{t} = α_{i} + β_{1} G L i q + β_{2} G V o l + β_{3} L_{t - 1} + e_{t}

L_{t} = α_{i} + β_{1} R L i q + β_{2} R V o l + β_{3} L_{t - 1} + e_{t}

L_{t} = α_{i} + β_{1} G L i q + β_{2} R L i q + β_{3} L_{t - 1} + e_{t}

L_{t} = α_{i} + β_{1} G V o l + β_{2} R V o l + β_{3} L_{t - 1} + e_{t}

L_{t} = α_{i} + β_{1} G L i q + β_{2} R L i q + β_{3} G V o l + β_{4} R V o l + β_{5} L_{t - 1} + e_{t}

where L_t is the liquidity of a country and GLiq and RLiq are global and regional liquidities. Similarly, GVol and RVol are global and regional volatilities and L_t_–1 is the first lag of domestic liquidity.

3.3 Structural Breaks

Table 3 shows the significant structural breaks in the Amivest liquidity measure using the Bai and Perron (1998) test. Australia, the UK and Japan experienced their first structural break in liquidity during the first quarter (Q1) of 2008. Most of the emerging Asian markets, along with the USA, France, Germany and Singapore, exhibited structural breaks in 2009, which reveal the lag effect of the crisis on market liquidity. Australia and India did not display any significant breaks during the study period, thereby indicating their resistance to external shocks. On an average, in Asia, the emerging markets have experienced more number of significant breaks as compared to the developed markets. Based on the common structural break dates, data were subdivided into two panels, representing the pre- and post-structural break periods. The period from January 2006 to March 2009 represents the pre-break period, when the market experienced the Global Financial Crisis. The post-break period refers to the subsequent period, beginning from April 2009 to December 2016, when the market witnessed the after effects of the crisis.

4. Empirical Results

4.1 Impact of Global and Regional Factors

Global and regional factors affect liquidity commonality through different channels. Table 5 presents the separate impact of global and regional factors on the liquidity of each Asian market. Statistical significance of the first lag value of domestic liquidity indicates that the liquidity of each country is significantly affected by own-country factors. Global liquidity has a significant positive impact, while global volatility has a negative impact. A similar correlation was noted with respect to regional factors. Market liquidity, both at the global and regional levels, exerts higher impact as compared to volatility. The estimated coefficients, as well as the R-square of cross-country models, indicates that regional factors have a higher impact as compared to global factors on the Asian markets. Inverse quoted spread (not reported here) has similar results, except in the case of Taiwan.

Table 5.
Independent Analysis of Global and Regional Factors

Global
Regional

Liquidity Volatility Liquidity_i,t–1 R² Liquidity Volatility Liquidity_i,t–1 R²

Japan 0.189* −0.035 0.678* 0.83 0.097* −0.033 0.795* 0.82

Singapore 0.263* 0.019 0.588* 0.64 0.354* 0.004 0.494* 0.67

Thailand 0.166* −0.100* 0.829* 0.83 0.440* −0.138* 0.681* 0.85

Korea 0.139* −0.098* 0.460* 0.65 0.154* −0.118* 0.394* 0.67

Malaysia 0.211* 0.016 0.613* 0.54 0.300* −0.002 0.533* 0.56

India 0.080* −0.065* 0.784* 0.75 0.201* −0.111* 0.639* 0.77

China 0.059* 0.007 0.878* 0.81 0.137* −0.002 0.818* 0.82

Philippines 0.336* −0.167* 0.443* 0.38 0.540* −0.166* 0.348* 0.43

Taiwan 0.067* −0.066* 0.771* 0.80 0.094* −0.058* 0.741* 0.80

Global	Regional
Japan	0.189***	−0.035**	0.678***	0.83	0.097***	−0.033**	0.795***	0.82
Singapore	0.263***	0.019	0.588***	0.64	0.354***	0.004	0.494***	0.67
Thailand	0.166***	−0.100***	0.829***	0.83	0.440***	−0.138***	0.681***	0.85
Korea	0.139***	−0.098***	0.460***	0.65	0.154***	−0.118***	0.394***	0.67
Malaysia	0.211***	0.016	0.613***	0.54	0.300***	−0.002	0.533***	0.56
India	0.080***	−0.065***	0.784***	0.75	0.201***	−0.111***	0.639***	0.77
China	0.059***	0.007	0.878***	0.81	0.137***	−0.002	0.818***	0.82
Philippines	0.336***	−0.167***	0.443***	0.38	0.540***	−0.166***	0.348***	0.43
Taiwan	0.067***	−0.066***	0.771***	0.80	0.094***	−0.058***	0.741***	0.80

Source: The authors.

Note: This table reports the parameter estimates of Equations (1) and (2) from model specification. Daily Amivest measure is averaged for a week and is used as proxy of liquidity, which represents the dependent variable in the model. Liquidity (Amivest) of each stock is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then, value weighted average of all the stocks in the given index is considered as liquidity and volatility at the market level for each day which is then averaged for a week. Volatility of each stock in the index is calculated as v_t = ln(P_t^H/P_t^L × 100), where P_t^H and P_t^L are high and low prices of the, week respectively.

Global factor is the first principal component of the USA, the UK, Australia, Germany and France, whereas regional factors extracted for Japan include all Asian markets considered in the study excluding Japan. Similarly, liquidity factor of each country presented in the table represents all other regional countries excluding the respective country. Liquidity_i,t–1 is the first lag of Country_i liquidity representing domestic market influence. It is marked as *, ** and *** which represents p values <0.10, <0.05 and <0.01, respectively.

4.2 Liquidity and Volatility Factors: A Comparative Analysis

Table 6 presents a comparative analysis of global and regional liquidity and volatility. When global and regional liquidity were modelled jointly, both the factors had a positive significant impact on Japan and Singapore, which are the developed markets in Asia. Among the emerging markets, only Korea and Taiwan exhibited a significant impact of global liquidity. This is because of the higher trade relationships these countries share with the developed West. As per International Monetary fund (IMF) data, after China and Japan, it was Korea and Taiwan that had higher export and import turnovers. Appendix 1 maps the trade statistics of Asian countries. Taiwan is also one of the fastest-growing economies in Asia. The global liquidity factor had a negative impact on other emerging markets in Asia. This can possibly be explained as a flight to quality for portfolio balancing. The inverse relationship infers that when global markets experience lower liquidity, international investors turn to emerging markets in search of better investment opportunities.

Table 6 compares the impact of global, regional and domestic factors upon the liquidity of each of the Asian markets. Domestic factor (liquidity_i,t–1) has the highest influence, followed by regional factors, in respect of all Asian markets. Similar results were noted in the comparative analysis of volatility. While regional volatility had a significant negative impact, global volatility was found to have a positive insignificant impact on emerging Asian markets. Similar to that of global liquidity, a statistically significant impact of global volatility was noted only in Japan, Korea, Thailand and Taiwan. Higher R² of the liquidity models, as compared to that of the volatility models, reconfirms that liquidity is the more important factor for emerging markets. The impact of higher regional factors in the stand-alone models presented in Table 5, as well as in the joint models presented in Table 6, indicates that liquidity is an effective channel of regional integration in Asia. Inverse quoted spread had an almost similar result, except that global volatility had no significant effects on any country (results not reported for brevity).

Table 6.
Comparative Analysis of Global and Regional Factors

Liquidity
Volatility

Global Regional Liquidity_i,t–1 R² Global Regional Liquidity_i,t–1 R²

Japan 0.178* 0.043 0.670* 0.83 −0.056 −0.023 0.856* 0.81

Singapore 0.089 0.291* 0.480* 0.67 0.029 −0.133* 0.714* 0.61

Thailand −0.036 0.501* 0.710* 0.84 −0.034* −0.163* 0.850* 0.83

Korea 0.096* 0.121* 0.452* 0.64 −0.061* −0.099* 0.564* 0.62

Malaysia 0.009 0.294* 0.533* 0.56 0.067 −0.175* 0.668* 0.52

India −0.058 0.279* 0.677* 0.76 0.027 −0.170* 0.769* 0.75

China −0.093 0.229* 0.797* 0.82 0.069 −0.112* 0.873* 0.81

Philippines −0.039* 0.653* 0.359* 0.42 −0.01 −0.372* 0.481*** 0.37

Taiwan 0.037* 0.085* 0.763* 0.80 −0.062* −0.029 0.813* 0.79

Liquidity	Volatility
Japan	0.178***	0.043**	0.670***	0.83	−0.056**	−0.023	0.856***	0.81
Singapore	0.089**	0.291***	0.480***	0.67	0.029	−0.133***	0.714***	0.61
Thailand	−0.036	0.501***	0.710***	0.84	−0.034***	−0.163***	0.850***	0.83
Korea	0.096***	0.121***	0.452***	0.64	−0.061***	−0.099***	0.564***	0.62
Malaysia	0.009	0.294***	0.533***	0.56	0.067	−0.175***	0.668***	0.52
India	−0.058	0.279***	0.677***	0.76	0.027	−0.170***	0.769***	0.75
China	−0.093**	0.229***	0.797***	0.82	0.069	−0.112***	0.873***	0.81
Philippines	−0.039***	0.653***	0.359***	0.42	−0.01	−0.372***	0.481***	0.37
Taiwan	0.037*	0.085***	0.763***	0.80	−0.062***	−0.029	0.813***	0.79

Source: The authors.

Note: This table reports the parameter estimates of Equations (3) and (4) from model specification. Daily Amivest measure is averaged for a week and is used as the proxy of liquidity which represents the dependent variable in the model. Liquidity (Amivest) of each stock is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then, value weighted average of all the stocks in the given index is considered as liquidity and volatility at the market level for each day which is then averaged for a week. Volatility of each stock in the index is calculated as v_t = ln(P_t^H/P_t^L × 100), where P_t^H and P_t^L are high and low prices of the week, respectively.

Global factor is first principal component of the USA, the UK, Australia, Germany and France, whereas regional factors extracted for Japan include all Asian markets considered in the study excluding Japan. Similarly, liquidity factor of each country presented in the table represents all other regional countries excluding the respective country. Liquidity_i,t–1 is first lag of Country_i liquidity representing domestic market influence. It is marked as *, ** and *** which represents p values <0.10, <0.05 and <0.01, respectively.

4.3 Multifactor Models

Table 7 presents cross-market commonality in a multifactor model, wherein we included the liquidity and volatility factors from global and regional markets together with the domestic factors. Table 7 reconfirms that apart from domestic market influence, regional factors significantly impact market liquidity in Asia. Strong cross-market liquidity and volatility movements depicted by regional factors reiterate the level of Asian market integration.

Table 7.
Multifactor Analysis of Global and Regional Factors

Global Liquidity Regional Liquidity Global Volatility Regional Volatility Liquidity_i,t–1 R²

Japan 0.161* 0.055 −0.058 0.034 0.667* 0.83

Singapore 0.104* 0.288* 0.037 −0.013 0.478* 0.67

Thailand −0.074 0.495* −0.027 −0.123* 0.683* 0.85

Korea 0.058* 0.127* −0.070* −0.058* 0.352* 0.68

Malaysia 0.02 0.283* 0.042 −0.035 0.533* 0.56

India −0.092* 0.278* 0.019 −0.142* 0.624* 0.77

China −0.081 0.218* 0.074* −0.069* 0.788* 0.82

Philippines −0.128 0.646* −0.143 −0.059 0.347* 0.43

Taiwan 0.004 0.098* −0.075* 0.001 0.728* 0.81

	Global Liquidity	Regional Liquidity	Global Volatility	Regional Volatility	Liquidity_i,t–1	R²
Japan	0.161***	0.055**	−0.058**	0.034	0.667***	0.83
Singapore	0.104***	0.288***	0.037	−0.013	0.478***	0.67
Thailand	−0.074	0.495***	−0.027	−0.123*	0.683***	0.85
Korea	0.058***	0.127***	−0.070***	−0.058***	0.352***	0.68
Malaysia	0.02	0.283***	0.042	−0.035	0.533***	0.56
India	−0.092***	0.278***	0.019	−0.142***	0.624***	0.77
China	−0.081**	0.218***	0.074*	−0.069*	0.788***	0.82
Philippines	−0.128	0.646***	−0.143	−0.059	0.347***	0.43
Taiwan	0.004	0.098***	−0.075***	0.001	0.728***	0.81

Source: The authors.

Note: This table reports the parameter estimates of Equation (5) from model specification for full sample period. Daily Amivest measure is averaged for a week and is used as the proxy of liquidity which represents the dependent variable in the model. Liquidity (Amivest) of each stock is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then, value weighted average of all the stocks in the given index is considered as liquidity and volatility at the market level for each day which is then averaged for a week. Volatility of each stock in the index is calculated as v_t = ln(P_t^H/P_t^L × 100) where P_t^H and P_t^L are high and low prices of the week, respectively.

The statistically significant negative association of global factors with India and China, coupled with the positive association with global volatility, strengthens the evidence about flight to quality. These results are in line with the findings reported by Smimou and Khallouli (2016). The results indicate that India and China have evolved as emerging hubs for global investments. Other studies on China have also reported that there is a positive correlation between traded volume and external volatility (Andersen, 1996; Jones, Kaul, & Lipson, 1994). Inverse quoted spread exhibited similar results, except that global volatility had no significant impact on any market, other than Japan. To check the robustness of the results, the analytical model is also re-estimated using the Roger and Satchell volatility measure. It is noted that the results are similar and hence not reported in the article.

4.4 Global Financial Crisis: Liquidity Transmission

The crisis effects on the liquidity of emerging markets were examined through the subpanel analysis. Table 8 presents the results from January 2006 to March 2009, prior to the structural breaks in market liquidity. It was during this period that the markets encountered large liquidity surges due to Global Financial Crisis, and the combined explanatory power of domestic, regional and global factors, as reflected by R², reduced marginally. Further, in contrast to the previous observation, during this period, global volatility had a significant negative association with the Asian markets. The results lead us to infer that global volatility has a higher influence, as compared to global liquidity, in the region. The effects of volatility shocks on emerging Asian markets were exacerbated during this period. The results are in consensus with the findings of Kalimipalli, Nayak, and Perez (2013), who reported that volatility shocks were persistent and had a long-term effect during the crisis.

Table 8.
Multifactor Analysis Before Structural Breaks (Period: from January 2006 to March 2009)

Global Liquidity Regional Liquidity Global Volatility Regional Volatility Liquidity_i,t–1 R²

Japan 0.147*** −0.013 −0.060* −0.017 0.601*** 0.79

Singapore 0.182* 1.353* 0.117 0.096 0.317* 0.52

Thailand −0.05 0.633 −0.357* 0.106 0.425* 0.56

Korea −0.021 0.450* −0.104* −0.023 0.365* 0.70

Malaysia 0.007 1.478* 0.142 0.083 0.364* 0.41

India −0.099* 0.385* −0.111 −0.031 0.505*** 0.70

China −0.244* 0.542* 0.107 −0.075 0.684* 0.57

Philippines 0.456* 1.676* −0.034 0.185 0.129* 0.44

Taiwan 0.014 0.271* −0.136* 0.049 0.603* 0.77

	Global Liquidity	Regional Liquidity	Global Volatility	Regional Volatility	Liquidity_i,t–1	R²
Japan	0.147***	−0.013	−0.060*	−0.017	0.601***	0.79
Singapore	0.182*	1.353***	0.117	0.096	0.317***	0.52
Thailand	−0.05	0.633**	−0.357***	0.106	0.425***	0.56
Korea	−0.021	0.450***	−0.104***	−0.023	0.365***	0.70
Malaysia	0.007	1.478***	0.142	0.083	0.364***	0.41
India	−0.099*	0.385***	−0.111**	−0.031	0.505***	0.70
China	−0.244*	0.542***	0.107	−0.075	0.684***	0.57
Philippines	0.456***	1.676***	−0.034	0.185	0.129***	0.44
Taiwan	0.014	0.271***	−0.136***	0.049	0.603***	0.77

Source: The authors.

Note: This table reports the parameter estimates of Equation (5) from model specification before the structural breaks in market liquidity. Daily Amivest measure is averaged for a week and is used as proxy of liquidity which represents dependent variable in the model. Liquidity (Amivest) of each stock is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then value weighted average of all the stocks in the given index is considered as liquidity and volatility at the market level for each day which is then averaged for a week. Volatility of each stock in the index is calculated as v_t = ln(P_t^H/P_t^L × 100) where P_t^H and P_t^L are high and low prices of the week, respectively.

Global factor is the first principal component of the USA, the UK, Australia, Germany and France, whereas regional factors extracted for Japan include all Asian markets considered in the study excluding Japan. Similarly, liquidity factor of each country presented in the table represents all other regional countries excluding the respective country. Liquidity_i,t–1 is first lag of Country_i liquidity representing domestic market influence. It is marked as *, ** and *** which represent p values <0.10, <0.05 and <0.01, respectively.

Further, in Asia, the regional liquidity linkages remained higher, as compared to those of global liquidity. The significant negative relationship of India and China with the global liquidity factor reveals the direction of investment flows from developed to emerging countries. Spread being a cost-based liquidity measure exhibited different results during the crisis period. It is notable that very few countries exhibited significant cross-country linkages in the analysis using inverse quoted spread as a liquidity proxy.

Table 9 reports the multifactor model results from April 2009 to December 2016. This period exhibits the after effects of the crisis and the recovery phase across the markets. The impact of global volatility, which was noted earlier during the crisis period, faded away. On the contrary, it was noted that global liquidity had a significant positive impact on the emerging Asian markets. Out of the seven emerging countries analysed, five countries were found to have a positive relationship with global liquidity. Global volatility did not exhibit any influence on the domestic market liquidity. Compared to both global and regional volatility, it was the global and regional liquidity that had a higher influence on the market liquidity in Asia. However, regional liquidity still had a higher influence as compared to global liquidity. Regional volatility had influenced the four emerging markets, namely, Thailand, Korea, India and Philippines. Surprisingly, regional volatility had a positive impact on the liquidity of Japan. This is consistence with the findings of Jianxin Wang (2013), who stated that in Asian developed countries, there is a positive relationship between domestic liquidity and regional volatility. The results of quoted spread were similar to that of Amivest for this period.

Table 9.

Multifactor Analysis After Structural Break (from April 2009 to December 2016)

	Global Liquidity	Regional Liquidity	Global Volatility	Regional Volatility	Liquidity_i,t–1	R²
Japan	0.154***	0.108***	−0.042	0.077**	0.655***	0.85
Singapore	0.144***	0.182***	0.002	−0.039	0.463***	0.69
Thailand	0.024	0.307***	0.057	−0.146*	0.733***	0.84
Korea	0.122***	0.062**	−0.079	−0.044***	0.301***	0.61
Malaysia	0.153**	0.09	−0.073	−0.062	0.411***	0.48
India	−0.007	0.189***	0.049	−0.150***	0.574***	0.62
China	0.066*	0.077*	−0.019	0.024	0.645***	0.64
Philippines	−0.314**	0.696***	−0.134	−0.278*	0.343***	0.37
Taiwan	0.046*	0.046	−0.068**	0.033	0.714***	0.74

Source: The authors.

Note: This table reports the parameter estimates of Equation (5) from model specification after the structural breaks in market liquidity. The daily Amivest measure is averaged for a week and is used as proxy of liquidity which represents dependent variable in the model. Liquidity (Amivest) of each stock is calculated as ln ${(\frac{| R_{i t} |}{V o l u m e} \times 10^{6})}^{- 1}$ , where R_it is the absolute return of stock i on day t and volume is the dollar trading volume of the stock on day t. Then value weighted average of all the stocks in the given index is considered as liquidity and volatility at the market level for each day which is then averaged for a week. Volatility of each stock in the index is calculated as v_t = ln(P_t^H/P_t^L × 100), where P_t^H and P_t^L are high and low prices of the week, respectively.

Global factor is the first principal component of the USA, the UK, Australia, Germany and France, whereas regional factors extracted for Japan include all Asian markets considered in the study excluding Japan. Similarly, liquidity factor of each country presented in the table represents all other regional countries excluding the respective country. Liquidity_i,t–1 is first lag of Country_i liquidity representing domestic market influence. It is marked as *, ** and *** which represent p values <0.10, <0.05 and <0.01, respectively.

5. Conclusion

This study explored the differential effects of global and regional volatility and liquidity on the liquidity of each Asian stock market. We examined the time-series relation between domestic liquidity and external factors and have provided evidence regarding the dynamic relationships. While earlier studies had focused exclusively either on volatility or on liquidity variables, we have examined the individual and joint effects of both the variables on the liquidity of emerging Asian equity markets. Our results indicate the relative importance of the regional versus global factors.

Regional factors have a higher significance for emerging Asian markets, as compared to global factors. However, the impact differs across the markets. This higher sensitivity to the regional factors infers a high level of integration in the region.

Further, liquidity has a higher influence than volatility, indicating that market liquidity also acts as the channel of regional integration in Asia.

During the recent Global Financial Crisis, the global volatility factors had a higher influence on the liquidity of all Asian equity markets.

This study shed additional light on the cause of liquidity co-movements by analysing the relation between market volatility and liquidity. The results of this study would alert the policymakers and regulators to check the rising liquidity commonality across the globe and iterate early warning signals to monitor the liquidity levels in the market.

Our results will help global investors measure their exposure to the liquidity risk factor in each market. Investors generally prefer investing in markets with higher liquidity. Among the emerging markets, China, Korea, Taiwan and India (Table 2) exhibited higher liquidity levels and evolved as promising investment destinations.

Our results can help in diversifying liquidity risks by constructing global portfolios based on the liquidity commonality. Markets that are less vulnerable to global shocks offer higher diversification benefits to the investors. Our findings indicate that liquidity levels in India, China and Taiwan are more influenced by their own domestic factors and are relatively less influenced by global factors. These countries offer higher diversification benefits to cross-border investors.

Equity markets are significant sources of corporate funding in emerging economies. Hence, the nexus between market liquidity and volatility will be of greater interest to all the firms. Markets having a higher external influence face a higher possibility of liquidity deterioration during market distress which would affect both their asset value and their economic activity. The interactions of global volatility and liquidity on individual market liquidity helps forecast the liquidity levels in that market and predicts the crisis more accurately.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendix

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Global Financial Crisis: Dynamics of Liquidity Risk in Emerging Asia

Abstract

Keywords

1. Introduction

2. Literature Review

3. Methodology

3.1 Data Description

Table 1. Market and Indices Country Index The UK FTSE AIM UK Australia ASX 50 France CAC Germany DAX The USA DJIA Singapore Straits Times Index Japan TOPIX Malaysia FTSE Bursa Malaysia KLCI South Korea KOSPI India NIFTY Philippines PCOMP Thailand SET 50 China SSE Taiwan TWSE

3.1.2 Volatility Measure

3.2 Data Summary

4. Empirical Results

4.1 Impact of Global and Regional Factors

Footnotes

Declaration of Conflicting Interests

Funding

Appendix

References

Table 1.
Market and Indices

Country Index

The UK FTSE AIM UK

Australia ASX 50

France CAC

Germany DAX

The USA DJIA

Singapore Straits Times Index

Japan TOPIX

Malaysia FTSE Bursa Malaysia KLCI

South Korea KOSPI

India NIFTY

Philippines PCOMP

Thailand SET 50

China SSE

Taiwan TWSE