The Financial Stress Spillover: Evidence from Selected Asian Countries

Shah, Zulfiqar Ali; Majeed, Muhammad Ejaz; Simonetti, Biagio; Crocetta, Corrado

doi:10.1007/978-3-030-05420-5_11

Zulfiqar Ali Shah²,
Muhammad Ejaz Majeed²,
Biagio Simonetti³ &
…
Corrado Crocetta⁴

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 274))

Included in the following conference series:

Convegno della Società Italiana di Statistica

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Abstract

The objective of the study is to analyze financial stress spillover among selected Asian countries, namely, China, Pakistan, Sri Lanka, Malaysia and India for the period from Jan 2001 to Dec 2009. The financial stress is measured by Financial Stress Index (FSI), a specially designed comprehensive measure of financial stress. The methodology of Yimlam 2012 is adopted for analyzing dynamics of variance decomposition among countries using FSI for the selected countries. The results of the study confirm that China and Pakistan are the largest transmitters of spillover towards other selected countries. Also the net spillover of China and Pakistan indicated to be positive whereas all other countries show up negative net spillovers. The economic and geographic linkages are suggested to be responsible for influencing magnitude of spillover among selected countries. Finally, the response of each country to shocks in other countries is found to be positive.

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Authors and Affiliations

International Islamic University, Islamabad, Pakistan
Zulfiqar Ali Shah & Muhammad Ejaz Majeed
University of Sannio, Sannio, Italy
Biagio Simonetti
University of Foggia, Foggia, Italy
Corrado Crocetta

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Zulfiqar Ali Shah
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Muhammad Ejaz Majeed
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Biagio Simonetti
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Corrado Crocetta
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Correspondence to Biagio Simonetti .

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Department of Economics, University of Foggia, Foggia, Italy
Corrado Crocetta

Appendices

Appendix A: VAR Analysis

	China	India	Malaysia	Pakistan	Sri Lanka
CHINA(−1)	0.302823	0.052225	0.121179	0.102787	0.00603
	−0.13367	−0.12939	−0.13788	−0.11002	−0.1188
	[ 2.26550]	[ 0.40363]	[ 0.87890]	[ 0.93422]	[ 0.05076]
India(−1)	0.036415	0.227214	−0.150422	−0.049573	0.191282
	−0.14968	−0.14489	−0.1544	−0.12321	−0.13304
	[ 0.24328]	[ 1.56818]	[−0.97427]	[−0.40236]	[1.43780]
Malaysia(−1)	0.042012	0.149417	0.486591	0.032349	−0.028551
	−0.10854	−0.10507	−0.11196	−0.08934	−0.09647
	[ 0.38705]	[ 1.42208]	[ 4.34605]	[ 0.36206]	[−0.29595]
Pakistan(−1)	0.290666	0.375003	0.250893	0.779128	0.011282
	−0.0985	−0.09535	−0.1016	−0.08108	−0.08755
	[2.95081]	[3.93291]	[2.46930]	[ 9.60934]	[ 0.12886]
Sri Lanka(−1)	0.007378	0.030333	0.086871	0.001282	0.456337
	−0.10428	−0.10094	−0.10756	−0.08583	−0.09268
	[ 0.07075]	[0.30051]	[0.80765]	[ 0.01494]	[4.92372]
C	0.024338	0.033375	−0.03342	−0.00558	−0.031657
	−0.21202	−0.20523	−0.21869	−0.17452	−0.18844
	[ 0.11479]	[0.16262]	[−0.15282]	[−0.03197]	[−0.16799]
R-squared	0.392873	0.520204	0.441596	0.671718	0.363847
Adj. R-squared	0.362817	0.496451	0.413953	0.655467	0.332355
Sum sq. resids	485.4769	454.8872	516.5255	328.9219	383.5076
S.E. equation	2.192419	2.122224	2.261441	1.80462	1.948616
F-statistic	13.07147	21.9012	15.97456	41.33251	11.55339
Log likelihood	−232.7346	−229.2527	−236.0513	−211.9065	−220.1208
Mean dependent	0.013182	0.018067	−0.031475	−0.02356	−0.026511
S.D. dependent	2.746576	2.990684	2.954057	3.074469	2.384808

Appendix B: VAR Lag Order Selection Criteria

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−1096.44	NA	1648.249	21.59685	21.72553	21.64896
1	−1003.83	174.3199*	438.1272*	20.27121*	21.04327*	20.58384*
2	−986.398	31.10711	509.9942	20.41957	21.835	20.99273
3	−971.511	25.10486	627.3644	20.61785	22.67666	21.45153
4	−954.259	27.40018	742.6635	20.76978	23.47195	21.86398
5	−933.901	30.33715	836.2091	20.8608	24.20635	22.21553
6	−913.877	27.87567	961.145	20.95838	24.94731	22.57363
* Indicates lag order selected by the criterion

Appendix C: Stability Test: AR Roots of Characteristic Polynomial

Root	Modulus
0.828249	0.828249
0.515858	0.515858
0.301387 − 0.092044i	0.315129
0.301387 + 0.092044i	0.315129
0.305214	0.305214
No root lies outside the unit circle. VAR satisfies the stability condition

Appendix D: Generalized Impulse Response Analysis

Appendix E

Construction of Index

The Financial Stress Index (FSI) comprises five variables, which are aggregated into an overall index to capture credit conditions in three financial market segments (banking, securities markets, and exchange markets). These five components all help associate the degree of financial stress with large swings in asset prices, abrupt changes regarding uncertainty and the appetite for risk, (international) liquidity conditions, credit availability and/or financial intermediation. The five components of the FSI are presented in table below:

The choice of sub-indices was limited by data considerations and a preference for parsimony.

To obtain the aggregate Financial Stress Index for each country the five components are standardized and summed up:

FSI = β + Stock market returns + Stock market volatility + Sovereign debt spreads + EMPI

	Category	Variable	Measurement
1	Banking sector	Banking beta	CAPM: banking sector equity index
2	Security market	Stock return
		Stock volatility	GARCH (1, 1)
		Sovereign debt spread	Bond Yield – 10y US T-Yield. Using JP Morgan EMBI Global Spread. o/w 5 year Credit Default Swap Spread
3	Exchange markets	Exchange market pressure (EMPI)	[%age change Exchange Rate] – [%age Change of Total Reserve-Gold]

Further details on the definition of the five components (before standardization) and the aggregation method are given below:

1.
Banking Sector:

The Banking-sector beta is the standard capital asset pricing model (CAPM) beta, and is defined as follows:

$$ \beta_{i,t} = \frac{{COV\left( {r_{i,t}^{M} ,r_{i,t}^{M} } \right)}}{{\sigma_{i,M}^{2} }}, $$

where:

r^M::: The market returns
r^B::: The banking returns

The beta greater than one shows that banking stocks move more than proportionately with the overall stock market—suggests that the banking sector is relatively risky, and would be associated with a higher likelihood of a banking crisis.

2.
Stock Market

Stock Market Returns are the percentage change in the stock index. A decrease in equity prices corresponds to increased securities-market-related stress.

Stock market volatility is a time-varying measure of market volatility obtained from a GARCH(1, 1) specification, using month-over-month real returns and modeled as an autoregressive process with 12 lags.

Sovereign debt spreads is defined as the bond yield minus the 10-year United States Treasury yield using JPMorgan EMBI Global spreads. When EMBI data were not available, five-year credit default swap spreads were used.

3.
Foreign Exchange Market

The EMPI captures exchange rate depreciations and declines in international reserves, and is defined for country i in month t as:

$$ EMPI_{i,t} = \frac{{\left( {\Delta e_{i,t} - \mu_{i,\Delta e} } \right)}}{{\sigma_{i,\Delta e} }} - \frac{{\left( {\Delta RES_{i,t} - \mu_{i,\Delta RES} } \right)}}{{\sigma_{i,\Delta RES} }}, $$

where:

Δe::: percentage change in exchange rate
ΔRES::: percentage change in total reserves minus gold
μ::: the mean
σ::: the standard deviation

All variables are in monthly or daily frequency. The index is constructed by taking the average of the components after adjusting for the sample mean and standardizing by the sample standard deviation. Then it is converted into quarterly frequency by taking the average of the monthly data.

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Shah, Z.A., Majeed, M.E., Simonetti, B., Crocetta, C. (2019). The Financial Stress Spillover: Evidence from Selected Asian Countries. In: Crocetta, C. (eds) Theoretical and Applied Statistics. SIS 2015. Springer Proceedings in Mathematics & Statistics, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-030-05420-5_11

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DOI: https://doi.org/10.1007/978-3-030-05420-5_11
Published: 13 February 2019
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05419-9
Online ISBN: 978-3-030-05420-5
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