Global Shock and Regional Spillovers

Iwan J. Azis; Sabyasachi Mitra; Anthony Baluga

doi:10.1515/peps-2013-0014

Article

Global Shock and Regional Spillovers

Iwan J. Azis , Sabyasachi Mitra and Anthony Baluga

Published/Copyright: July 27, 2013

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From the journal Peace Economics, Peace Science and Public Policy Volume 19 Issue 2

Abstract

When global crisis struck at a time of great global and regional interdependence, contagion occurs; it can work via capital flows or through spillovers of the returns/yields on financial assets. The analysis in the paper deals with the latter. Focusing on the shocks in the United States and Eurozone bonds market, and using multivariate GARCH models with conditional variance-covariance matrix being positive definite, it is shown that the shock and volatility spillovers in some emerging Asian countries are quite significant. They spread throughout different asset classes, threatening the region’s financial stability, and making it more difficult for the policy response to focus on a particular market. Although local bonds volatilities are more determined by their own respective shocks and volatilities, in some markets the direct shock and volatility spillovers remain significant; so does the indirect spillovers within domestic asset markets and across economies. Absent of policy coordination within and across countries. Such undesirable spillovers due to other country’s unilateral policy can be damaging. Growing financial nationalism in the midst of a crisis is likely to spark strong reactions from affected countries, potentially creating a conflict situation.

Keywords: spillovers; contagion; sovereign bond yields and returns; conditional volatility; emerging East Asia local currency bond markets; financial crisis; JEL classification: G12, C14, E43, E62, H62, H63

Corresponding author: Iwan J. Azis, Asian Development Bank (ADB), Cornell University, 7 Lowell Place, Ithaca, NY 14850, USA

¹
With liquidity in local markets higher in the belly of the curve – usually around the 3–7 year bracket – 5-year bonds for Asian debt are used, except for the Japanese case where we use 10-year bond yields.
²
Rising international prices of food and other commodities, including oil, aggravated the inflation pressure during that period.
³
Due to evaporation of global liquidity, foreign currency borrowing conditions in Korea were severely worsened. In response, the Bank of Korea (BOK) used its foreign reserves and proceeds of its currency swaps with the US Federal Reserve to supply some US$26.6 billion liquidity through Competitive Auction Swap Facility and Competitive Auction Loan Facility. BOK also established a US$30 billion swap arrangement with the US Federal Reserve on October 30, 2008. As the pressures continued, the BOK subsequently entered into a 180 billion yuan/38 trillion won swap arrangement with China’s central bank (PBC), and expanded the arrangement with Bank of Japan (BOJ) from US$3 billion to US$20 billion. Yet, the “power” of financial market spillovers remained unmatched, as clearly shown by the trends of currency swap rates and interest rate swap and the rapid widening of credit spread on corporate and bank bonds.
⁴
Yields of government bonds in Indonesia, Malaysia, and Thailand began to edge up in July and August 2012 on renewed uncertainty – despite the continued decline in US and German bond yields.
⁵
Conditional variances are used instead of unconditional variances to address the issue of volatility clustering and leverage effect that are commonly observed in high frequency financial data.
⁶
The GARCH modeling framework has also been applied to analyze the volatility spillovers in a single country; see, e.g., Conrad, Kaul, and Nimalendran (1991) and Kroner and Ng (1998) for the US equity market, and Chelley-Steeley and Steeley (1996) for the UK equity market. While past shocks to the volatility of large firm portfolios appeared to influence the volatility of small firm portfolios, the reverse was not found to be the case. Alli, Thapa, and Yung (1994) applied the same technique to examine volatility spillovers between different sectors of the US oil industry.
⁷
Lütkepohl (1993) argued that over-fitting (selecting a higher order lag length than the true lag length) causes an increase in the mean-square forecast errors of the VAR, and under-fitting the lag length often generates auto-correlated errors.
⁸
Most financial time series volatility clustering characteristics are aptly modeled by a GARCH(1,1) process (i.e., p=q=1). This implies that conditional variances and covariances depend on one period lag values of all the conditional variances and covariances across bond market returns, as well as one period lag squared errors and cross-products of error terms. Setting K=1 allows mathematical tractability of the model.
⁹
Unlike in the preceding section, however, here the volatility clusters that tend to appear during a crisis are taken into account (reflected in the larger coefficient).
¹⁰
See Appendix for the Tables A1 and A2 of significant shock and volatility spillover coefficients.
¹¹
To further investigate claims of tightening in the US$-funding market during the height of the global financial crisis in 2008/2009, we have used the MV GARCH model to observe spillovers between US$ SIBOR and local money markets. Results show significant shock and volatility spillovers between the two funding markets, implying that instability is transmitted across onshore and offshore money markets. The region’s FX markets also showed significant shock effects on bond markets, particularly when the source of shock and volatility spillovers was US Treasuries.
¹²
Many studies (Xie 2012, Ito 1999) have cited the yen’s devaluation from 1995 to 1997 – in part due to correction of the excess rise in the previous years and also in line with weak domestic fundamentals – as one of the factors triggering the 1997/1998 Asian financial crisis.

Appendix – Indirect spillovers

Table A1

Shock spillovers and persistence (significant at the 5% level).

BM=local bond market, CHN=People’s Rep. of China, EQ=domestic equity market, FX=domestic currency market, IDN=Indonesia, IND=India, JAP=Japan, KOR=Rep. of Korea, MM=domestic money market, MAS=Malaysia, PHI=Philippines, THA=Thailand.

Source: Authors’ calculations.

Table A2

Volatility spillovers and persistence (significant at the 5% level).

Source: Authors’ calculations.

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Published Online: 2013-07-27

Published in Print: 2013-08-01

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Articles in the same Issue

https://doi.org/10.1515/peps-2013-0014

Keywords for this article

spillovers; contagion; sovereign bond yields and returns; conditional volatility; emerging East Asia local currency bond markets; financial crisis; JEL classification: G12, C14, E43, E62, H62, H63