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Thailand: Selected Issues

Author(s):
International Monetary Fund. Asia and Pacific Dept
Published Date:
June 2018
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Thailand: Financial Conditions1

Indicators of a country’s overall financial conditions are very relevant for monetary policy. This paper shows that Thai financial market conditions do not appear to impair the monetary policy transmission mechanism. Strongly influenced by global factors, financial conditions in Thailand have been favorable over recent years, but have however improved less than in ASEAN–4 neighboring countries.

A. Introduction

1. Indices of financial conditions summarize the information content of several financial variables. Financial markets nowadays comprise many different segments: sovereign and corporate bond markets, foreign exchange market, stock markets, and others. While an efficient market should provide a consistent pricing across asset classes and financial instruments, for monetary policy purposes it is useful to summarize financial conditions by means of indices aimed at capturing the situation across several markets. But even within a single market segment the number of financial instruments regularly traded is often large nowadays, providing a true data rich environment in which the selection of the indicators is to some extent inevitably arbitrary. Similarly, a direct link between the estimated financial conditions indices and monetary policy cannot be established without resorting to auxiliary models, and is beyond the scope of this paper. However, it is plausible to assume that monetary policy is one of the main factors behind developments in overall financial conditions, and therefore to interpret evidence on financial conditions in the context of the monetary policy stance.

2. Against this background, this paper looks at two different but complementary indicators. These two indicators are the Financial Stress Index (EM-FSI introduced by Balakrishnan, Danninger, Elekdag, and Tytell, 2009) and a Financial Conditions Index (FCI by Koop and Korobilis, 2014). In addition to cross checking the information of the two indices, we will exploit their properties to explore two main questions: (i) what has been the evolution of financial conditions in Thailand over recent years? and (ii) what are the main drivers behind those conditions? For both questions we will also compare Thailand’s conditions with available international evidence, which may however be different for each indicator.

B. Gauging Financial Conditions in ASEAN-4 Countries

3. The EM-FSI was designed to get quantitative evidence into the capacity of the financial system to contribute to the monetary transmission mechanism. The optimal transmission of monetary policy stimulus hinges on well functioning financial intermediation through the financial system. The degree of financial “stress” in the system, defined as a period of impaired financial intermediation, can therefore be useful information to assess the potential effect of monetary policy.

4. The EM-FSI comprises information about five key variables.2

  • The “banking-sector beta” is the standard capital asset pricing model (CAPM) beta, which captures the move in banking stocks relative to the overall stock market (values higher than one may therefore reflect episodes in which banking sector valuations become (relatively) riskier.

  • Stock market returns are computed as the year-on-year change in the stock index multiplied by minus one, so that a decline in equity prices corresponds to higher market-related stress.

  • Stock market volatility is a time-varying measure of market volatility obtained from a GARCH (1,1) specification, using autoregressive month-over-month real returns.

  • Sovereign debt spreads is defined as the bond yield minus the 10-year United States Treasury yield using JPMorgan EMBI Global spreads.

  • The EMPI captures exchange rate depreciations and declines in international reserves.

Higher values of the index denote greater stress in the financial system, and point to a potentially impaired transmission of monetary policy through the financial system. An important advantage of the EM-FSI is that it is available for most neighboring countries, which allows for not only looking at the evolution of the index in Thailand over recent years, but also comparing it to other large neighboring economies.

5. The EM-FSI suggests that overall financial conditions have remained accommodative in Thailand over recent years. However, a comparison of financial conditions with other countries suggest that despite the very low level of monetary policy rates, financial conditions in Thailand remain somewhat “tighter” than those in the ASEAN–4 neighboring countries (Figure 1, left panel).3 While market conditions improved following the last interest rate cut in May 2015, the fact that policy rates have been kept constant since then while other ASEAN–4 countries have introduced further policy stimulus—motivated by macroeconomic and especially inflation developments in 2016 and 2017—may explain the more favorable conditions in their markets.

Figure 1.Thailand: Financial Conditions

6. Recent Thai financial market conditions appear to be sufficiently good not to impair the monetary policy transmission. Since financial intermediation would be crucial for the transmission of monetary policy, the EM-FSI provides a gauge of how current conditions in Thai financial markets could transmit monetary policy. Overall, developments in the EM-FSI suggest that financial market conditions do not appear to impair the monetary policy transmission mechanism. At the same time, since the policy rate in Thailand has been on hold since May 2015 while some neighboring countries have lowered policy rates since then, it is also possible to compare conditions at the dates in which other central banks lowered interest rates by looking at all components of the index (Figure 1, right panel). That comparison suggests that conditions in the Thai financial system remain relatively better than when those other countries cut rates in recent years.

C. Main Factors Behind Financial Conditions

7. The FCI summarizes a broader set of financial sector variables. This paper provides additional robustness to the measurement of financial conditions by the calculation of an alternative index. The FCI proposed by Koop and Korobilis (2014) builds on the literature on time-varying parameter vector autoregression and dynamic factor models.4 This approach has two main advantages. First, it can control for (current) macroeconomic conditions. Second, it allows for a dynamic interaction between the FCIs and macroeconomic conditions, which can also evolve over time. Importantly, the FCI allows for widening the financial sector coverage by combining 19 macroeconomic and financial indicators that help assess the influence of price of risk (e.g., term spread), leverage (e.g., credit to GDP), external factors (e.g., VIX) and macroeconomic conditions (e.g., GDP growth) to the overall financial conditions (Table 1). In that sense, the FCI can be interpreted as an extension of the FSI, which mainly uses “price of risk” indicators.

Table 1.Thailand: Variables Used in the Calculation of the FCI
VariablesDescriptionSource
Domestic-Level Variables
Term SpreadsYield on 10-year government bonds minus yield on three-month treasury bills.Bloomberg LP.; and IMF staff.
Interbank SpreadsInterbank interest rate minus yield on three-month treasury bills.Bloomberg LP.; and IMF staff.
Change in Long Term Real Interest RatePercentage point change in the 10-year government bond yield, adjusted for inflation.Bloomberg LP.; and IMF staff.
Corporate SpreadsCorporate yield of the country minus yield of the benchmark country. JPMorgan CEMBI Broad is used for emerging market economies where available.Bloomberg LP.; and Thomson Reuters Datastream.
Equity Returns (local currency)Log difference of the equity indices.Bloomberg LP.
House Price ReturnsLog difference of the house price index.Bank for International Settlements; Haver Analytics; and IMF staff.
Equity Return VolatilityExponential weighted moving average of equity price returns.Bloomberg LP.; and IMF staff.
Change in Financial Sector ShareLog difference of the market capitalization of the financial sector to total market capitalization.Bloomberg LP.
Credit GrowthPercent change in the depository corporations claims on private sector.Bank for International Settlements; Haver Analytics; and IMF, International Financial Statistics database.
Sovereign SpreadsYield on 10-year government bonds minus the benchmark country’s staff yield on 10-year government bonds.Bloomberg LP.; and IMF staff.
Banking Sector VulnerabilityExpected default frequency of the banking sector.Moody’s Analytics, CreditEdge; and IMF staff.
Exchange Rate MovementsChange in U.S. dollar per national currency exchange rate.IMF, Global Data Sources; IMF, International Financial Statistics; and Bloomberg LP.
Domestic Commodity Price Inflation followingA country-specific commodity export price index constructed prices and country-Bloomberg LP.; United Nations, COMTRADE
Gruss (2014), which combines internationallevel data on exports and imports for individualdatabase; IMF, Global Data source; and IMF staff.
commoditycommodities. Change in the estimated country-specific commodity export price index is used.
Trading Volume (equities)Equity markets’ trading volume, calculated as level to 12-month moving average.Bloomberg LP.
Market Capitalization (equities)Market capitalization of the equity markets, calculated as level to 12-Datastream month moving average.Bloomberg LP.
Market Capitalization (bonds)Bonds outstanding, calculated as level to 12-month moving average.Dealogic; and IMF staff.
Change in Credit-to-GDPChange in credit provided by domestic banks, all other sectors of the economy, and nonresidents in percent of GDP.Bank for International Settlements; Haver Analytics; and IMF staff.
Real GDP GrowthPercent change in the GDP at constant prices.IMF, World Economic Outlook database.
Industrial Production GrowthPercent change in the industrial production index.Haver Analytics; and IMF, Global Data Source Statistics database.
Global-Level Variables
VDChicago Board Options Exchange Market Volatility Index.Bloomberg LP.; Haver analytics
MOVEMerrill lynch Option Volatility Estimate Index.Bloomberg LP.
Source: IMF staff calculations.Note: BIS = Bank for International Settlements; CEMBI = Corporate Emerging Markets Bond Index; MOVE = Merrill Lynch Option Volatility Estimate Index; VIX = Chicago Board Options Exchange Volatility Index.
Source: IMF staff calculations.Note: BIS = Bank for International Settlements; CEMBI = Corporate Emerging Markets Bond Index; MOVE = Merrill Lynch Option Volatility Estimate Index; VIX = Chicago Board Options Exchange Volatility Index.

8. The estimation of FCI corroborates the main insights from the EM-FSI. In particular, the time series evolution of the index corroborates the favorable financial conditions experienced in Thailand (and several other emerging economies) over recent years.

9. The improvement in Thai financial conditions over recent years appears to reflect the very favorable conditions in global financial markets. The estimated factor loadings provide valuable information on the main drivers of financial conditions. In the absence of further monetary policy stimulus, Thai financial conditions over recent years have instead benefitted from the significant decline of market volatility in global markets, both in the bond and the stock markets. Despite the recent and gradual normalization of monetary policy in the United States, the significant accommodative monetary policy stance worldwide is likely to be the main factor behind those favorable global financial conditions. In addition, the significant development of capital markets in emerging economies and the limited spreads on their bond markets (relative to the United States) are the other main factors behind the favorable financial conditions. Available evidence suggests that this has been indeed the situation for other emerging economies like Brazil, China, and Indonesia as well, despite their very different macro-financial challenges compared to Thailand.5 Furthermore, as most other emerging (and developed) economies also benefit from those conditions, these findings for the FCI are fully consistent with the interpretation of differences in the EM-FSI across ASEAN–4 countries as most likely reflecting differences in domestic monetary policy stance over recent years.

Figure 2.Thailand: Main Factors Behind Financial Conditions

Sources: BIS; Haver Analytics; Merrill Lynch; CBOE; and IMF staff estimates.

D. Conclusion

10. Overall evidence suggests that financial conditions in Thailand have been favorable over recent years, but remain vulnerable to a tightening of conditions in global financial markets. The monetary policy transmission may have weakened somewhat in recent years, but there is no evidence of financial conditions impairing the monetary transmission mechanism. Importantly, evidence suggests that domestic monetary policy stance could have been more supportive for financial conditions over recent years. Global financial conditions were also found to have been a major contributor to the favorable financial conditions in Thailand. Looking ahead, a reversal of those conditions or their likely tightening over the coming years—not only as interest rates adjust to the ongoing normalization of U.S. monetary policy but also in terms of market volatility and risk pricing—may require counterbalancing domestic measures if an accommodative monetary policy stance is to be maintained for some time.

References

Prepared by Juan Angel Garcia.

The choice of variables is to a large extent motivated to guarantee the construction of the index across a large number of countries, while at the same time combining information over several key market segments. For further details on the rationale behind the variables chosen as part of the index and sub-index definitions see Balakrishnan, et al (2009).

The FSI methodology does not allow for statistical evidence on differences across countries’ indices, but evidence across countries and time suggests that quantitative differences in the FSI as the ones shown in Figure 1 can be attributed to specific macro-financial events.

Our empirical approach follows the application of Koop and Korobilis (2014) methodology as described in detail in Chapter 3 of the IMF GFSR (2017), October.

The analysis also reveals that other variables may be of particular importance for specific countries, such as bank vulnerability in the case of Thailand.

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