Friday, June 5, 2009

Using Markov regime-switching analysis, it shows that the Lehman Brothers failure was a watershed event in the crisis

TO BE NOTED: From RGE Monitor:

Financial crisis, global conditions, and regime changes
This column examines the use of key global market conditions to assess financial volatility and the likelihood of crisis. Using Markov regime-switching analysis, it shows that the Lehman Brothers failure was a watershed event in the crisis, although signs of heightened systemic risk could be detected as early as February 2007.

The international community has called for the IMF to deepen its work on systemic risks and early warning signals. While there are currently many different research strands, this short column empirically examines the role that global market conditions play in detecting systemic risk. We adopt regime-switching models using variables that proxy for global market conditions – Chicago Board Options Exchange Volatility Index (VIX), TED spread (the difference between LIBOR and Treasuries), and US dollar-euro foreign exchange swap rate. For instance, the Lehman Brothers collapse on 15 September 2008 was a watershed event that rapidly spilled over to emerging market countries, sharply increasing uncertainty across asset markets, a scramble for US dollars with the breakdown of the carry trade, and the need for financial institutions to refinance their US dollar positions. The regime-switching models indicate a move towards a high volatility state before the Lehman episode, which are consistent with elevated systemic risks in the financial system. We first look qualitatively at the behaviour of some global market variables during the financial crisis before presenting the formal findings of the regime-switching models.

Global market conditions and systemic risk: A qualitative view

With interbank markets across advanced economies becoming clogged in early August 2007, there was clear evidence of a flight to quality by investors. For example, the gold spot price, which is often used as a crude measure of storage of value, started its continuous increase in early August 2007 from $660 per ounce and reached its peak of $1002 around the Bear Stearns rescue by JP Morgan and the Fed’s announcement of the Primary Credit Dealer Facility on 16 March 2008, after which time the gold spot price dropped 10% in a short time.1 In addition, there was a strong demand for 10-year US Treasury notes as a “safe haven,” and yields almost halved between the onset of the crisis in August 2007 and the Bear Stearns and Lehman episodes. The bid-ask spread deviated frequently from its usual pattern.

The flight to quality was also accompanied by a flight to liquidity. With liquidity evaporating in many asset-backed securities, liquidity spirals occurred with both market and funding liquidity being significantly impaired (IMF, 2008; Frank, González- Hermosillo and Hesse, 2008). While the LIBOR-OIS spread, a proxy for funding liquidity and general stress in the interbank markets, has been subject to various spikes such as at the onset of the crisis and year-end effects in December 2007, the Lehman collapse exposed the interbank market to heightened counterparty and liquidity risk concerns, with market participants across the world withdrawing from interbank lending. Many central banks had to inject liquidity and, in effect, substituted for the interbank market. There was a shortage of high quality collateral for posting with the central bank with haircuts increasing in both Treasury securities and risky assets.

Volatility in various asset classes was also affected, mirroring the spikes of the LIBOR-OIS spreads. For instance, a structural break in the VIX since the Lehman collapse is apparent, and other implied volatility indices reveal similar patterns. Volatility also spilled over into the foreign currency markets with the carry trade starting to rapidly unwind at the end of September 2008. The implied volatilities of major emerging market currencies (based on option prices) reflected this breakdown in the carry trade. High-yielding and previous investment currencies saw large depreciations against the US dollar, while funding currencies such as the Japanese yen benefited from a repatriation of funds. There was a scramble for US dollars, which was reflected in the higher volatility of the euro-US dollar swap rates. Relatedly, the assumption of covered interest rate parity has been violated during the crisis.2 The daily deviations from the covered parity jumped at the time of the Bear Stearns rescue, and then completely broke down for various emerging market currencies after Lehman’s bankruptcy.

Overall, emerging markets were less affected during the initial stages of the subprime crisis than countries in the epicentre; for example, emerging market equity markets peaked in November 2007. But the persistence of the subprime crisis, the deterioration of economic fundamentals in advanced economies, and the rise of global risk aversion hit emerging markets with full force in late 2008 after the Lehman collapse. While emerging market corporate spreads (over Treasuries) gradually began to increase following the onset of the subprime crisis, they escalated sharply across regions after the Lehman bankruptcy. Having presented this qualitative analysis as a useful starting point for examining the role of key global market variables in systemic risks, we now turn to a more formal systematic analysis.

Markov regime-switching analysis

We use Markov regime-switching techniques to examine financial stress in a formal way. Given the intrinsic volatility of high-frequency financial data, especially during periods of stress, we chose the ARCH Markov-Switching model (SWARCH) by Hamilton and Susmel (1994) because it can differentiate between different volatility states.3

A SWARCH model of the euro-US dollar forex swap reveals that this variable moved from a low- to a medium-volatility regime in the beginning of August 2007, before entering the high-volatility state right after the Lehman collapse in September 2008 and remaining there until the end of November 2008 (Figure 1). Many non-US banks, especially European ones, faced a shortage of US dollar funding for their conduits and special investment vehicles beginning in the summer of 2007. As the interbank market for dollar funding became squeezed due to counterparty and liquidity risks, these banks increasingly engaged in foreign currency (FX) and cross-currency swap arrangements (see Baba et al, 2008). In particular, both the euro and sterling were used as the funding currencies for the dollar FX swaps. The spillovers from the interbank market to the FX swap market led FX swap prices to temporarily deviate from covered interest parity. With the turbulence becoming more persistent, many non-US financial institutions also increasingly engaged in longer-term cross-currency swaps. This episode especially highlighted the international interconnectedness of banks’ funding requirements through FX swap markets.


As shown in Figure 1, the move of the forex swap into the high volatility state on 15 September 2008 coincides with the sharp increase in counterparty risk resulting from Lehman’s failure and a sizeable dollar shortage that occurred with margins and haircuts increasing on most dollar-denominated assets.

After the Lehman episode, the VIX increased to historical highs, and it is of interest to put the S&P 500 stock market volatility during the current financial crisis into a historical perspective. Figure 2 shows a daily SWARCH model of the VIX from 1998 to the end of 2008. The model has the highest probability of being in the high volatility state during the Russian Crisis and LTCM default in 1998, the period surrounding the WorldCom scandal and Brazil’s election in 2002, as well as the beginning of the subprime crisis in the fall of 2007 and the period following the Lehman collapse. The model also enters the high volatility state briefly at the time of the Shanghai stock market crash and the first abrupt ABX (BBB) price decline of investment grade subprime mortgage-backed securities in late February 2007. During the Bear Stearns rescue, the VIX was more likely to be in the high rather than medium-volatility state. The Lehman failure then triggered a very fast movement of the VIX into the high-volatility regime, where it remained until the sample period ended on 31 December 2008. After the start of the subprime crisis, the VIX oscillated exclusively between the medium- and high-volatility regimes, in contrast to the predominantly low-volatility regime during 2003-2007.


A similar SWARCH model is estimated for the 3-month TED spread (the difference between LIBOR and Treasuries). Figure 3 suggests that this indicator of short-term bank credit risk moved decidedly into a high-volatility regime in the beginning of August 2007 and remained in it until the Bear Stearns’ rescue. The Lehman collapse again triggered a high-volatility regime. As in the VIX model, the SWARCH framework for the TED spread picks up the Russia and LTCM default in 1998 as well as the September 11 shock. The findings also imply a regime change around the sharp Shanghai stock market correction and the first round of ABX (BBB) price declines in late February 2007, which could have been seen as a potential warning signal about the impending fragilities in the global financial system.


This article presents a Markov regime-switching technique to examine when key global market conditions variables such as the VIX, forex swap or the TED spread moved into a high-volatility regime. The findings support the view that the Lehman failure was a key watershed event in the crisis, but periods of high volatility were also present before Lehman’s failure. In particular, based on the VIX SWARCH model, these earlier episodes of distress include the Shanghai stock exchange crash and the ABX (BBB) price decline in February 2007, the beginning of the subprime crisis in August 2007, and the Bear Stearns rescue in March 2008. The results suggest that the bankruptcy of Lehman Brothers aggravated what appeared to be already a crisis, characterised by persistent (albeit at times noisy) signs of a high-volatility state. High-volatility states can be viewed as a potential manifestation of systemic risk.

Note: The views expressed in this article are those of the authors and should not be attributed to the IMF, its Executive Board, or its management. Any errors and omissions are the sole responsibility of the authors.


Baba, Naohiko, Frank Packer, and Teppei Nagano, 2008, “The spillover of money market turbulence to FX swap and cross-currency swap markets,” BIS Quarterly Review, pp.73-86 Frank, Nathaniel, Brenda González-Hermosillo, and Heiko Hesse, 2008, “Transmission of Liquidity Shocks: Evidence from the 2007 Subprime Crisis,” IMF Working Paper 08/200 (Washington: International Monetary Fund). Hamilton, James D., and Raul Susmel, 1994, “Autoregressive Conditional Heteroskedasticity and Changes in Regime,” Journal of Econometrics, Vol. 64 (September-October), pp. 307–33. International Monetary Fund, 2008, Global Financial Stability Report, “Market and Funding Illiquidity: When Private Risk Becomes Public,” Chapter 3. World Economic and Financial Surveys (Washington, April).

1 The bankruptcy of Lehman Brothers saw the price of gold soar over 20% within a few weeks, as global risk appetite dramatically deteriorated and precipitated a flight to quality across asset classes and markets. 2 Covered interest rate parity postulates that the currency forward premium equals the interest rate differentials of the home and foreign interest rate covering the same time period. A violation would indicate possible arbitrage opportunities. 3 Univariate SWARCH models are adopted with variables in first differences to account for the non-stationarity of the variables. The mean equation is an AR(1) process and the variance is time-varying with the ARCH parameters being state dependent.-
Originally published at VOX and reproduced here with the author’s permission.

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