This chapter provided an application of analysing the financial fragility in the U.S. equity market. We empirically showed the correlation concentration level can be used as an early
warning indicator of the financial fragility, even for systemic risk, through analysing the absorption ratio by the quantile regression and the change point analysis (also supported by existed literature Kirtzman et al. (2011) and Billio et al. (2012)). Based on predicted conditional covariance, we computed the predicted absorption ratio, to indicate further im- plementations to policy makers and investors. Detecting the instability in the absorption ratio is a rough proxy for detecting the instability in the covariance structure. More methods about testing for the instability in the covariance structure will be developed in next two chapters.
Chapter 3
Change Point Detection in The Conditional
Correlation Structure of Multivariate Volatility
Models
3.1
Introduction
Multivariate models, whether for fitting first moment or second moments, are being more and more widely used in financial applications. As a remarkable application, testing for contagion in global financial markets has been widely re-visited after the recent great re- cession (See Chiang et al. 2007; Celık 2012; Wang and Nguyen Thi 2013; amongst others). Forbes and Rigobon (2002) defined a contagion effect as the correlation structure of several markets experiences an increase after a certain date. To identify contagion effect, major- ity of literature consider some financial events as change dates for correlation structure, but this assumption contains biases because the occurrence of change may happen earlier within some sensitive financial indicators or with delay in some macroeconomic indicators. Therefore, to be precise, it is motivated to consider an endogenous date for the change in correlation structures.
A part of previous literature on correlation breaks concentrated on using parametric methods to detect changes on the coefficients of multivariate volatility models (Andreou and Ghy- sels, 2003; Qu and Perron, 2007). This stream of tests has reasonable statistical properties but suffers from the usual difficulties associated with parametric model selection and the heavy computational cost in large dimensional systems. Later, Aue et al. (2009) proposed a non-parametric CUSUM test for detecting instabilities of volatilities and cross-volatilities of multivariate time series models. They provided an asymptotic CUSUM test for the null of no change also derived its asymptotic distribution, and applied it to several multivari- ate frameworks such as, multivariate ARMA process and constant conditional correlation GARCH. Their test was modified by Wied et al. (2012) and their test is based on sample correlations.
Although the tests of Aue et al. (2009) and Wied et al. (2012) are of merits from aspects of easy computation and good statistical properties, it is developed under the assumption
that the correlation structure remains a constant term. However, in the financial world, conditional correlation structures are seldom constant (Tse and Tsui, 2002; Engle, 2002), which can be captured through standardizing the observations by conditional variances. This paper aims to contribute to the literature on proposing a modification of the non-parametric CUSUM technique of Aue et al. (2009), to detect unknown change points of the dynamic evolving conditional correlations within multivariate volatility models.
In more detail, we propose a two steps semi-parametric test. The first step is to estimate the covariance structure through Multivariate GARCH models (including BEKK, Factor, CCC, DCC and ADCC), thereby obtaining conditional correlations and volatilities. Then, the second step is apply CUSUM test to detect correlation changes in the context of mul- tivariate models with dynamically evolving conditional correlations. In order to discuss the performance of the test under mixing property correlation structure, a Monte Carlo simula- tion study is taken on data generating process with different extents of dependence. Results present that strong mixing data would deteriorate the performance of CUSUM tests, but this limitation can be controlled in an acceptable level by using M-GARCH models to estimate conditional correlations.
We then use the semi-parametric CUSUM test to detect the correlation breaks in large financial system for identifying the contagion effect. Investigating the dynamic correlations among Latin American, Central East European and East Asian emerging markets with U.S. markets, we find that the contagion effect existed in these three regions during the Great Recession. However, the contamination from United States to East Asian markets is not as strong as to other two regions.
The paper is structured as below. In the next section, we review literature on the aspects of M-GARCH models, relevant existing change point tests and their developments. Section 3.3 discussed the theory of semi-parametric CUSUM test. In section 3.4, we assess the
performance of the test with a Monte Carlo simulation study. Section 3.5 provides an empirical application in the context of tests for financial contagion, a summary concludes.
3.2
Literature Review
The dynamics of volatilities and cross volatilities of multivariate processes play a crucial role in the understanding of the relationship between economic and especially financial observa- tions. Hence the literature on multivariate volatilities, especially on GARCH-type models is rich. For reviews we refer to Bauwens et al. (2006), Engle (2009), Silvennoinen and
Ter¨asvirta (2009) and Francq and Zakoian (2010). It has been established from an empirical
as well as theoretical point of view that non-linear multivariate processes, especially with non-constant conditional, usually outperform linear time series models. Thus generalizations of the conditional correlation model of Bollerslev (1990), including the DCC model (Engle, 2002), and their extensions (cf. Cappiello et al. (2006), Noureldin et al. (2014)), are used to produce accurate forecasts both in-sample and out-of-sample.
However, all these popular models, like every empirical model in econometrics, must also account for changes in their parameters, which might arise as a result of sudden shocks oc- curring in the economy, market crashes, financial crises or intervention of the policy markers. One of the first and most influential work on change point detection is due to Page (1955), who proposed a cumulative sum method (CUSUM). Since then, the literature has developed both parametric and non-parametric tests, the classical results have been extended to time series (cf. Bai and Perron (1998), Bai and Perron (2003), Qu and Perron (2007), Aue et al. (2006) and Aue et al. (2009)). Due to their optimality properties, likelihood-based para- metric tests have been widely used. Due to their simplicity and robustness, non-parametric,
especially CUSUM-based approaches have become popular. Cs¨org˝o and Horv´ath (1997)
Dijk (2004) used CUSUM test to detect change in the variance of a heteroscedastic process, and their results showed a deterioration in the type I. Also, they pointed out that the size and power became good again when they conducted same test on the standardized residuals
of a GARCH model. Aue and Horv´ath (2013) and Horv´ath and Rice (2014) reviewed sev-
eral methods on how to derive asymptotic properties of popular methods when dependence between the observations cannot be neglected.
It is therefore clear why this theoretical issue has become an important concern for applied economists, and the extension of change point tests to a more flexible environment has become a critical research topic for theorists. Hence the literature on this topic has been
developing in the last two decades. For example, Horv´ath and Kokoszka (1997) provided the
asymptotic behavior of change point tests for Gaussian long-range dependent data. Antoch et al. (1997) studied the behavior of partial sum change point statistics when data follows an autoregressive order 1 process, establishing its asymptotic theory. Similarly, the change point tests of Quandt (1958, 1960) which was designed for i.i.d processes, was studied by Yao and Davis (1986) and further extended to detect change points in near-epoch dependent sequences by Ling et al. (2007). Inclan and Tiao (1994) were the first to attempt to detect changes in the variance of i.i.d processes using a cumulative sums of squares test. Then, the literature developed further with the contribution of Kim (2000), who showed the consistency of CUSUM tests when applied to a GARCH (1,1) process; and further, Lee et al. (2003) applied the test of Inclan and Tiao (1994) to detect variance change of non-stationary AR(q) process with strongly mixing innovations. Through these developments, most of the change point tests have become applicable to a wider variety of economic or financial processes. However, in the context of financial data, second moments are usually modeled by ARCH or GARCH models. Kokoszka and Leipus (2000) examined change point tests in processes with dependent volatility. Their findings showed the CUSUM test is valid when applied
to short memory ARCH model making feasible to detect changes within certain types of ARCH models in financial data (cf. also Andreou and Ghysels (2002) and Fryzlewicz and Rao (2011)). Andreou and Ghysels (2002) applied the test of Kokoszka and Leipus (2000) to detect multiple changes in the volatility of high frequency stock and foreign exchange data, where the conditional variance is captured by a GARCH model. They found, consistent with results reported by Kokoszka and Leipus (2000) and De Pooter and Van Dijk (2004), that if the data are highly persistent or have long memory, the existence of change points is overestimated and the tests became completely unreliable, especially in case of nearly nonstationary processes.
Change points detection in the second moments is not limited to univariate cases, but it can be extended to the covariance and correlation structure of multivariate models. Early studies on change points detection in the covariance structure were focussed on using model selection criteria and standard stability tests on the parameters of M-GARCH models. For example, Yao (1988), Lavielle and Teyssiere (2006) proposed penalized contrast function to detect multiple changes in covariance structure simultaneously. The procedures however are only designed to be applied to i.i.d and weakly dependent constant conditional correlation (CCC) processes. However, even though these methods can detect change points within the framework of CCC models, it is arguable that allowing for dynamic conditional correlations (DCC) would be more realistic as shown in Tse and Tsui (2002) and Engle (2002). In order to detect conditional correlation change in foreign exchange market, Andreou and Ghysels (2002) modeled multivariate data by DCC model and then detected parameter changes through the test of Bai and Perron (1998). Among the theoretical studies, Aue et al. (2006) provided a strong approximation for the CUSUM statistics in the context of GARCH processes. Aue et al. (2009) constructed a CUSUM statistics for detecting changes in the covariance structure of multivariate sequences and derived their asymptotics. Their
method was transferred to study the stability of the correlation matrix by Wied et al. (2012). As mentioned above, assuming constant conditional correlations is often unrealistic, as highly-persistent conditional correlation exists as the norm especially in financial data. Hence, the emergence of the need of detecting changes in dynamically evolving persistent conditional correlation structures. In this paper, deriving the appropriate conditions, we show that the asymptotic theory of CUSUM tests is still valid for the cases where the condi- tional correlation structure is allowed to be (strongly) persistent (albeit not integrated) and modeled as constant conditional correlation (CCC), dynamic conditional correlation (DCC), BEKK, factor, asymmetric DCC and BEKK processes. Finally we apply the procedure to detect the occurrence of financial contagion as defined in Forbes and Rigobon (2002).