CONSISTENT ESTIMATION OF RESIDUAL VARIANCE IN REGULATORY EVENT STUDIES

This study presents new evidence on alternative methods used to test for abnormal returns in regulatory event studies where cross-sectional correlation in residuals is significant. Results contradict earlier studies that find no advantages to using joint generalized least squares (JGLS) methods over ordinary least squares (OLS). We find that in an actual regulatory event study cross-correlation is significant, and that failing to correct for this correlation results in substantially higher calculated F-statistics. In Monte Carlo simulations we find that OLS test statistics are not well specified when residuals exhibit cross-sectional correlation at levels that are reasonable to expect in daily return data, while JGLS test statistics are well specified. The study includes tests of the effective power of the OLS and JGLS statistics.     

Disentangling contagion among sovereign CDS spreads during the European debt crisis

During the last crisis, developed economies' sovereign credit default swap (hereafter CDS) premia have gained in importance as a tool for approximating credit risk. In this paper, we fit a dynamic factor model to decompose the sovereign CDS spreads of ten OECD economies into three components: a common factor, a second factor driven by European peripheral countries and an idiosyncratic component. We use this decomposition to propose a novel methodology based on the real-time estimates of the model to characterize contagion among the ten series. Our procedure allows the country that triggers contagion in each period, which can be any peripheral economy, to be disentangled. According to our findings, since the onset of the sovereign debt crisis, contagion has played a non-negligible role in the European peripheral countries, which confirms the existence of significant financial linkages between these economies.     

Testing for financial contagion based on a nonparametric measure of the cross‐market correlation

When contagion is defined as a significant increase in market comovement after a shock to one country, we propose a test for financial contagion based on a nonparametric measure of the cross-market correlation. Monte Carlo simulation studies show that our test has reasonable size and good power to detect financial contagion, and that Forbes and Rigobon's test (2002) is relatively conservative, indicating that their test tends not to find evidence of contagion when it does exist. Applying our test to investigate contagion from the 1997 East Asian crisis and the 2007 Subprime crisis, we find that there existed international financial contagion from the two financial crises.     

Using local Gaussian correlation in a nonlinear re-examination of financial contagion

This paper examines financial contagion, that is, whether the cross-market linkages in financial markets increase after a shock to a country. We use a new measure of local dependence (introduced by Tjøstheim and Hufthammer (2013)) to study the contagion effect. The central idea of the new approach is to approximate an arbitrary bivariate return distribution by a family of Gaussian bivariate distributions. At each point of the return distribution there is a Gaussian distribution that gives a good approximation at that point. The correlation of the approximating Gaussian distribution is taken as the local correlation in that neighbourhood. By examining the local Gaussian correlation before the shock (in a stable period) and after the shock (in the crisis period), we are able to test whether contagion has occurred by a bootstrap testing procedure. The use of local Gaussian correlation is compared to other methods of studying contagion. Further, the bootstrap test is examined in a Monte Carlo study, and shows good level and power properties. We illustrate our approach by re-examining the Mexican crisis of 1994, the Asian crisis of 1997–1998 and the financial crisis of 2007–2009. We find evidence of contagion based on our new procedure and are able to describe the nonlinear dependence structure of these crises.     

Time-varying integration,interdependence and contagion

Bekaert et al. (2005) define contagion as “correlation over and above what one would expect from economic fundamentals”. Based on a two-factor asset pricing specification to model fundamentally-driven linkages between markets, they define contagion as correlation among the model residuals, and develop a corresponding test procedure. In this paper, we investigate to what extent conclusions from this contagion test depend upon the specification of the time-varying factor exposures. We develop a two-factor model with global and regional market shocks as factors. We make the global and regional market exposures conditional upon both a latent regime variable and three structural instruments, and find that, for a set of 14 European countries, this model outperforms more restricted versions. The structurally-driven increase in global (regional) market exposures and correlations suggest that market integration has increased substantially over the last three decades. Using our optimal model, we do not find evidence that further integration has come at the cost of contagion. We do find evidence for contagion, however, when more restricted versions of the factor specifications are used. We conclude that the specification of the global and regional market exposures is an important issue in any test for contagion.     

The 2008 financial crisis: Stock market contagion and its determinants

In this paper, we investigate worldwide contagion and its determinants during the 2008 financial crisis. Utilizing an international sample of returns from 2003 to 2009, we consider both uni- and bi-directional contagion. After controlling for crisis-related volatility, we find strong evidence that cross-market linkages increase among many financial markets. In contrast to previous crises, contagion following the 2008 global financial crisis is not confined to emerging markets. The United States and other mature financial markets in the sample transmit and receive contagion. Country markets are less influenced by regions than they are by other country markets. We also construct variables that represent relative changes in economic variables before and during the crisis. We find that both economic fundamentals such as trade structure, interest rates, inflation rates, industrial production, and regional effects, and investors’ risk aversion contribute to international contagion.     

Risk assessment based on the analysis of the impact of contagion flow

This paper presents a new framework to model and calibrate the process of firm value evolution when an unanticipated exogenous event impacting one firm can contagiously affect other firms. The nature of propagation of such contagion is determined by the underlying connections between firms, which can adversely affect the tail risks of firm value, hence the securities issued by the firm. This paper combines the insights gained from the existing firm-value models and historical events into a structural model for flow of contagion among firms using a network-based approach. Rather than using stylized networks, we develop a data-driven approach for network construction where we define and calibrate several contagion variables to model the spread of contagion. This framework is applied for assessing firm-level risk under downside risk measures. Using actual data, our model illustrates how connections between firms can lead to heavy-tailed default distributions and default clustering observed in practice.     

Constructing Commercial Indices: A Semiparametric Adaptive Estimator Approach

Constant-quality commercial indices generated by ordinary least squares may suffer an efficiency loss due to leptokurtosis caused by outliers in transactions data. When the subsequent nonnormality occurs, substantial improvement in index precision is obtained by estimating the hedonic model using a semiparametric adaptive estimator technique. When this method was applied to 1,846 office transactions that occurred in the Phoenix metropolitan area from January 1997 through June 2004, a substantial standard error reduction of approximately 9% was realized relative to ordinary least squares estimates. The difference in average returns between the semiparametric method and ordinary least squares was about 0.25% in each period, which represents a substantial increase in commercial property index precision. JEL Classification C4 R0     

Financial contagion: evolutionary optimization of a multinational agent-based model

Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during a crisis are referred to as financial contagion. We simulate crisis transmission in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a minority game approach, we develop an agent-based multinational model and investigate the reasons for contagion. Although the phenomenon has been extensively investigated in the financial literature, it has not been studied through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognizing financial crises with the potential to destabilize cross-market linkages. In the real world, such information would be extremely valuable in developing appropriate risk management strategies. Copyright © 2009 John Wiley & Sons, Ltd.     

Time varying contagion in EMU government bond spreads

We analyze the time varying behavior of pure contagion effects between Economic and Monetary Union (EMU) government bond spreads before and during the subprime mortgage crisis and the EMU debt crisis. By conducting a rolling window analysis, we are able to monitor the evolution of pure contagion effects and the changing influence of exogenous factors over time. Importantly, this is done without an ex-ante specification of the contagion window. Hence, we are able to determine the exact timing of the start and end for the different contagion periods. In contrast to related studies, we use a slightly different definition of contagious events and show that this approach leads to different conclusions about the progression of the EMU debt crisis. First, the main sources of pure contagion in the later phase of the EMU debt crisis appear to be Italy and Spain and not Greece, Ireland and Portugal. Furthermore, we find that substantial contagion effects among EMU government bond spreads (caused by Ireland and Portugal) already arise during the subprime mortgage crisis and not only during the EMU debt crisis, as one might expect.     

Using Cointegration Restrictions to Improve Inference in Vector Autoregressive Systems

An improved way of dealing with uncertain prior information in the context of vector autoregressive systems of equations is proposed. The procedure is appropriate when inference about parameters of a cointegrated system is the aim of the analysis. The estimator uses uncertain prior information about the existence of trends and co-trends in the time series to improve parameter estimation within these systems. The improved estimator eliminates the need to carry out the unit root, cointegration, and parameter restriction pretests and is shown in our Monte Carlo experiments to have good statistical properties in small samples. The pretest, maximum likelihood, and restricted maximum likelihood estimators are compared to the proposed estimator based on squared error risk, mean square error of prediction risk, and out-of-sample root-mean-square forecast error. The Monte Carlo simulations are based on actual economic data collected for eurodollar futures contracts. The evidence suggests that the parameters of vector autoregressive systems can be estimated with lower mean square error with the new estimator even when prior guesses about the nature of the cointegrating vector(s) are incorrect. In-sample prediction is likewise improved. The Monte Carlo simulations are based on eurodollar spot and futures market data that has been used to test the unbiased expectations hypothesis.Marjory B. Ourso Center for Excellence in Teaching Professor     

Financial instability contagion: a dynamical systems approach

We build a multi-agent dynamical system for the global economy to investigate and analyse financial crises. The agents are large aggregates of a subeconomy, and the global economy is a collection of subeconomies. We use well-known theories of dynamical systems to represent a financial crisis as propagation of a negative shock on wealth due the breakage of a financial equilibrium. We first extend the framework of the market instability indicator, an early warning signal defined for a single economy as the spectral radius of the Jacobian matrix of the wealth dynamical system. Then, we formulate a quantitative definition of instability contagion in terms thereof. Finally, we analyse the mechanism of instability contagion for both single and multiple economies. Our contribution is to provide a methodology to quantify and monitor the level of instability in sectors and stages of a structured global economic model and how it may propagate through its components.     

Estimation of risk contributions with MCMC

Determining risk contributions of unit exposures to portfolio-wide economic capital is an important task in financial risk management. Computing risk contributions involves difficulties caused by rare-event simulations. In this study, we address the problem of estimating risk contributions when the total risk is measured by value-at-risk (VaR). Our proposed estimator of VaR contributions is based on the Metropolis-Hasting (MH) algorithm, which is one of the most prevalent Markov chain Monte Carlo (MCMC) methods. Unlike existing estimators, our MH-based estimator consists of samples from the conditional loss distribution given a rare event of interest. This feature enhances sample efficiency compared with the crude Monte Carlo method. Moreover, our method has consistency and asymptotic normality, and is widely applicable to various risk models having a joint loss density. Our numerical experiments based on simulation and real-world data demonstrate that in various risk models, even those having high-dimensional (≈500) inhomogeneous margins, our MH estimator has smaller bias and mean squared error when compared with existing estimators.     

Looking for risk premium and contagion in Asia-Pacific foreign exchange markets

This article tests pure contagion effects among four Asian foreign exchange markets, namely, Japan, Hong Kong, Singapore, and Taiwan during the 1997 Asian crisis. A conditional version of international capital asset pricing model (ICAPM) in the absence of purchasing power parity (PPP) is used to control for economic fundamentals or systematic risks. The empirical results show strong contagion effects in both conditional means and volatilities of those markets after systematic risks have been accounted for. Specifically, the contagion-in-mean effects are mainly driven by the past return shocks in Hong Kong, Singapore, and Taiwan. As for contagion in volatility, the lead/lag relationships appear to be multidirectional among Japan, Singapore, and Taiwan, but between Hong Kong and Singapore, and between Hong Kong and Taiwan, they are unidirectional, with Hong Kong playing the dominant role in generating negative volatility shocks. In addition, the conditional ICAPM with asymmetric multivariate general autoregressive conditional heteroscedastic in mean (MGARCH(1,1)-M) structure is able to explain/predict on average 17.28% of the return variations in those markets. Therefore, this study provide a further evidence that the time-varying risk premium is a very strong candidate in explaining the predictable excess return puzzle [Lewis, K. K. (1994). Puzzles in international financial markets. NBER Working Paper No. 4951] since the risk premia founded in this article are not only statistically significant but also economically significant.     

Exchange rate contagion in Latin America

A regular vine copula approach is implemented for testing for contagion among the exchange rates of the six largest Latin American countries. Using daily data from June 2005 through April 2012, we find evidence of contagion among the Brazilian, Chilean, Colombian and Mexican exchange rates. However, there are interesting differences in contagion during periods of large exchange rate depreciation and appreciation. Our results have important implications for the response of Latin American countries to currency crises originated abroad.     

The mechanism of credit risk contagion among internet P2P lending platforms based on a SEIR model with time-lag

The credit risk contagion of Internet peer-to-peer (P2P) lending platforms is an important part of Internet financial risk management and supervision. This study analyzes the contagion path of credit risk in Internet P2P lending. Based on complex network theory and the theory of infectious disease dynamics, the characteristics of Internet P2P lending development are combined to construct a SEIR model of credit risk transmission among Internet P2P lending platforms with time lag, and the robustness of the model is analyzed and proven. The influence of platform correlations, the susceptible immune rate, the platform elimination rate, contagion latency, the saturation coefficient, and the susceptibility input rate on credit risk contagion behavior among Internet P2P lending platforms is analyzed, using the equilibrium point and threshold value. The impact of each variable is analyzed by simulation. Corresponding countermeasures and suggestions are proposed to prevent and control credit risk contagion among these platforms.     

Deconstructing the Nasdaq bubble: A look at contagion across international stock markets

Share prices for the technology, media, and telecommunication (TMT) sector experienced phenomenal growth and decline at the turn of this century in the U.S. and many other OECD economies. We investigate whether contagion occurred from the U.S. to other international stock markets after the Nasdaq bubble collapsed. Results document a significant structural break in comovements between the international TMT sectors, and suggest that the collapse of the stock market in more than a dozen countries is tied to close sectoral links (particularly in TMT), and cannot be attributed to widespread contagion. We also show the importance of modeling the intrinsic heteroskedasticity in the data using a GARCH framework for inferences on contagion.     

Estimating Cointegrated Panels with Common Factors and the Forward Rate Unbiasedness Hypothesis

This article proposes a bias-adjusted estimator for use in cointegratedpanel regressions when the errors are cross-sectionally correlatedthrough an unknown common factor structure. The asymptotic distributionof the new estimator is derived and is examined in small samplesusing Monte Carlo simulations. For the estimation of the numberof factors, several information-based criteria are considered.The simulation results suggest that the new estimator performswell in comparison to existing ones. In our empirical application,we provide new evidence suggesting that the forward rate unbiasednesshypothesis cannot be rejected.     

Estimating and testing beta pricing models: Alternative methods and their performance in simulations

We conduct a simulation analysis of the Fama and MacBeth[1973. Risk, returns and equilibrium: empirical tests. Journal of Political Economy 71, 607–636.] two-pass procedure, as well as maximum likelihood (ML) and generalized method of moments estimators of cross-sectional expected return models. We also provide some new analytical results on computational issues, the relations between estimators, and asymptotic distributions under model misspecification. The generalized least squares estimator is often much more precise than the usual ordinary least squares (OLS) estimator, but it displays more bias as well. A “truncated” form of ML performs quite well overall in terms of bias and precision, but produces less reliable inferences than the OLS estimator.     

Tail risk contagion between international financial markets during COVID-19 pandemic

In this paper, we combine the time-varying financial network model and FARM-selection approach to analyze the tail risk contagion between international financial market during the COVID-19 epidemic. Since the tail risk acts as a global transmission channel, we use the sample of 19 international financial markets to explore the contagion of tail risk during the epidemic. We find that the COVID-19 epidemic increases the number of contagion channels in the international financial system. The clustering level of the financial system has a significant growth during the COVID-19 pandemic, and the number of risk drivers is also larger than risk takers. The key financial market of each international financial network is related to the epidemic country. We also consider the tail risk contagion in local financial markets and find that the COVID-19 pandemic has an important influence on the tail risk contagions in local network systems