Are Banking Systems Increasingly Fragile ? Investigating Financial Institutions’ CDS Returns Extreme Co-Movements

This paper investigates potential contagion among the major financial institutions in developed economies. Using Credit Default Swaps (CDS) premia as a measure of credit or counterparty risk, our analysis focuses on the extreme co-movements of Financial Institutions' default contracts during the high level of stress undergone by the CDS markets in the aftermath of the 2007 sub-prime crisis. Our approach is twofold: first, under different tail dependence scenarios, we calibrate several multivariate linear propagation models of constant correlation. Our Monte Carlo simulation study finds evidence of contagion for Financial Institutions- notably in the US-and captures a non-normal dependence structure in the tails for the traded contracts. Second, we estimate a multivariate Dynamic Conditional Correlation-GARCH (DCC-GARCH) model, and demonstrate significant ARCH and GARCH effects, as well as time-varying correlations in CDS spreads variations. Our overall analysis rejects the assumption of constant correlation. More importantly, it advocates changing structures in tail dependence for CDS series during times of financial turmoil as an important feature of banks’ increased fragility.Bank fragility, Counterparty risk, Financial crises, Extreme co-movements, Conditional correlation, Multivariate GARCH, Monte Carlo simulation

Free Trade Agreements and Volatility of Stock Returns and Exchange Rates: Evidence from NAFTA

This paper uses GARCH models and daily data to investigate the effect of the Canada – U.S. Free Trade Agreement (CUSFTA) and NAFTA on the volatility of, and the relationship between stock market returns and changes in bilateral exchange rates of the member countries. Empirical results indicate that the CUSFTA had a stabilizing effect on the Canadian and U.S. equity markets while increasing the volatility of the CAD/USD exchange rate. NAFTA further reduced the two stock markets’ volatility, however unlike CUSFTA, NAFTA also reduced the volatility of the CAD/USD exchange rate. Additional results indicate that during NAFTA, the Mexican stock market is more volatile than the other stock and bilateral exchange markets. Moreover, the exchange rate of the Mexican peso against both the U.S. and Canadian dollars has been more volatile than the Canadian dollar/US dollar exchange rate. Evidence also suggests that all three stock markets are positively correlated with each other with the U.S. market being much less correlated with the Canadian and Mexican stock markets than the latter two markets are correlated with each other. Evidence found in this paper suggests a negative relationship between the stock and bilateral currency markets that is statistically significant except for the U.S. equity market when paired with an exchange rate that involves the Mexican peso

The changing dynamics of US inflation persistence : a quantile regression approach : [Version 4 September 2012]

We examine both the degree and the structural stability of inflation persis tence at different quantiles of the conditional inflation distribution. Previous research focused exclusively on persistence at the conditional mean of the inflation rate. Economic theory, however, provides various reasons -for example downward wage rigidities or menu costs- to expect higher inflation persistence at the upper than at the lower tail of the conditional inflation distribution. Based on post-war US data we indeed find slower mean reversion in response to positive than to negative shocks. We find robust evidence for a structural break in persistence at all quantiles of the inflation process in the early 1980s. Inflation persistence has decreased and become more homogeneous across quantiles. Persistence at the conditional mean became more informative about the degree of persistence across the entire conditional inflation distribution. While prior to the 1980s inflation was not mean reverting in response to large positive shocks, our evidence strongly suggests that since the end of the Volcker disinflation the unit root can be rejected at every quantile including the upper tail of the conditional inflation distribution

Analysis of high-resolution foreign exchange data of USD-JPY for 13 years

We analyze high-resolution foreign exchange data consisting of 20 million data points of USD-JPY for 13 years to report firm statistical laws in distributions and correlations of exchange rate fluctuations. A conditional probability density analysis clearly shows the existence of trend-following movements at time scale of 8-ticks, about 1 minute.Comment: 6 pages, 7 figures, submitted to Physica

Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data

Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived

Time-Varying Risk Perceptions and the Pricing of Risky Assets

Empirical results based on two different statistical approaches lead to several conclusions about the role of time-varying asset risk assessments in accounting for what, on the basis of many earlier studies, appear to be time-varying differentials in ex ante asset returns. First, both methods indicate sizeable changes over time in variance-covariance structures conditional on past information. These changing conditional variance-covariance structures in turn imply sizeable changes over time in asset demand behavior, and hence in the market-clearing equilibrium structure of ex ante asset returns. Second, at least for some values of the parameter indicating how rapidly investors discount the information contained in past observations, the implied ex ante excess returns bear non-negligible correlation to observed ex post excess returns on either debt or equity. The percentage of the variation of ex post excess returns explained by the implied time-varying ex ante excess returns is comparable to values to which previous researchers have interpreted as warranting rejection of the hypothesis that risk premia are constant over time. Third, although for long-term debt the two statistical methods used here give sharply different answers to the question of how much relevance market participants associate with past observations in assessing future risks, for equities both methods agree in indicating extremely rapid discounting of more distant observations -- so much so that in neither case do outcomes more than a year in the past matter much at all. While the paper's other conclusions are plausible enough, the finding of such an extremely short "memory" on the part of equity investors suggests that the standard representation of equity risk by a single normally distributed disturbance is overly restrictive.

Sparse Vector Autoregressive Modeling

The vector autoregressive (VAR) model has been widely used for modeling temporal dependence in a multivariate time series. For large (and even moderate) dimensions, the number of AR coefficients can be prohibitively large, resulting in noisy estimates, unstable predictions and difficult-to-interpret temporal dependence. To overcome such drawbacks, we propose a 2-stage approach for fitting sparse VAR (sVAR) models in which many of the AR coefficients are zero. The first stage selects non-zero AR coefficients based on an estimate of the partial spectral coherence (PSC) together with the use of BIC. The PSC is useful for quantifying the conditional relationship between marginal series in a multivariate process. A refinement second stage is then applied to further reduce the number of parameters. The performance of this 2-stage approach is illustrated with simulation results. The 2-stage approach is also applied to two real data examples: the first is the Google Flu Trends data and the second is a time series of concentration levels of air pollutants.Comment: 39 pages, 7 figure