Contagion versus flight to quality in financial markets

None doubts that financial markets are related (interdependent). What is not so clear is whether there exists contagion among them or not, its intensity, and its causal direction. The aim of this paper is to define properly the term contagion (different from interdependence) and to present a formal test for its existence, the magnitude of its intensity, and for its direction. Our definition of contagion lies on tail dependence measures and it is made operational through its equivalence with some copula properties. In order to do that, we define a NEW copula, a variant of the Gumbel type, that is sufficiently flexible to describe different patterns of dependence, as well as being able to model asymmetric effects of the analyzed variables (something not allowed with the standard copula models). Finally, we estimate our copula model to test the intensity and the direction of the extreme causality between bonds and stocks markets (in particular, the flight to quality phenomenon) during crises periods. We find evidence of a substitution effect between Dow Jones Corporate Bonds Index with 2 years maturity and Dow Jones Stock Price Index when one of them is through distress periods. On the contrary, if both are going through crises periods a contagion effect is observed. The analysis of the corresponding 30 years maturity bonds with the stock market reflects independent effects of the shocks

Macroeconomic News, Announcements, and Stock Market Jump Intensity Dynamics

This paper examines the effect of macroeconomic releases on stock market volatility through a Poisson-Gaussian-GARCH process with time varying jump intensity, which is allowed to respond to such information. It is found that the day of the announcement, per se, has little impact on jump intensities. Employment releases are an exception. However, when macroeconomic surprises are considered, inflation shocks show persistent effects while monetary policy and employment shocks show only short-lived effects. Also, the jump intensity responds asymmetrically to macroeconomic shocks. Evidence that macroeconomic variables are relevant to explain jump dynamics and improve volatility forecasts on event days is provided.Conditional jump intensity, conditional volatility, macroeconomic announcements.

Downside Risk Efficiency Under Market Distress

In moments of financial distress downside risk measures like lower partial moments are more appropriate than the standard variance to characterize risk. The goal of this paper is to study how to choose optimal portfolios in these periods. In order to do this we extend the definition of lower partial moments to this environment, derive the corresponding mean-risk dominance set and define the concept of stochastic dominance under distress. The paper shows the close connection between the mean-risk dominance set and the stochastic dominance frontier in these situations. The advantage of using stochastic dominance is that we can readily compare investors' preferences over investment portfolios in a meaningful way regardless their degree of risk aversion. We do this by proposing a hypothesis test. Our novel family of test statistics for testing stochastic dominance under distress makes allowance for testing orders of dominance higher than one, for general forms of dependence between portfolios and can be extended to residuals of regression models. These results are illustrated in an empirical application for data from US stocks. We show that mean- variance strategies are stochastically dominated by meanrisk efficient portfolios in episodes of financial distress.Downside risk, Lower partial moments, Market distress, Mean-risk models, Mean-variance models,Stochastic dominance

Conditional stochastic dominance tests in dynamic settings

This paper proposes nonparametric consistent tests of conditional stochastic dominance of arbitrary order in a dynamic setting. The novelty of these tests resides on the nonparametric manner of incorporating the information set into the test. The test allows for general forms of unknown serial and mutual dependence between random variables, and has an asymptotic distribution under the null hypothesis that can be easily approximated by a p-value transformation method. This method has a good finite-sample performance. These tests are applied to determine investment efficiency between US industry portfolios conditional on the performance of the market portfolio. Our analysis suggests that Utilities are the best performing sectors in normal as well as distress episodes of the market.Empirical processes, Hypothesis testing, Lower partial moments, Martingale difference sequence, P-value transformation, Stochastic dominance,

A fractional Dickey-Fuller test for unit roots

This paper presents a new test for fractionally integrated (FI) processes. In particular, it proposes a testing procedure in the time domain that extends the well-known Dickey-Fuller approach. Monte-Carlo simulations support the analytical results derived in the paper and show that proposed tests fare very well, both in terms of power and size, when compared with others available in the literature. The paper ends with two empirical applications.Publicad

Testing I(1) against I(d) alternatives with Wald Tests in the presence of deterministic components

This paper analyses how to test I(1) against I(d), d<1, in the presence of deterministic components in the DGP, by extending a Wald-type test, i.e., the (Efficient) Fractional Dickey-Fuller (EFDF) test, to this case. Tests of these hypotheses are important in many economic applications where it is crucial to distinguish between permanent and transitory shocks because I(d) processes with d<1 are mean-reverting. On top of it, the inclusion of deterministic components becomes a necessary addition in order to analyze most macroeconomic variables. We show how simple is the implementation of the EFDF in these situations and argue that, in general, has better properties than LM tests. Finally, an empirical application is provided where the EFDF approach allowing for deterministic components is used to test for long-memory in the GDP p.c. of several OECD countries, an issue that has important consequences to discriminate between growth theories, and on which there has been some controversy

Simple Wald tests of the fractional integration parameter : an overview of new results

This paper presents an overview of some new results regarding an easily implementable Wald test-statistic (EFDF test) of the null hypotheses that a time-series process is I(1) or I(0) against fractional I(d) alternatives, with d∈(0,1), allowing for unknown deterministic components and serial correlation in the error term. Specifically, we argue that the EFDF test has better power properties under fixed alternatives than other available tests for fractional roots, as well as analyze how to implement this test when the deterministic components or the long-memory parameter are subject to structural breaks

Wald Tests of I(1) against I(d) alternatives : some new properties and an extension to processes with trending components

This paper analyses the power properties, under fixed alternatives, of a Wald-type test, i.e., the (Efficient) Fractional Dickey-Fuller (EFDF) test of I(1) against I(d), d<1, relative to LM tests. Further, it extends the implementation of the EFDF test to the presence of deterministic trending components in the DGP. Tests of these hypotheses are important in many macroeconomic applications where it is crucial to distinguish between permanent and transitory shocks because shocks die out in I(d) processes with d<1. We show how simple is the implementation of the EFDF in these situations and argue that, under fixed alternatives, it has better power properties than LM tests. Finally, an empirical application is provided where the EFDF approach allowing for deterministic components is used to test for long-memory in the GDP p.c. of several OECD countries, an issue that has important consequences to discriminate between alternative growth theories