Measuring asymmetries in financial returns : an empirical investigation using local gaussian correlation

A number of studies have provided evidence that financial returns exhibit asymmetric dependence, such as increased dependence during bear markets, but there seems to be no agreement as to how such asymmetries should be measured. We introduce the use of a new measure of local dependence to study this asymmetry. 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. The new measure does not suffer from the selection bias of the conditional correlation for Gaussian data, and is able to capture nonlinear dependence. Analyzing several financial returns from the US, UK, German and French markets, we confirm and are able to explicitly quantify the asymmetry. Finally, we discuss a risk management application, and point out a number of possible extensions

Measuring Financial Contagion by Local Gaussian Correlation

This paper examines financial contagion, that is, whether the cross-market linkages in financial markets increases after a shock to a country. We introduce the use of a new measure of local dependence (introduced by Hufthammer and Tjøstheim (2009)) 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 proposed bootstrap testing procedure. Examining the Mexican crisis of 1994, the Asian crisis of 1997-1998 and the financial crisis of 2007-2009, we find some evidence of contagion based on our new procedure.Financial Contagion; Crisis; Gaussian Correlation

Recognizing and visualizing copulas : an approach using local Gaussian approximation

Copulas are much used to model nonlinear and non-Gaussian dependence between stochastic variables. Their functional form is determined by a few parameters, but unlike a dependence measure like the correlation, these parameters do not have a clear interpretation in terms of the dependence structure they create. In this paper we examine the relationship between a newly developed local dependence measure, the local Gaussian Correlation, and standard copula theory. We are able to describe characteristics of the dependence structure in different copula models in terms of the local Gaussian correlation. In turn, these characteristics can be effectively visualized. More formally, the characteristic dependence structure can be used to construct a goodness-of-fit test for bivariate copula models by comparing the theoretical local Gaussian correlation for a specific copula and the estimated local Gaussian correlation. A Monte Carlo study reveals that the test performs very well compared to a commonly used alternative test. We also propose two types of diagnostic plots which can be used to investigate the cause of a rejected null. Finally, our methods are used on a ”classic” insurance data set

Modelling clusters of corporate defaults: Regime-switching models significantly reduce the contagion source

In this paper, we report robust evidence that the process of corporate defaults is time-dependent and can be modelled by extending an autoregressive count time series model class via the introduction of regime-switching. That is, some of the parameters of the model depend on the regime of an unobserved Markov chain, capturing the model changes during clusters observed for count time series in corporate defaults. Thus, the process of corporate defaults is more dynamic than previously believed. Moreover, the contagion effect—that current defaults affect the probability of other firms defaulting in the future—is reduced compared to models without regime-switching, and is only present in one regime. A two-regime model drives the counts of monthly corporate defaults in the United States. To estimate the model, we introduce a novel quasi-maximum likelihood estimator by adapting the extended Hamilton–Gray algorithm for the Poisson autoregressive model.publishedVersio

Testing for asymmetric dependency structures in financial markets: regime-switching and local Gaussian correlation

This paper examines asymmetric and time-varying dependency structures between financial returns, using a novel approach consisting of a combination of regime-switching models and the local Gaussian correlation (LGC). We propose an LGC-based bootstrap test for whether the dependence structure in financial returns across different regimes is equal. We examine this test in a Monte Carlo study, where it shows good level and power properties. We argue that this approach is more intuitive than competing approaches, typically combining regime-switching models with copula theory. Furthermore, the LGC is a semi-parametric approach, hence avoids any parametric specification of the dependence structure. We illustrate our approach using returns from the US-UK stock markets and the US stock and government bond markets. Using a two-regime model for the US-UK stock returns, the test rejects equality of the dependence structure in the two regimes. Furthermore, we find evidence of lower tail dependence in the regime associated with financial downturns in the LGC structure. For a three-regime model fitted to US stock and bond returns, the test rejects equality of the dependence structures between all regime pairs. Furthermore, we find that the LGC has a primarily positive relationship in the time period 1980-2000, mostly a negative relationship from 2000 and onwards. In addition, the regime associated with bear markets indicates less, but asymmetric dependence, clearly documenting the loss of diversification benefits in times of crisis

The European Biological Variation Study (EuBIVAS): Biological Variation Data for Coagulation Markers Estimated by a Bayesian Model.

Abstract Background For biological variation (BV) data to be safely used, data must be reliable and relevant to the population in which they are applied. We used samples from the European Biological Variation Study (EuBIVAS) to determine BV of coagulation markers by a Bayesian model robust to extreme observations and used the derived within-participant BV estimates [CVP(i)] to assess the applicability of the BV estimates in clinical practice. Method Plasma samples were drawn from 92 healthy individuals for 10 consecutive weeks at 6 European laboratories and analyzed in duplicate for activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, D-dimer, antithrombin (AT), protein C, protein S free, and factor VIII (FVIII). A Bayesian model with Student t likelihoods for samples and replicates was applied to derive CVP(i) and predicted BV estimates with 95% credibility intervals. Results For all markers except D-dimer, CVP(i) were homogeneously distributed in the overall study population or in subgroups. Mean within-subject estimates (CVI) were <5% for APTT, PT, AT, and protein S free, <10% for protein C and FVIII, and <12% for fibrinogen. For APTT, protein C, and protein S free, estimates were significantly lower in men than in women ≤50 years. Conclusion For most coagulation markers, a common CVI estimate for men and women is applicable, whereas for APTT, protein C, and protein S free, sex-specific reference change values should be applied. The use of a Bayesian model to deliver individual CVP(i) allows for improved interpretation and application of the data

A convolution estimator for the density of nonlinear regression observations

The problem of estimating an unknown density function has been widely studied. In this paper we present a convolution estimator for the density of the responses in a nonlinear regression model. The rate of convergence for the variance of the convolution estimator is of order n-1. This is faster than the rate for the kernel density method. The intuition behind this result is that the convolution estimator uses model information, and thus an improvement can be expected. We also derive the bias of the new estimator and conduct simulation experiments to check the finite sample properties. The proposed estimator performs substantially better than the kernel density estimator for well-behaved noise densities