Hans Manner

A Survey on Time-Varying Copulas: Specification, Simulations, and Application

The aim of this article is to bring together different specifications for copula models with time-varying dependence structure. Copula models are widely used in financial econometrics and risk management. They are considered to be a competitive alternative to the Gaussian dependence structure. The dynamic structure of the dependence between the data can be modeled by allowing either the copula function or the dependence parameter to be time-varying. First, we give a brief description of eight different models, among which there are fully parametric, semiparametric, and adaptive methods. The purpose of this study is to compare the applicability of each particular model in different cases. We conduct a simulation study to show the performance for model selection, to compare the model fit for different setups and to study the ability of the models to estimate the (latent) time-varying dependence parameter. Finally, we provide an illustration by applying the competing models on the same financial dataset and compare their performance by means of Value-at-Risk.

On factors related to car accidents on German Autobahn connectors

We make an attempt to identify factors that explain accidents on German Autobahn connectors. To find these factors we perform an empirical study making use of count data models with fixed and random coefficients. The findings are based on a set of 197 ramps, which we classify into three distinct types of ramps. For these ramps, accident data is available for a period of 3 years (January 2003 until December 2005). The negative binomial model with some random coefficients proved to be an appropriate model in our cross-sectional setting for detecting factors that are related to accidents. The most significant variable is a measure of the average daily traffic. For geometric variables, not only continuous effects were found to be significant, but also threshold effects indicating the exceedance of certain values.

Analyzing the severity of accidents on the German Autobahn

We study the severity of accidents on the German Autobahn in the state of North Rhine-Westphalia using data for the years 2009 until 2011. We use a multinomial logit model to identify statistically relevant factors explaining the severity of the most severe injury, which is classified into the four classes fatal, severe injury, light injury and property damage. Furthermore, to account for unobserved heterogeneity we use a random parameter model. We study the effect of a number of factors including traffic information, road conditions, type of accidents, speed limits, presence of intelligent traffic control systems, age and gender of the driver and location of the accident. Our findings are in line with studies in different settings and indicate that accidents during daylight and at interchanges or construction sites are less severe in general. Accidents caused by the collision with roadside objects, involving pedestrians and motorcycles, or caused by bad sight conditions tend to be more severe. We discuss the measures of the 2011 German traffic safety programm in the light of our results.

Testing For Asset Market Linkages: A New Approach Based On Time-Varying Copulas

This paper proposes a new approach based on time-varying copulas to test for the presence of increases in stock market interdependence (also known as shift contagion) after a financial crisis. We discuss the importance of considering simultaneously separate breaks in volatility and dependence. Without such consideration, the contagion test turns out to be biased. A sequential algorithm is proposed to tackle this problem. Applied to the recent 1997 Asian crisis, the analysis confirms that breaks in variances always precede those in the dependence parameter. Moreover, a significant ‘J-shape’ evolution of the dependence parameter is detected, supporting the idea of shift contagion.

The “wrong skewness” problem in stochastic frontier models: A new approach

ABSTRACT Stochastic frontier models are widely used to measure, e.g., technical efficiencies of firms. The classical stochastic frontier model often suffers from the empirical artefact that the residuals of the production function may have a positive skewness, whereas a negative one is expected under the model, which leads to estimated full efficiencies of all firms. We propose a new approach to the problem by generalizing the distribution used for the inefficiency variable. This generalized stochastic frontier model allows the sample data to have the wrong skewness while estimating well-defined and nondegenerate efficiency measures. We discuss the statistical properties of the model, and we discuss a test for the symmetry of the error term (no inefficiency). We provide a simulation study to show that our model delivers estimators of efficiency with smaller bias than those of the classical model even if the population skewness has the correct sign. Finally, we apply the model to data of the U.S. textile industry for 1958–2005 and show that for a number of years our model suggests technical efficiencies well below the frontier while the classical one estimates no inefficiency in those years.

Models for short-term forecasting of spike occurrences in Australian electricity markets: A comparative study

Understanding the dynamics of extreme observations, so called spikes, in real-time electricity prices has a crucial role in risk-management and trading. Yet the contemporaneous literature appears to be at the beginning of understanding the dierent mechanisms that drive spike probabilities. We reconsider the problem of short-term, i.e., half hourly, forecasts of spike occurrence in the Australian electricity market and develop models, tailored to capture the data properties. These models are variations of a dynamic binary response model, extended to allow for regime specic eects and an asymmetric link function. Furthermore, we study a recently proposed approach based on the autoregressive conditional hazard model. The proposed models use load forecasts and lagged log-prices as exogenous variables. Our in- and out-of-sample results suggest that some specications dominate and can therefore be recommended for the problem of spike forecasting.

Multivariate Time Series Models for Asset Prices

The modelling of multivariate financial time series has attracted an enormous interest recently, both from a theoretical and practical perspective. Focusing on factor type models that reduce the dimensionality and other models that are tractable in high dimensions, we review models for volatility, correlation and dependence, and show their application to quantities of interest such as value-at-risk or minimum-variance portfolio. In an application to a 69-dimensional asset price time series, we compare the performance of factor-based multivariate GARCH, stochastic volatility and dynamic copula models.

Asymmetries in Business Cycles and the Role of Oil Prices

We estimate asymmetries in innovations to Solow residuals for eleven OECD countries using Stochastic Frontier Analysis. Likelihood ratio statistics and variance ratios imply that all countries with net energy imports have significant negative asymmetries, while other countries do not. Conditioning Solow residuals on Hamilton’s (2011) net oil price increase variable reduces evidence of negative asymmetries. We construct a simple theoretical model which yields an asymmetric response of measured TFP for oil importers to oil prices. The asymmetric response is due to the effect of the endogenous adjustment of imported energy on value added measured in constant dollars.

Improved Forecasting of Realized Variance Measures

We consider the problem of forecasting realized variance measures. These measures are highly persistent, but also noisy estimates of the underlying integrated variance. Recently, Bollerslev, Patton and Quaedvlieg (2016, Journal of Econometrics, 192, 1-18) exploited this fact to extend the commonly used Heterogeneous Autoregressive (HAR) by letting the model parameters vary over time depending on estimated measurement errors. We propose an alternative specification that allows the autoregressive parameter of the HAR model for volatilities to be driven by a latent Gaussian autoregressive process that may depend on the estimated measurement error. The model is estimated using the Kalman filter. Our analysis considers realized volatilities of 40 stocks from the S&P 500 for three different observation frequencies. Our preferred model provides a better model fit and generates superior forecasts. It consistently outperforms the competing models in terms of different loss functions and for various subsamples of the forecasting period.

TESTING FOR ASSET MARKET LINKAGES: A NEW APPROACH BASED ON TIME-VARYING COPULAS: testing for asset market linkages

This paper proposes a new approach based on time-varying copulas to test for the presence of increases in stock market interdependence (also known as shift contagion) after a financial crisis. We discuss the importance of considering simultaneously separate breaks in volatility and dependence. Without such consideration, the contagion test turns out to be biased. A sequential algorithm is proposed to tackle this problem. Applied to the recent 1997 Asian crisis, the analysis confirms that breaks in variances always precede those in the dependence parameter. Moreover, a significant ‘J-shape’ evolution of the dependence parameter is detected, supporting the idea of shift contagion.