Christian Haefke

Turbulence and Unemployment in a Job Matching Model

According to Ljungqvist and Sargent (1998), high European unemployment since the 1980s can be explained by a rise in economic turbulence, leading to greater numbers of unemployed workers with obsolete skills. These workers refuse new jobs due to high unemployment benefits. In this paper we reassess the turbulence-unemployment relationship using a matching model with endogenous job destruction. In our model, higher turbulence reduces the incentives of employed workers to leave their jobs. If turbulence has only a tiny effect on the skills of workers experiencing endogenous separation, then the results of Ljungqvist and Sargent (1998, 2004) are reversed, and higher turbulence leads to a reduction in unemployment. Thus, changes in turbulence cannot provide an explanation for European unemployment that reconciles the incentives of both unemployed and employed workers.

Subsampling the distribution of diverging statistics with applications to finance

In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known or unknown rates when the underlying time series is strictly stationary and strong mixing. Based on our results we provide a detailed discussion of how to estimate extreme order statistics with dependent data and present two applications to assessing financial market risk. Our method performs well in estimating Value at Risk and provides a superior alternative to Hills estimator in operationalizing Safety First portfolio selection.

Index forecasting and model selection

In this paper we derive a trading strategy that exploits the informational difference implied by different stock market index construction principles. In order to gain a competitive advantage over other market participants we forecast the indexes one day ahead and subsequently generate buy and sell signals through the trading rule. To illustrate how the system works we apply it to select from those stocks that are included in the Austrian Traded Index (ATX). The forecasting of the indexes is performed on the basis of standard financial econometric techniques and feedforward neural networks. We discuss the importance of parsimonious modeling and the applicability of information criteria for architecture selection in artificial neural networks. Copyright

A neural network model to exploit the econometric properties of Austrian IPOs

Applies cointegration and Granger (1969) causality analyses to specify linear and neural network error-correction models for IPOX/sub ATX/ (Initial Public Offerings indeX for the Austrian Traded indeX). We use the significant relationship between IPOX/sub ATX/ and the Austrian stock market index ATX to forecast IPOX/sub ATX/. For prediction purposes, we apply augmented feedforward neural networks whose architecture is determined by sequential network construction with the Schwartz (1978) information criterion as an estimator for the prediction risk. The results suggest that trading schemes based on the forecasts significantly increase an investors return as compared to buy-and-hold or simple moving-average trading strategies.

The applicability of information criteria for neural network architecture selection

In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment of the Vienna stock exchange might be less efficient than the whole market and hence easier to forecast. Extending the focus of investigation in the paper, we use feedforward networks and linear models to predict the all share index WBI as well as various subindexes covering the highly liquid, semi-liquid, and initial public offering (IPO) market segment. In order to shed some light on network construction principles, we compare different models as selected by hold-out cross-validation (HCV), Akaikes (1974) information criterion (AIC), and Schwartz (1978) information criterion (SIC). The forecasts are subsequently evaluated on the basis of hypothetical trading in the out-of-sample period.