Using a recently introduced method to quantify the time varying lead-lag dependencies between pairs of economic time series (the thermal optimal path method), we test two fundamental tenets of the theory of fixed income: (i) the stock market variations and the yield changes should be...

We review estimation methods of the tail dependence coefficient (TDC), simulating their finite-sample performance. With our chosen semi-parametric and non-parametric estimators, we estimate TDCs of major U.S. stocks. We have three aims. The first is to establish the “stylized facts” about...

In the paper we test for the different reactions of stock markets to the current financial crisis. We focus on Central European stock markets, namely the Czech, Polish and Hungarian ones, and compare them to the German and U.S. benchmark stock markets. Using wavelet analysis, we decompose a time...

The question of whether empirical models are able to forecast the equity premium more accurately than the simple historical mean is intensively debated in the financial literature. The low prediction power is disappointing, even when using nonparametric models that make use of typical predictor...

This essay explores various components of a measure of pretax U.S. family income after adjusting for the size of the family in the U.S. in 1975 and 2000. Using data from the Panel Study on Income Dynamics, an important stylized fact is revealed: The rising inequality of property incomes,...

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning...

In this paper, an attempt is made to examine the relationship between inflation and stock returns in India using spectral and time-frequency methods. Scale specific relation between inflation and stock returns is unraveled, allowing us to capture the relationship at varying investment horizons....

We present a framework for modeling and estimating dynamics of variance and skewness from time-series data using a maximum likelihood approach assuming that the errors from the mean have a non-central conditional t distribution. We parameterize conditional variance and conditional skewness in an...

Subordination is an often used stochastic process in modeling asset prices. Subordinated Levy price processes and local volatility price processes are now the main tools in modern dynamic asset pricing theory. In this paper, we introduce the theory of multiple internally embedded financial...