C58 - Financial EconometricsReturn

This paper focuses on using survival analysis models in the area of credit risk and on the modelling of the probability of default (i.e. a situation where the debtor is unwilling or unable to repay the loan in time) in particular. Most of the relevant scholarly literature argues that the survival models produce similar results to the commonly used logistic regression models for the development or testing of samples. However, this paper challenges the standard performance criteria measuring precision and performs a comparison using a new prediction-based method. This method gives more weight to the predictive power of the models measured on an ex-ante validation sample rather than the standard precision of the random testing sample. This new scheme shows that the predictive power of the survival model outperforms the logistic regression model in terms of Gini and lift coefficients. This finding opens up the prospect for the survival models to be further studied and considered as relevant alternatives in financial modelling.

By modifying and generalizing the wavelet-based approach of approximately simulating univariate long-memory processes that is available in the literature, we propose a methodology for simulating a bivariate stationary process, whose components exhibit different relationships at different scales. We derive the formulas for the autocovariance and cross-covariance sequences of the simulated bivariate process. We provide a setting for the parameters of the simulation which might generate a bivariate time series resembling that of stock log returns. Using this setting, we study the properties of our methodology via Monte Carlo simulation.

Volatility of the financial time series belongs to the crucial estimated parameters in finance (e.g. in risk management, derivative pricing). It is well known, that volatility varies in time, so that new approaches of volatility modeling have appeared. In this paper two models of the conditional heteroskedasticity - fractionally integrated GARCH (FIGARCH) and EWMA are presented. These models are illustrated on the daily historical returns of stock index PX and index BUX. Standard tests of normality, autocorrelation and conditional heteroskedasticity are applied to these log-return time series and before estimating the models, which confirm a usability of the conditional heteroskedasticity models. Empirical results of the Rescale Range analysis (R/S) indicate a long memory in the volatility process of PX index and the first 40 autocorrelations of the square log-returns show their hyperbolic decay. The volatility models are estimated by quasi-maximum likelihood method with Student's t-distribution and used to the calculation of the 1-day 95% and 99% Value at Risk values. Finally, the validity of the models is verified by Kupiec's test, TUFF and Christoffersen's test. These tests demonstrate, that the FIGARCH model is a suitable alternative to the EWMA model in the Value at Risk calculation.

The article deals with a typical phenomenon of financial time series - volatility. These time series usually embody intermittent periods of relative "calm" and quite high variability. A volatility modelling of time series is made with the help of special econometric volatility models which characterize the so-called conditional heteroskedasticity. The goal of this paper is to choose a suitable volatility model for Prague PX Index and London FTSE 100. The path to the aim is via a stationarity analysis of tracked time series of closing values of the mentioned indexes, conditional heteroskedasticity and autocorrelation tests and an identification of probability distribution of the studied quantity. A profiling of asymmetric effects is also very important because they determine the linear or nonlinear character of the resulting model.