A new approach is presented to describe the change in the statistics of the log return distribution of financial data as a function of the timescale. To this purpose a measure is introduced, which quantifies the distance of a considered distribution to a reference distribution. The existence of a small timescale regime is demonstrated, which exhibits different properties compared to the normal timescale regime for timescales larger than one minute. This regime seems to be universal for individual stocks. It is shown that the existence of this small timescale regime is not dependent on the special choice of the distance measure or the reference distribution. These findings have important implications for risk analysis, in particular for the ...
Extreme Value Theory (EVT) methods are used to investigate the asymptotic distribution of the lower ...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
Financial time series analysis is a highly empirical discipline concerned with the evolution of the...
A stochastic analysis of financial data is presented. In particular we investigate how the statistic...
We discuss recent results concerning statistical regularities in the return intervals of volatility ...
Most of the papers that study the distributional and fractal properties of financial instruments foc...
The extreme event statistics plays a very important role in the theory and practice of time series a...
Abstract: For any investor on stock market it is very important to predict possible loss, depending ...
The statistical properties of the increments x(t+T) - x(t) of a financial time series depend on the ...
The necessity of more trustworthy methods for measuring the risk (volatility) of financial assets has...
This thesis will first criticize standard financial theory. The focus will be on return distribution...
In this paper we study the possible microscopic origin of heavy-tailed probability density distribut...
Being able to quantify the probability of large price changes in stock markets is of crucial importa...
Extreme Value Theory (EVT) methods are used to investigate the asymptotic distribution of the lower ...
The necessity of more trustworthy methods for measuring the risk (volatility) of financial assets ha...
Extreme Value Theory (EVT) methods are used to investigate the asymptotic distribution of the lower ...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
Financial time series analysis is a highly empirical discipline concerned with the evolution of the...
A stochastic analysis of financial data is presented. In particular we investigate how the statistic...
We discuss recent results concerning statistical regularities in the return intervals of volatility ...
Most of the papers that study the distributional and fractal properties of financial instruments foc...
The extreme event statistics plays a very important role in the theory and practice of time series a...
Abstract: For any investor on stock market it is very important to predict possible loss, depending ...
The statistical properties of the increments x(t+T) - x(t) of a financial time series depend on the ...
The necessity of more trustworthy methods for measuring the risk (volatility) of financial assets has...
This thesis will first criticize standard financial theory. The focus will be on return distribution...
In this paper we study the possible microscopic origin of heavy-tailed probability density distribut...
Being able to quantify the probability of large price changes in stock markets is of crucial importa...
Extreme Value Theory (EVT) methods are used to investigate the asymptotic distribution of the lower ...
The necessity of more trustworthy methods for measuring the risk (volatility) of financial assets ha...
Extreme Value Theory (EVT) methods are used to investigate the asymptotic distribution of the lower ...
Modeling the dependence between consecutive observations in a time series plays a crucial role in ri...
Financial time series analysis is a highly empirical discipline concerned with the evolution of the...