14 research outputs found
Temporal structure and gain/loss asymmetry for real and artificial stock indices
We demonstrate that the gain/loss asymmetry observed for stock indices
vanishes if the temporal dependence structure is destroyed by scrambling the
time series. We also show that an artificial index constructed by a simple
average of a number of individual stocks display gain/loss asymmetry - this
allows us to explicitly analyze the dependence between the index constituents.
We consider mutual information and correlation based measures and show that the
stock returns indeed have a higher degree of dependence in times of market
downturns than upturns
A multiscale view on inverse statistics and gain/loss asymmetry in financial time series
Researchers have studied the first passage time of financial time series and
observed that the smallest time interval needed for a stock index to move a
given distance is typically shorter for negative than for positive price
movements. The same is not observed for the index constituents, the individual
stocks. We use the discrete wavelet transform to illustrate that this is a long
rather than short time scale phenomenon -- if enough low frequency content of
the price process is removed, the asymmetry disappears. We also propose a new
model, which explain the asymmetry by prolonged, correlated down movements of
individual stocks
Gain/loss asymmetry in time series of individual stock prices and its relationship to the leverage effect
Previous research has shown that for stock indices, the most likely time until a return of a particular size has been observed is longer for gains than for losses. We establish that this so-called gain/loss asymmetry is present also for individual stocks and show that the phenomenon is closely linked to the well-known leverage effect -- in the EGARCH model and a modified retarded volatility model, the same parameter that governs the magnitude of the leverage effect also governs the gain/loss asymmetry.
A multiscale view on inverse statistics and gain/loss asymmetry in financial time series
Temporal structure and gain/loss asymmetry for real and artificial stock indices
We demonstrate that the gain/loss asymmetry observed for stock indices vanishes if the temporal dependence structure is destroyed by scrambling the time series. We also show that an artificial index constructed by a simple average of a number of individual stocks display gain/loss asymmetry - this allows us to explicitly analyze the dependence between the index constituents. We consider mutual information and correlation based measures and show that the stock returns indeed have a higher degree of dependence in times of market downturns than upturns.