arXiv subjects

Jeffrey Todd Lins

Publications and source records attributed to Jeffrey Todd Lins.

4 recordsLinked to original sources

arXiv2009

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.

q-fin.ST↗

arXiv2009

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.

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arXiv2008

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.

q-fin.ST↗

arXiv2008

Value-at-Risk Computation by Fourier Inversion with Explicit Error Bounds

The value-at-risk of a delta-gamma approximated derivatives portfolio can be computed by numerical integration of the characteristic function. However, while the choice of parameters in any numerical integration scheme is paramount, in practice it often relies on ad hoc procedures of trial and error. For normal and multivariate $t$-distributed risk factors, we show how to calculate the necessary parameters for one particular integration scheme as a function of the data (the distribution of risk factors, and delta and gamma) \emph{in order to satisfy a given error tolerance}. This allows for implementation in a fully automated risk management system. We also demonstrate in simulations that the method is significantly faster than the Monte Carlo method, for a given error tolerance.

stat.AP↗