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We describe a psychophysiological study of the emotion regulation of investment bank traders. Building on work on the role of emotions in financial decision making, we examined the relationship between market conditions, trader experience, and emotion regulation while trading, as indexed by high-frequency heart rate variability (HF HRV). We found a significant inverse relationship between HF HRV and market volatility and a positive relationship between HF HRV and trader experience. We argue that this suggests that emotion regulation may be an important facet of trader expertise, and that learning effects demonstrated in financial markets may include improved emotion regulation as an important component of that learning. Our results also suggest the value of investigating the role of effective emotion regulation in a broader range of financial decision-making contexts.

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 demonstrate that this so-called gain-loss asymmetry 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.

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.

JEL classification: G10 G21 C63 C46 Keywords:Value-at-Risk Delta-gamma approximation Fourier inversion Characteristic function Error boundsThe 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) 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.