References of "Deaconu, Madalina"
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See detailRegime switching model for financial data: empirical risk analysis
Khaled, Salhi; Deaconu, Madalina; Lejay, Antoine et al

in Physica A: Statistical Mechanics and its Applications (2016), 461

This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes ... [more ▼]

This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes volatility clustering into account. In the first stage, HMM is used to classify data in crisis and steady periods, while in the second stage, EVT is applied to the previously classified data to rub out the delay between regime switching and their detection. This new model is applied to prices of numerous stocks exchanged on NYSE Euronext Paris over the period 2001–2011. We focus on daily returns for which calibration has to be done on a small dataset. The relative performance of the regime switching model is benchmarked against other well-known modeling techniques, such as stable, power laws and GARCH models. The empirical results show that the regime switching model increases predictive performance of financial forecasting according to the number of violations and tail-loss tests. This suggests that the regime switching model is a robust forecasting variant of power laws model while remaining practical to implement the VaR measurement. [less ▲]

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See detailAn empirical analysis of heavy-tails behavior of financial data: the case for power laws
Champagnat, Nicolas; Deaconu, Madalina; Lejay, Antoine et al

Report (2013)

This work aims at underlying the importance of a correct modelling of the heavy-tail behavior of extreme values of financial data for an accurate risk estimation. Many financial models assume that prices ... [more ▼]

This work aims at underlying the importance of a correct modelling of the heavy-tail behavior of extreme values of financial data for an accurate risk estimation. Many financial models assume that prices follow normal distributions. This is not true for real market data, as stock (log-)returns show heavy-tails. In order to overcome this, price variations can be modeled using stable distribution, but then, as shown in this study, we observe that it over-estimates the Value-at-Risk. To overcome these empirical inconsistencies for normal or stable distributions, we analyze the tail behavior of price variations and show further evidence that power-law distributions are to be considered in risk models. Indeed, the efficiency of power-law risk models is proved by comprehensive backtesting experiments on the Value-at-Risk conducted on NYSE Euronext Paris stocks over the period 2001-2011. [less ▲]

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