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on Market Microstructure |
| By: | Alessandro Innocenti; Pier Malpenga; Lorenzo Menconi; Alessandro Santoni |
| Abstract: | The paper analyses a case study of time lag in processing market-sensitive information with intraday data. On February 2011, the Italian Parliament approved the so called Milleproroghe decree issued by the Government which included, among others, a new important rule for banks transforming the deferred tax assets into tax credits. Although information on the approval of the law had been available since February 8, on February 15 the market took twelve minutes to react to the news and almost an hour to fully absorb it. This delay created significant arbitrage opportunities that can be explained with traders’ inability to process immediately technical and complex matters. Failure to comply with these cognitive limitations prevents traders from incorporating promptly new information in market prices. |
| Keywords: | financial trading, arbitrage, information processing, cognitive limitations. |
| JEL: | F36 G14 G15 |
| Date: | 2011–12 |
| URL: | http://d.repec.org/n?u=RePEc:usi:depfid:0811&r=mst |
| By: | Ulrich Horst; Michael Kupper; Andrea Macrina; Christoph Mainberger |
| Abstract: | We consider a class of generalized capital asset pricing models in continuous time with a finite number of agents and tradable securities. The securities may not be sufficient to span all sources of uncertainty. If the agents have exponential utility functions and the individual endowments are spanned by the securities, an equilibrium exists and the agents' optimal trading strategies are constant. Affine processes, and the theory of information-based asset pricing are used to model the endogenous asset price dynamics and the terminal payoff. The derived semi-explicit pricing formulae are applied to numerically analyze the impact of the agents' risk aversion on the implied volatility of simultaneously-traded European-style options. |
| Date: | 2012–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1201.1840&r=mst |
| By: | Wolfgang Karl Härdle; Ostap Okhrin; Weining Wang |
| Abstract: | Understanding the dynamics of high dimensional non-normal dependency structure is a challenging task. This research aims at attacking this problem by building up a hidden Markov model (HMM) for Hierarchical Archimedean Copulae (HAC), where the HAC represent a wide class of models for high dimensional dependency, and HMM is a statistical technique to describe time varying dynamics. HMM applied to HAC provide flexible modeling for high dimensional non Gaussian time series. Consistency results for both parameters and HAC structures are established in an HMM framework. The model is calibrated to exchange rate data with a VaR application, where the model’s performance is compared with other dynamic models, and in the second application we simulate rainfall process. |
| Keywords: | Hidden Markov model, Hierarchical Archimedean Copulae, Multivariate Distribution |
| JEL: | C13 C14 |
| Date: | 2012–01 |
| URL: | http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2012-001&r=mst |