1,720,977 research outputs found
Stochastic volatility with heterogeneous time scales
Full text linkIn this work, the model for the statistical description of financial stylized facts proposed in Delpini and Bormetti (2011) is amended from the unrealistically fast decay of the volatility autocorrelation. This is achieved by introducing an extra stochastic factor that drives the volatility. With respect to previous approaches and analyses of continuous-time stochastic volatility models, we believe that the estimation procedure proposed here represents a further improvement and fulfills the desirable requirements of statistical soundness. At the same time, it also allows us to focus on those facts which are established as relevant for the description of financial data. In particular, we pursue an heuristic approach to optimization that reduces the dimensionality of the parameter space and retains only the ingredients needed to capture the aforementioned empirical evidence
Exact moment scaling from multiplicative noise
No full textFor a general class of diffusion processes with multiplicative noise, describing a variety of physical as well
as financial phenomena, mostly typical of complex systems, we obtain the analytical solution for the moments
at all times. We allow for a nontrivial time dependence of the microscopic dynamics and we analytically
characterize the process evolution, possibly toward a stationary state, and the direct relationship existing
between the drift and diffusion coefficients and the time scaling of the moments
Accounting for risk of non linear portfolios : A novel Fourier approach
No full textThe presence of non linear instruments is responsible for the emergence of non Gaussian features in the price changes distribution of realistic portfolios, even for Normally distributed risk factors. This is especially true for the benchmark Delta Gamma Normal model, which in general exhibits exponentially damped power law tails. We show how the knowledge of the model characteristic function leads to Fourier representations for two standard risk measures, the Value at Risk and the Expected Shortfall, and for their sensitivities with respect to the model parameters. We detail the numerical implementation of our formulae and we emphasize the reliability and efficiency of our results in comparison with Monte Carlo simulation. © 2010 EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg
Score-driven exponential random graphs: A new class of time-varying parameter models for temporal networks
No full text: Motivated by the increasing abundance of data describing real-world networks that exhibit dynamical features, we propose an extension of the exponential random graph models (ERGMs) that accommodates the time variation of its parameters. Inspired by the fast-growing literature on dynamic conditional score models, each parameter evolves according to an updating rule driven by the score of the ERGM distribution. We demonstrate the flexibility of score-driven ERGMs (SD-ERGMs) as data-generating processes and filters and show the advantages of the dynamic version over the static one. We discuss two applications to temporal networks from financial and political systems. First, we consider the prediction of future links in the Italian interbank credit network. Second, we show that the SD-ERGM allows discriminating between static or time-varying parameters when used to model the U.S. Congress co-voting network dynamics
Bayesian Analysis of Value-at-Risk with Product Partition Models
No full textWe consider Bayesian estimation of Value-at-Risk (VaR) using parametric Product Partition Models (PPM). VaR is a standard tool to measure and control the market risk of an asset or a
portfolio, and it is also required for regulatory purposes. We use PPM to provide robustly Bayesian
estimators of VaR, remaining in a Normal setting, even in presence of outlying points. We consider two
different scenarios: a product partition structure on the vector of means and a product partition structure
on the vector of variances. In both frameworks we obtain a closed-form expression for VaR. The results
are illustrated with an application to a set of shares from the Italian stock market. The methodology and
the obtained results are described in details in Bormetti et al. (2009)
A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics
No full textThe analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-sec NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons
The adaptive nature of liquidity taking in limit order books
No full textIn financial markets, the order flow, defined as the process assuming value one for buy market orders and minus one for sell market orders, displays a very slowly decaying autocorrelation function. Since orders impact prices, reconciling the persistence of the order flow with market efficiency is a subtle issue. A possible solution is provided by asymmetric liquidity, which states that the impact of a buy or sell order is inversely related to the probability of its occurrence. We empirically find that when the order flow predictability increases in one direction, the liquidity in the opposite side decreases, but the probability that a trade moves the price decreases significantly. While the last mechanism is able to counterbalance the persistence of order flow and restore efficiency and diffusivity, the first acts in the opposite direction. We introduce a statistical order book model where the persistence of the order flow is mitigated by adjusting the market order volume to the predictability of the order flow. The model reproduces the diffusive behaviour of prices at all time scales without fine-tuning the values of parameters, as well as the behaviour of most order book quantities as a function of the local predictability of the order flow. © 2014 IOP Publishing Ltd
A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics
Full text linkThe analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-second NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons
- …
