1,720,966 research outputs found
The Effects of Defensive Medicine in Physician–Patient Dynamics: An Agent-Based Approach
No full textWe analyze different scenarios of defensive medicine in a novel framework based on game theory and network analysis, where links in the network represent healing relationships between a physician and a patient. The physician should choose between providing the optimal treatment or an inferior one, which can amount to practicing defensive medicine. The patient should choose whether to litigate or not if an adverse event occurs. A major result of such analysis is that the steady state does not depend on the litigiousness of the initial system or the initial distribution of strategies among physicians or the distribution of patients over physicians. Moreover, reaching a virtuous steady state or an entirely defensive one appears to be independent of the fact that patients take into account the quality of treatments directly or they rely merely on popularity when choosing their physicians
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. © 2010 The American Physical Society
Stochastic Volatility with Heterogeneous Time Scales
No full textAgents' heterogeneity is recognized as a driver mechanism for the persistence
of financial volatility. We focus on the multiplicity of investment strategies'
horizons, we embed this concept in a continuous time stochastic volatility
framework and prove that a parsimonious, two-scale version effectively captures
the long memory as measured from the real data. Since estimating parameters in
a stochastic volatility model is challenging, we introduce a robust methodology
based on the Generalized Method of Moments supported by a heuristic selection
of the orthogonal conditions. In addition to the volatility clustering, the
estimated model also captures other relevant stylized facts, emerging as a
minimal but realistic and complete framework for modelling financial time
series
Minimal model of financial stylized facts
No full textIn this work we propose a statistical characterization of a linear stochastic volatility model featuring inverse-gamma stationary distribution for the instantaneous volatility. We detail the derivation of the moments of the return distribution, revealing the role of the inverse-gamma law in the emergence of fat tails and of the relevant correlation functions. We also propose a systematic methodology for estimating the parameters and we describe the empirical analysis of the Standard & Poor's 500 index daily returns, confirming the ability of the model to capture many of the established stylized facts as well as the scaling properties of empirical distributions over different time horizons
Accounting for risk of non linear portfolios
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
Bayesian Value-at-Risk with product partition models
No full textIn this paper we propose a novel Bayesian methodology for Value-at-Risk computation based on parametric Product Partition Models. Value-at-Risk is a standard tool for measuring and controlling the market risk of an asset or portfolio, and is also required for regulatory purposes. Its popularity is partly due to the fact that it is an easily understood measure of risk. The use of Product Partition Models allows us to remain in a Normal setting even in the presence of outlying points, and to obtain a closed-form expression for Value-at-Risk computation. We present and compare two different scenarios: a product partition structure on the vector of means and a product partition structure on the vector of variances. We apply our methodology to an Italian stock market data set from Mib30. The numerical results clearly show that Product Partition Models can be successfully exploited in order to quantify market risk exposure. The obtained Value-at-Risk estimates are in full agreement with Maximum Likelihood approaches, but our methodology provides richer information about the clustering structure of the data and the presence of outlying points
Systemic risk from investment similarities
Full text linkNetwork theory proved recently to be useful in the quantification of many properties of financial systems. The analysis of the structure of investment portfolios is a major application since their eventual correlation and overlap impact the actual risk by individual investors. We investigate the bipartite network of US mutual fund portfolios and their assets. We follow its evolution during the Global Financial Crisis and study the diversification, as understood in modern portfolio theory, and the similarity of the investments of different funds. We show that, on average, portfolios have become more diversified and less similar during the crisis. However, we also find that large overlap is far more likely than expected from benchmark models of random allocation of investments. This indicates the existence of strong correlations between fund investment strategies. We exploit a deliberately simplified model of shock propagation to identify a systemic risk component stemming from the similarity of portfolios. The network is still partially vulnerable after the crisis because of this effect, despite the increase in the diversification of multi asset portfolios. Diversification and similarity should be taken into account jointly to properly assess systemic risk
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
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