1,720,999 research outputs found
Stochastic Volatility Models: A Survey with Applications to Option Pricing and Value at Risk
No full textThis chapter presents an introduction to the current literature on stochastic volatility models. For these models the volatility depends on some unobserved components or a latent structure. Given the time-varying volatility exhibited by most financial data, in the last two decades there has been a growing interest in time series models of changing variance and the literature on stochastic volatility models has expanded greatly. Clearly, this chapter cannot be exhaustive, however we discuss some of the most important ideas, focusing on the simplest forms of the techniques and models used in the literature. The chapter is organised as follows. Section 8.1 considers some motivations for stochastic volatility models: empirical stylised facts, pricing of contingent assets and risk evaluation. While Section 8.2 presents models of changing volatility, Section 8.3 focuses on stochastic volatility models and distinguishes between models with continuous and discrete volatility, the latter depending on a hidden Markov chain. Section 8.4 is devoted to the estimation problem which is still an open question, then a wide range of possibility is given. Sections 8.5 and 8.6 introduce some extensions and multivariate models. Finally, in Section 8.7 an estimation program is presented and some possible applications to option pricing and risk evaluation are discussed. Readers interested in the practical utilisation of stochastic volatility models and in the applications can skip Section 8.4.3 without hindering comprehension
A scoring rule for factor and autoregressive models under misspecification
No full textFactor models (FM) are now widely used for forecasting with large set of time series. Another class of models, which can be easily estimated and used in a large dimensional setting, is multivariate autoregressive models (MAR), where independent autoregressive processes are assumed for the series in the panel. When applied to big data, the estimation, model selection and combination of both models can be time consuming. We assume both FM and MAR models are misspecified and provide a scoring rule which can be evaluated on an initial training sample to either select or combine the models in forecasting exercises on the whole sample. Some numerical illustrations are provided both on simulated data and on well known large economic datasets. The empirical results show that the frequency of the true positive signals is larger when FM and MAR forecasting performances differ substantially and it decreases as the horizon increases
Sovereign Risk and Contagion Effects in the Eurozone: A Bayesian Stochastic Correlation Model
No full textThis research proposes a Bayesian multivariate stochastic volatility model to analyze the dynamics of sovereign risk in eurozone CDS markets during the recent financial crisis. We follow a MCMC approach to parameters and latent variables estimation and provide evidence of significant volatility shifts in asset returns, strong simultaneous increases in cross-market correlations, as well as sharp declines in correlations patterns. Overall, these findings are highly consistent with various empirical characterizations of contagion put forward in the literature, allowing us to conclude that the recent financial crisis generated severe contagion effects in sovereign debt markets of eurozone
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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