1,721,169 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
Identifying Business Cycle Turning Points with Sequential Monte Carlo
No full textDiscussion Paper, University of Brescia
Bayesian Estimation of Stochastic-transition Markov-switching Models for Business Cycle Analysis.
No full textStochastic Optimisation for Allocation Problem with Shortfall Risk Constraints
No full textOne of the crucial aspects in asset allocation problems is the assumption concerning the probability distribution of asset returns. Financial managers generally suppose normal distribution, even if extreme realizations usually have an higher frequency than in the Gaussian case. The aim of this paper is to propose a general Monte Carlo simulation approach able to solve an asset allocation problem with shortfall constraint, and to evaluate the exact portfolio risk-level when managers assume a misspecified return behaviour. We assume that returns are generated by a multivariate skewed Student-t distribution where each marginal
can have different degrees of freedom. The stochastic optimization allows us to value the effective risk for managers. In the empirical application we consider a symmetric and heterogeneous case, and interestingly note that a multivariate Student-t with heterogeneous marginal distributions produces in the optimization problem a shortfall probability and a shortfall return level that can be adequately approximated by assuming a multivariate Student-t with common degrees of freedom. Thus, the proposed simulation-based approach could be an important instrument for investors who require a qualitative assessment of the reliability
and sensitivity of their investment strategies in the case their models could be potentially misspecified
Multivariate Markov Switching Dynamic Conditional Correlation GARCH representations for contagion analysis
No full textThis paper provides an extension of the Dynamic Conditional Correlation model of Engle (2002) by allowing both the unconditional correlation and the parameters to be driven by an unobservable Markov chain. We provide the estimation algorithm and perform an empirical analysis of the contagion phenomenon in which our model is compared to the traditional CCC and DCC representations
Market Linkages, Variance Spillover and Correlation Stability: Empirical Evidences of Financial Contagion
No full textTo model the contemporaneous relationships among Asian and American stock markets, a simultaneous equation system with GARCH errors is introduced. In the estimated residuals,
the correlation matrix is analyzed over rolling windows and using a correlation matrix distance, which allows a graphical analysis and the development of a statistical test of correlation movements. Furthermore, a methodology that can be used to identify turmoil
periods on a data-driven basis is presented. The previous results are applied in the analysis of the contagion issue between Asian and American stock markets. The results show some evidence of contagion, and the proposed statistics identify, on a data-driven basis, turmoil periods consistent with the ones currently assumed in the literature
Multivariate radial symmetry of copula functions: Finite sample comparison in the i.i.d case
No full textGiven a d-dimensional random vector X = (X-1, ..., X-d), if the standard uniform vector U obtained by the component-wise probability integral transform (PIT) of X has the same distribution of its point reflection through the center of the unit hypercube, then Xis said to have copula radial symmetry. We generalize to higher dimensions the bivariate test introduced in [11], using three different possibilities for estimating copula derivatives under the null. In a comprehensive simulation study, we assess the finite-sample properties of the resulting tests, comparing them with the finite-sample performance of the multivariate competitors introduced in [17] and [1]
A meta-measure of performance related to both investors and investments characteristics
No full textWe introduce hereafter a new flexible meta-measurement of portfolio performance, called the Generalized Utility-based N-moment measure, relying both on a characterization of the whole return distribution and on the set of preferences of the investor, which is adapted to analyze the performance of hedge funds. It could also serve as the basis of a Fraudulent Behavior Index aiming to detect fraudulent funds
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