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- PublicationMétadonnées seulementMacroeconomic stress-testing of mortgage default rate using a vector error correction model and entropy pooling(2016)
; ;Guerrouaz, AnasRey, JeanneWe propose a methodology to perform macroeconomic stress-testing on the probability of default of a given borrowers’ population (i.e., aggregate probability of default) through simulation from a vector error correction model and entropy pooling (Meucci, 2008).
- PublicationMétadonnées seulement
- PublicationMétadonnées seulementDEoptim: An R package for global optimization by Differential Evolution(2011)
;Mullen, Katharine ; ;Gil, David L. ;Windover, DonaldCline, JamesThis article describes the R package DEoptim, which implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector. The implementation of Differential Evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated by case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-Switching Generalized AutoRegressive Conditional Heteroskedasticity model for the returns of the Swiss Market Index.
- PublicationMétadonnées seulementQuantitative portfolio construction and systematic trading strategies using factor entropy pooling(2014)
;Meucci, Attilio ;Colasante, MarcelloThe Entropy Pooling approach is a versatile theoretical framework to process market views and generalized stress-tests into an optimal "posterior" market distribution, which is then used for risk management and portfolio management. Entropy Pooling can be implemented non-parametrically or parametrically. The non-parametric implementation with historical scenarios is more suitable for risk management applications. Here introduce the parametric implementation of Entropy Pooling under a factor structure, which we name Factor Entropy Pooling. The factor structure reduces the dimension of the problem and linearizes the parameter space, allowing for fast computation of the posterior market distribution. We apply Factor Entropy Pooling to two portfolio construction problems. First, we use the Factor Entropy Pooling to construct the "implied returns", i.e. a market distribution consistent with a target optimal portfolio, such as maximum diversification/risk parity, or the CAPM equilibrium. Our approach improves on the implied returns a-la-Black-Litterman, and the ensuing distribution can be used as the starting point for further portfolio construction. Second, we use Factor Entropy Pooling to construct and backtest quantitative systematic trading strategies based on ranking views, or "portfolios from sorts". Unlike standard approaches, Factor Entropy Pooling closely ties to the actual empirical data.
- PublicationMétadonnées seulementReturn and risk of pairs trading using a simulation-based Bayesian procedure for predicting stable ratios of stock prices(2016)
; ;Gatarek, Lukasz T. ;Hoogerheide, LennartVan Dijk, HermanWe investigate the direct connection between the uncertainty related to estimated stable ratios of stock prices and risk and return of two pairs trading strategies: a conditional statistical arbitrage method and an implicit arbitrage one. A simulation-based Bayesian procedure is introduced for predicting stable stock price ratios, defined in a cointegration model. Using this class of models and the proposed inferential technique, we are able to connect estimation and model uncertainty with risk and return of stock trading. In terms of methodology, we show the effect that using an encompassing prior, which is shown to be equivalent to a Jeffreys’ prior, has under an orthogonal normalization for the selection of pairs of cointegrated stock prices and further, its effect for the estimation and prediction of the spread between cointegrated stock prices. We distinguish between models with a normal and Student t distribution since the latter typically provides a better description of daily changes of prices on financial markets. As an empirical application, stocks are used that are ingredients of the Dow Jones Composite Average index. The results show that normalization has little effect on the selection of pairs of cointegrated stocks on the basis of Bayes factors. However, the results stress the importance of the orthogonal normalization for the estimation and prediction of the spread—the deviation from the equilibrium relationship—which leads to better results in terms of profit per capital engagement and risk than using a standard linear normalization.
- PublicationAccès libreFinancial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications(Heidelberg: Springer, 2008)This book presents in detail methodologies for the Bayesian estimation of single-regime and regime-switching GARCH models. These models are widespread and essential tools in financial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach offers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The first two chapters introduce the work and give a short overview of the Bayesian paradigm for inference. The next three chapters describe the estimation of the GARCH model with Normal innovations and the linear regression models with conditionally Normal and Student-t-GJR errors. For these models, we compare the Bayesian and Maximum Likelihood approaches based on real financial data. In particular, we document that even for fairly large data sets, the parameter estimates and confidence intervals are different between the methods. Caution is therefore in order when applying asymptotic justifications for this class of models. The sixth chapter presents some financial applications of the Bayesian estimation of GARCH models. We show how agents facing different risk perspectives can select their optimal VaR point estimate and document that the differences between individuals can be substantial in terms of regulatory capital. Finally, the last chapter proposes the estimation of the Markov-switching GJR model. An empirical application documents the in- and out-of-sample superiority of the regime-switching specification compared to single-regime GJR models. We propose a methodology to depict the density of the one-day ahead VaR and document how specific forecasters’ risk perspectives can lead to different conclusions on the forecasting performance of the MS-GJR model.
- PublicationMétadonnées seulementA new bootstrap test for multiple assets joint risk testing(2017-4)
; ;Gatarek, LukaszHoogerheide, Lennart
- PublicationMétadonnées seulementAdMit: Adaptive mixtures of Student-t distributions(2009)
; ;Hoogerheide, LennartVan Dijk, HermanThis short note presents the R package AdMit which provides flexible functions to approximate a certain target distribution and it provides an efficient sample of random draws from it, given only a kernel of the target density function. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. To illustrate the use of the package, we apply the AdMit methodology to a bivariate bimodal distribution. We describe the use of the functions provided by the package and document the ability and relevance of the methodology to reproduce the shape of non-elliptical distributions.
- PublicationMétadonnées seulementGeneralized marginal risk(2011)
;Keel, SimonAn important aspect of portfolio risk management is the analysis of the overall risk with respect to the assets' allocations. Marginal risk is the traditional tool, however, this metric is only meaningful when a position is levered or when the proceeds from the sale of a position are put in the cash account. This paper proposes an extension of the traditional marginal risk approach as a means of overcoming this defficiency. The new concept addresses situations where the change in a position results in changes to other positions as well. An illustration is provided for synthetic and real-world portfolios.
- PublicationMétadonnées seulementParametric stress-testing in non-normal markets via entropy pooling(2015)
;Meucci, AttilioA novel approach for stress-testing (portfolios of) financial assets is presented. The technique extends the parametric Entropy Pooling approach to skewed and thick-tailed markets. The technique rests on a copula-marginal decomposition for the entropy together with several approximation schemes which renders the numerical computations feasible for real-life problems. An illustration with a portfolio of European options is presented.