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DTSTART;TZID=UTC:20240304T130000
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SUMMARY:Dynamic Factor Models\, Matteo Barigozzi (Università di Bologna)
DESCRIPTION:  \n\n\n\n  \n  \nSCHEDULE\n  \nMonday\n  \n4th March 2024 \n11th March 2024\n  \nFrom 13:00 to 16:45\n  \nRoom 2033\n\n\n  \nThursday\n  \n7th March 2024\n  \nFrom 13:00 to 16:15\n  \nRoom 2033\n\n\n\nAims and objectives\nThe aim of this course is to provide an introduction to factor models in time series analysis by teaching students the basic theoretical foundations and by illustrating  them some applications to macroeconomics and finance. \nIn the last years large datasets have become increasingly available to researchers and practitioners in many disciplines. In particular\, during this big data revolution the analysis of high-dimensional time series has become one of the most active subjects of modern statistical methodology with applications in the most different areas of science including finance\, econometrics\, meteorology\, genomics\, chemometrics\, complex physics simulations\, biological and environmental research. Although the value of information is unquestionable\, the possibility of extracting meaningful and useful information out of this large amount of data is also of great importance. To achieve such dimension reduction\, several new analytical and computational techniques have been developed under the name of machine learning methods. Among these factor models not only are one of the pioneering methods in the field of unsupervised learning (dating back to Spearman\, 1904)\, but up to these days have also been one of the most popular and most employed ones. \nWe start by discussing principal component analysis as a useful dimension reduction technique for large panels of time series. This is the most simple example of factor model (the static model) which we then generalize to include all temporal relations among the considered variables (the dynamic model). We then focus on the case in which the dynamic model can be re–written as a state space model and we present its estimation via Kalman filter and the Expectation Maximization algorithm. We then consider application of these models in two fields. First\, in macroeconometrics for building indicators of the business cycle\, for nowcasting\, and for policy analysis problems. Related to these we briefly discuss how to deal with the issues of non–fundamentalness and cointegration. Second\, in financial econometrics for volatility modelling and forecasting. Related to these we briefly discuss the complementarity of factor and network models and the issue of conditional heteroskedasticity. Real–data applications taken from existing papers are discussed during the lectures. Matlab or R code will be provided. \n\n
URL:https://crest.science/event/dynamic-factor-models-matteo-barigozzi-universita-di-bologna/
LOCATION:2033
CATEGORIES:Doctoral Courses,Finance
ORGANIZER;CN="Jean-Michel%20Zakoian":MAILTO:zakoian@ensae.fr
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DTSTART;TZID=Europe/Helsinki:20240307T100000
DTEND;TZID=Europe/Helsinki:20240307T230000
DTSTAMP:20260711T032731
CREATED:20240214T072523Z
LAST-MODIFIED:20240219T140803Z
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SUMMARY:Davide LA VECCHIA (University of Geneva) "GLAMLE: INFERENCE FOR MULTIVIEW NETWORK DATA IN THE PRESENCE OF LATENT VARIABLES\, WITH APPLICATION TO COMMODITIES TRADING"
DESCRIPTION:Finance & Financial Econometrics : \nTime: 10.00 am\nDate: 07th of March 2023\nRoom 3001 \nDavide LA VECCHIA (University of Geneva) “GLAMLE: INFERENCE FOR MULTIVIEW NETWORK DATA IN THE PRESENCE OF LATENT VARIABLES\, WITH APPLICATION TO COMMODITIES TRADING” \nAbstract : The statistical analysis of import/export data is helpful to understand the mechanism that determines exchanges in an economic network. The probability of having a commercial relationship between two countries often depends on some unobservable (or not easy-to-measure) factors\, like socio-economical conditions\, political views\, level of the infrastructures. To conduct inference on this type of data\, we introduce a novel class of latent variable models for multiview networks\, where a multivariate latent Gaussian variable determines the probabilistic behavior of the edges. We label our model the Graph Generalized Linear Latent Variable Model (GGLLVM) and we base our inference on the maximization of the Laplace-approximated likelihood. We call the resulting M-estimator the Graph Laplace-Approximated Maximum Likelihood Estimator (GLAMLE) and we study its statistical properties. Numerical experiments on simulated networks illustrate that the GLAMLE yields fast and accurate inference. A real data application to commodities trading in Central Europe countries unveils the import/export propensity that each node of the network has toward other nodes\, along with additional information specific to each traded commodity.\nJoint work : C.Jiang and R.Rastelli.\n \nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/davide-la-vecchia-university-of-geneva-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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DTSTART;TZID=Europe/Helsinki:20240307T110000
DTEND;TZID=Europe/Helsinki:20240307T120000
DTSTAMP:20260711T032731
CREATED:20240214T072950Z
LAST-MODIFIED:20240214T072950Z
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SUMMARY:Jeroen ROMBOUTS (ESSEC) - "Modeling Higher Moments and Risk Premiums for S&P 500 Returns"
DESCRIPTION:Finance & Financial Econometrics : \nTime: 11.00 am\nDate: 07th of March 2023\nRoom 31001 \nJeroen ROMBOUTS (ESSEC) – “Modeling Higher Moments and Risk Premiums for S&P 500 Returns” \nAbstract : Using joint estimation on a large sample of index option prices and the underlying returns\, we study how multifactor models capture time-series and cross-sectional patterns in option prices through improved modeling of the dynamics of the first four moments of the return distribution. Including a second and especially a third stochastic volatility factor greatly improves option fit\, and the resulting time series of skewness and kurtosis better match non-parametric benchmarks. The third volatility factor is critical in generating larger and more variable skewness and kurtosis risk premiums. Return jumps provide more modest improvements in option fit and a higher equity risk premium\, but their impact on higher moment risk premiums is small. All models we investigate struggle to match the unconditional term structure of risk-neutral skewness and kurtosis. \nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST \n
URL:https://crest.science/event/jeroen-rombouts-essec-modeling-higher-moments-and-risk-premiums-for-sp-500-returns/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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