Computational and Financial Econometrics (CFE 2014) Computational and Methodological Statistics (ERCIM 2014)

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1 CFE-ERCIM 2014 PROGRAMME AND ABSTRACTS 8th International Conference on Computational and Financial Econometrics (CFE 2014) and 7th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2014) University of Pisa, Italy 6 8 December CMStatistics and CFEnetwork c I

2 CFE-ERCIM 2014 Depósito Legal: AS ISBN: c CMStatistics and CFEnetwork Technical Editors: Angela Blanco-Fernandez, Gil Gonzalez-Rodriguez and George Loizou. All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any other form or by any means without the prior permission from the publisher. II CMStatistics and CFEnetwork c

3 CFE-ERCIM 2014 International Organizing Committee: Alessandra Amendola, Monica Billio, Ana Colubi, Herman van Dijk and Stefan van Aelst. CFE 2014 Co-chairs: Manfred Deistler, Eric Jacquier, Erricos John Kontoghiorghes, Tommaso Proietti and Richard J. Smith. CFE 2014 Programme Committee: Peter Boswijk, Massimiliano Caporin, Cathy Chen, Serge Darolles, Jean-Marie Dufour, Mardi Dungey, Christian Franq, Ghislaine Gayraud, Eric Ghysels, Domenico Giannone, Markus Haas, Andrew Harvey, Alain Hecq, Eric Hillebrand, Michel Juillard, Hannes Leeb, Marco Lippi, Gian Luigi Mazzi, Paul Mizen, Serena Ng, Yasuhiro Omori, Edoardo Otranto, Anne Philippe, D.S.G. Pollock, Arvid Raknerud, Francesco Ravazzolo, Marco Reale, Jeroen V.K. Rombouts, Barbara Rossi, Eduardo Rossi, Berc Rustem, Willi Semmler, Mike K.P. So, Mark Steel, Giuseppe Storti, Rodney Strachan, Genaro Sucarrat, Elias Tzavalis, Jean-Pierre Urbain, Martin Wagner and Jean-Michel Zakoian. ERCIM 2014 Co-chairs: Jae C. Lee, Peter Green, Paul McNicholas, Hans-Georg Muller and Monica Pratesi. ERCIM 2014 Programme Committee: Ana M. Aguilera, Sanjib Basu, Graciela Boente, Peter Buehlmann, Efstathia Bura, Taeryon Choi, Christophe Croux, Maria De Iorio, Michael Falk, Roland Fried, Efstratios Gallopoulos, Xuming He, George Karabatsos, Yongdai Kim, Thomas Kneib, Alois Kneip, Soumendra Lahiri, Jaeyong Lee, Victor Leiva, Antonio Lijoi, Brunero Liseo, Giovanni Marchetti, Geoff McLachlan, Giovanni Montana, Angela Montanari, Domingo Morales, Hannu Oja, Byeong U. Park, Taesung Park, Marco Riani, Judith Rousseau, Peter Rousseeuw, Tamas Rudas, Laura Sangalli, Thomas Scheike, Robert Serfling, Ingrid Van Keilegom and Liming Xiang. Local Organizer: Department of Economics and Management, University of Pisa, Italy. Department of Economics and Statistics, University of Salerno, Italy. CMStatistics and CFEnetwork c III

4 CFE-ERCIM 2014 Dear Friends and Colleagues, We warmly welcome you to Pisa, for the Eighth International Conference on Computational and Financial Econometrics (CFE 2014) and the Seventh International Conference of the ERCIM Working Group on Computational and Methodological Statistics (ERCIM 2014). As many of you know, this annual conference has been established as a leading joint international meeting for the interface of computing, empirical finance, econometrics and statistics. The conference aims at bringing together researchers and practitioners to discuss recent developments in computational methods for economics, finance, and statistics. The CFE-ERCIM 2014 programme consists of 275 sessions, 5 plenary talks and over 1150 presentations. There are over 1250 participants. This is the biggest meeting of the conference series in terms of number of participants and presentations. The growth of the conference in terms of size and quality makes it undoubtedly one of the most important international scientific events in the area. The co-chairs have endeavoured to provide a balanced and stimulating programme that will appeal to the diverse interests of the participants. The international organizing committee hopes that the conference venue will provide the appropriate environment to enhance your contacts and to establish new ones. The conference is a collective effort by many individuals and organizations. The Scientific Programme Committee, the Session Organizers, the local hosting universities and many volunteers have contributed substantially to the organization of the conference. We acknowledge their work and the support of our hosts and sponsors, and particularly University of Pisa, University of Salerno and Queen Mary University of London, UK. Looking forward, the CFE-ERCIM 2015 will be held at the Senate House, University of London and Birkbeck University of London, UK, from Saturday the 12th to Monday the 14th December Tutorials will take place on Friday the 11th of December You are invited and encouraged to actively participate in these events. We wish you a productive, stimulating conference and a memorable stay in Pisa. The CFE-ERCIM 2014 co-chairs and the International Organizing Committee. IV CMStatistics and CFEnetwork c

5 CFE-ERCIM 2014 AIMS AND SCOPE ERCIM Working Group on COMPUTATIONAL AND METHODOLOGICAL STATISTICS The working group (WG) CMStatistics focuses on all computational and methodological aspects of statistics. Of particular interest is research in important statistical applications areas where both computational and/or methodological aspects have a major impact. The aim is threefold: first, to consolidate the research in computational and methodological statistics that is scattered throughout Europe; second, to provide researches with a network from which they can obtain an unrivalled sources of information about the most recent developments in computational and methodological statistics as well as its applications; third, to edit quality publications of high impact and significance in the broad interface of computing, methodological statistics and its applications. The scope of the WG is broad enough to include members in all areas of methododological statistics and those of computing that have an impact on statistical techniques. Applications of statistics in diverse disciplines are strongly represented. These areas include economics, medicine, epidemiology, biology, finance, physics, chemistry, climatology and communication. The range of topics addressed and the depth of coverage establish the WG as an essential research network in the interdisciplinary area of advanced computational and methodological statistics. The WG comprises a number of specialized teams in various research areas of computational and methodological statistics. The teams act autonomously within the framework of the WG in order to promote their own research agenda. Their activities are endorsed by the WG. They submit research proposals, organize sessions, tracks and tutorials during the annual WG meetings and edit journals special issues (currently for the Journal of Computational Statistics & Data Analysis). Specialized teams Currently the ERCIM WG has over 1150 members and the following specialized teams BM: Bayesian Methodology CODA: Complex data structures and Object Data Analysis CPEP: Component-based methods for Predictive and Exploratory Path modeling DMC: Dependence Models and Copulas DOE: Design Of Experiments EF: Econometrics and Finance GCS: General Computational Statistics WG CMStatistics GMS: General Metholological Statistics WG CMStatistics GOF: Goodness-of-Fit and Change-Point Problems HDS: High-Dimensional Statistics ISDA: Imprecision in Statistical Data Analysis LVSEM: Latent Variable and Structural Equation Models MCS: Matrix Computations and Statistics MM: Mixture Models MSW: Multi-Set and multi-way models NPS: Non-Parametric Statistics OHEM: Optimization Heuristics in Estimation and Modelling RACDS: Robust Analysis of Complex Data Sets SAE: Small Area Estimation SAET: Statistical Analysis of Event Times SAS: Statistical Algorithms and Software SEA: Statistics of Extremes and Applications SFD: Statistics for Functional Data SL: Statistical Learning SSEF: Statistical Signal Extraction and Filtering TSMC: Times Series Modelling and Computation You are encouraged to become a member of the WG. For further information please contact the Chairs of the specialized groups (see the WG s web site), or by at info@cmstatistics.org. CMStatistics and CFEnetwork c V

6 CFE-ERCIM 2014 AIMS AND SCOPE CFEnetwork COMPUTATIONAL AND FINANCIAL ECONOMETRICS Computational and Financial Econometrics (CFEnetwork) is an autonomous group linked to CMStatistics. This network will enhance the interface of theoretical and applied econometrics, financial econometrics and computation in order to advance a powerful interdisciplinary research field with immediate applications. The aim is first, to promote research in computational and financial econometrics; second, to provide researchers with a network from which they can obtain an unrivalled sources of information about the most recent developments in computational and financial econometrics as well as its applications; third, to edit quality publications of high impact and significance. Computational and financial econometrics comprise a broad field that has clearly interested a wide variety of researchers in economics, finance, statistics, mathematics and computing. Examples include estimation and inference on econometric models, model selection, panel data, measurement error, Bayesian methods, time series analyses, portfolio allocation, option pricing, quantitative risk management, systemic risk and market microstructure, to name but a few. While such studies are often theoretical, they can also have a strong empirical element and often have a significant computational aspect dealing with issues like highdimensionality and large number of observations. Algorithmic developments are also of interest since existing algorithms often do not utilize the best computational techniques for efficiency, stability, or conditioning. So also are developments of environments for conducting econometrics, which are inherently computer based. Integrated econometrics packages have grown well over the years, but still have much room for development. The CFEnetwork comprises a number of specialized teams in various research areas of computational and financial econometrics. The teams contribute to the activities of the network by organizing sessions, tracks and tutorials during the annual CFEnetwork meetings, editing special issues (currently published under the CSDA Annals of CFE) and submitting research proposals. Specialized teams Currently the CFEnetwork has over 700 members and the following specialized teams AE: Applied Econometrics BE: Bayesian Econometrics BM: Bootstrap Methods CE: Computational Econometrics ET: Econometric Theory FA: Financial Applications FE: Financial Econometrics TSE: Time Series Econometrics You are encouraged to become a member of the CFEnetwork. For further information please see the web site or contact by at info@cfenetwork.org. VI CMStatistics and CFEnetwork c

7 CFE 2014 Saturday, 6th December :00-09:10 Opening CFE-ERCIM 09:10-10:00 Plenary Session A 10:00-10:30 Coffee Break 10:30-11:45 Parallel Sessions C 11:55-12:45 Plenary Session D 12:45-14:35 Lunch Break 14:35-16:15 Parallel Sessions E 16:15-16:45 Coffee Break 16:45-18:50 Parallel Sessions G 20:30-22:15 Reception Sunday, 7th December :45-10:25 Parallel Sessions I 10:25-10:55 Coffee Break 10:55-13:00 Parallel Sessions J 13:00-14:45 Lunch Break 14:45-16:25 Parallel Sessions K 16:25-16:55 Coffee Break 16:55-18:35 Parallel Sessions L 20:30-23:30 Conference Dinner Monday, 8th December :45-10:05 Parallel Sessions N 10:05-10:35 Coffee Break 10:35-12:15 Parallel Sessions P 12:25-13:15 Plenary Session Q 13:15-14:50 Lunch Break 14:50-16:30 Parallel Sessions R 16:30-16:55 Coffee Break 16:55-18:15 Parallel Sessions S 18:25-19:15 Plenary Session T 19:15-19:30 Closing CFE-ERCIM 2014 SCHEDULE ERCIM 2014 Saturday, 6th December :00-09:10 Opening CFE-ERCIM 09:10-10:00 Plenary Session A 10:00-10:30 Coffee Break 10:30-12:35 Parallel Sessions B 12:35-14:35 Lunch Break 14:35-16:15 Parallel Sessions E 16:15-16:45 Coffee Break 16:45-18:00 Parallel Sessions F 18:10-19:00 Plenary Session H 20:30-22:15 Reception Sunday, 7th December :45-10:25 Parallel Sessions I 10:25-10:55 Coffee Break 10:55-13:00 Parallel Sessions J 13:00-14:45 Lunch Break 14:45-16:25 Parallel Sessions K 16:25-16:55 Coffee Break 16:55-19:00 Parallel Sessions M 20:30-23:30 Conference Dinner Monday, 8th December :45-10:05 Parallel Sessions N 10:05-10:35 Coffee Break 10:35-12:15 Parallel Sessions O 12:25-13:15 Plenary Session Q 13:15-14:50 Lunch Break 14:50-16:30 Parallel Sessions R 16:30-16:55 Coffee Break 16:55-18:15 Parallel Sessions S 18:25-19:15 Plenary Session T 19:15-19:30 Closing TUTORIALS, MEETINGS AND SOCIAL EVENTS TUTORIALS The tutorials will take place on Friday the 5th of December 2014 at the central builidng of the Scuola Superiore Sant Anna, Piazza Martiri della Liberta 33, Pisa (see the map at page X). The first one is given by Prof. D. Stephen G. Pollock (Band pass filtering and Wavelets analysis: Tools for the analysis of inhomogeneous time series - Aula Magna) at 9:00-13:30. The second ones are given by Prof. Mike West (Multivariate Time Series: Bayesian Dynamic Modelling & Forecasting - Aula Magna) and Prof. Vincenzo Esposito Vinzi (Component-based path modeling - Aula Magna Storica) at 15:00-19:30. SPECIAL MEETINGS by invitation to group members The CSDA Editorial Board meeting will take place on Friday 5th of December 2014 from 18:00-19:30. The CSDA & CFEnetwork Editorial Boards dinner will take place on Friday 5th of December 2014 from 20:00-22:30. The ERCIM Specialized Teams Co-chairs meeting will take place on Saturday 6th of December 2014, 14:05-14:30, Lecture Room A0. The CFEnetwork Editorial Board meeting will take place on Saturday 6th of December 2014, 19:05-19:25, Lecture Room A0. The IASC Assembly meeting Sunday 7th of December, 13:00-14:30, Lecture Room A0. Open to all IASC members. The COST Action CRONOS meeting will take place on Sunday 7th of December 2014, 13:15-13:45, Lecture Room Sala Convegni. The meeting is by invitation only. SOCIAL EVENTS The coffee breaks will take place at the halls of each floor of the venue (Polo didattico delle Piagge dell Università di Pisa). Welcome Reception, Saturday 6th of December, from 20:30 to 22:15. The Welcome Reception is open to all registrants who had preregistered and accompanying persons who have purchased a reception ticket. It will take place at Leopolda (Stazione Leopolda, Piazza Guerrazzi 2, Pisa, see map at page X). Conference registrants must bring their conference badge and ticket and any accompanying persons should bring their reception tickets in order to attend the reception. Preregistration is required due to health and safety reasons, and limited capacity of the venue. Entrance to the reception venue will be strictly allowed only to those who have a ticket. Conference Dinner, Sunday 7th of December, from 20:30 to 23:30. The conference dinner is optional and registration is required. It will take place at Leopolda (Stazione Leopolda, Piazza Guerrazzi 2, Pisa, see map at page X). Conference registrants and accompanying persons should bring their conference dinner tickets in order to attend the conference dinner. CMStatistics and CFEnetwork c VII

8 CFE-ERCIM 2014 Addresses of venues: Polo didattico delle Piagge dell Università di Pisa, Via Giacomo Matteotti 3, Pisa. Conference Center, Palazzo dei Congressi di Pisa, Via Giacomo Matteotti 1, Pisa. Central builidng of the Scuola Superiore Sant Anna, Piazza Martiri della Liberta 33, Pisa. Registration, exhibitors and networking activities The registration and exhibitors will be located in the ground floor of the Polo didattico delle Piagge. The Lecture Room A0 is available for meetings upon request. Lecture rooms The paper presentations will take place at the Polo didattico delle Piagge dell Università di Pisa (see map in page X). The different rooms are shown in the following floor plans of the venue. We advise that you visit the venue in advance. The opening, keynote and closing talks will take place at the Conference Center, Palazzo dei Congressi di Pisa. The poster sessions will take place at the first floor Hall of the Polo didattico delle Piagge dell Università di Pisa. GROUND FLOOR FIRST FLOOR VIII CMStatistics and CFEnetwork c

9 CFE-ERCIM 2014 SECOND FLOOR Presentation instructions The lecture rooms will be equipped with a laptop and a computer projector. The session chairs should obtain copies of the talks on a USB stick before the session starts (use the lecture room as the meeting place), or obtain the talks by prior to the start of the conference. Presenters must provide to the session chair with the files for the presentation in PDF (Acrobat) or PPT (Powerpoint) format on a USB memory stick. This must be done at least ten minutes before each session. Chairs are requested to keep the sessions on schedule. Papers should be presented in the order they are listed in the programme for the convenience of attendees who may wish to go to other rooms mid-session to hear particular papers. In the case of a presenter not attending, please use the extra time for a break or a discussion so that the remaining papers stay on schedule. The laptops in the lecture rooms should be used for presentations. IT technicians will be available during the conference and should be contacted in case of problems. Posters The poster sessions will take place at the first floor Hall of the Polo didattico delle Piagge dell Università di Pisa. The posters should be displayed only during their assigned session. The authors will be responsible for placing the posters in the poster panel displays and removing them after the session. The maximum size of the poster is A0. Internet Participants from any eduroam-enabled institution should use the Eduroam service in order to obtain access to Internet. For participants without Eduroam access, there will be limited accounts for wireless Internet connection at the main venue of Polo didattico delle Piagge dell Università di Pisa. You will need to have your own laptop in order to connect to the Internet. The username and password for the wireless Internet connection can be obtained by the IT desk which will be located next to the registration desk. Information and messages You may leave messages for each other on the bulletin board by the registration desks. General information about restaurants, useful numbers, etc. can be obtained from the registration desk. CMStatistics and CFEnetwork c IX

10 CFE-ERCIM 2014 Map of the venue and nearby area X CMStatistics and CFEnetwork c

11 CFE-ERCIM 2014 PUBLICATION OUTLETS Journal of Computational Statistics & Data Analysis (CSDA) Selected peer-reviewed papers will be published in special issues, or in regular issues of the Journal of Computational Statistics & Data Analysis. Submissions for the CSDA should contain a strong computational statistics, or data analytic component. Theoretical papers or papers with simulation as the main contribution are not suitable for the special issues. Authors who are uncertain about the suitability of their papers should contact the special issue editors. Papers will go through the usual review procedures and will be accepted or rejected based on the recommendations of the editors and referees. However, the review process will be streamlined to facilitate the timely publication of the papers. Papers that are considered for publication must contain original unpublished work and they must not be submitted concurrently to any other journal. Papers should be submitted using the Elsevier Electronic Submission tool EES: (in the EES please choose the appropriate special issue). All manuscripts should be double spaced or they will be returned immediately for revision. Any questions may be directed via to: csda@dcs.bbk.ac.uk. Annals of Computational and Financial Econometrics Selected peer-reviewed papers will be published in the CSDA Annals of Computational and Financial Econometrics (as a supplement of the journal of Computational Statistics & Data Analysis). Submissions for the CSDA Annals of CFE should contain both a computational and an econometric or financial-econometric component. Special Issues Information about the CSDA special issues for the can be found at: CMStatistics and CFEnetwork c XI

12 CFE-ERCIM 2014 SPONSORS ric-az/misura/misura.html EXHIBITORS Elsevier (URL Numerical Algorithms Group (NAG) (URL Springer Taylor & Francis (URL (URL ENDORSED SOCIETIES & GROUPS ERCIM Working Group on Computational and Methodological Statistics (CMStatistics) Computational and Financial Econometrics (CFEnetwork) The Society for Computational Economics International Statistical Institute The Royal Statistical Society The Italian Statistical Society XII CMStatistics and CFEnetwork c

13 CFE-ERCIM 2014 Contents General Information I Committees III Welcome IV ERCIM Working Group on Computational and Methodological Statistics V CFEnetwork Computational and Financial Econometrics VI Scientific and Social Programme Schedule, Meetings and Social Events Information VII Venue, lecture rooms, presentation instructions and internet access VIII Map of the venue and nearby area X Publications outlets XI Sponsors, exhibitors and supporting societies XII Keynote Talks 1 MISURA PRIN Project CFE-ERCIM Keynote talk 1 (Mike West, Duke University, United States) Saturday at 09:10-10:00 Dynamic sparsity modelling CFE Keynote talk 2 (Robert Taylor, University of Essex, UK) Saturday at 11:55-12:45 Tests for explosive financial bubbles in the presence of non-stationary volatility ERCIM Keynote talk 2 (Nicolai Meinhausen, ETH, Switzerland) Saturday at 18:10-19:00 Maximin effects in inhomogeneous large-scale data CFE-ERCIM Keynote talk 3 (Irene Gijbels, Katholieke Universiteit Leuven, Belgium) Monday at 12:25-13:15 Recent advances in estimation of conditional distributions, densities and quantiles University of Salerno CFE-ERCIM Keynote talk 4 (James G. MacKinnon, Queens University, Canada) Monday at 18:25-19:15 Cluster-robust inference and the wild cluster bootstrap Parallel Sessions 2 Parallel Session B CFE (Saturday at 10:30-11:45) 2 CS10: QUANTILE REGRESSION APPLICATIONS IN FINANCE (Room: E2) CS13: BAYESIAN ECONOMETRICS (Room: A2) CS22: DYNAMIC MODELING OF VARIANCE RISK PREMIA (Room: Q2) CS34: ECONOMETRICS OF ART MARKETS (Room: G2) CS35: ANALYSIS OF EXTREMES AND DEPENDENCE (Room: O2) CS50: MONITORING MACRO-ECONOMIC IMBALANCES AND RISKS (Room: P2) CS68: MULTIVARIATE TIME SERIES (Room: B2) CS71: NONPARAMETRIC AND SEMIPARAMETRIC METHODS: RECENT DEVELOPMENTS (Room: I2) CS83: ENERGY PRICE AND VOLATILITY MODELLING (Room: N2) CS88: QUANTITIVE METHODS IN CREDIT RISK MANAGEMENT (Room: C2) CS90: RISK ESTIMATION AND ESTIMATION RISK (Room: M2) CS61: FILTERS WAVELETS AND SIGNALS I (Room: D2) Parallel Session D ERCIM (Saturday at 10:30-12:35) 8 ES19: DEPENDENCE MODELS AND COPULAS: THEORY I (Room: N1) ES27: OUTLIERS AND EXTREMES IN TIME SERIES (Room: A1) ES35: THRESHOLD SELECTION IN STATISTICS OF EXTREMES (Room: B1) ES48: HANDLING NUISANCE PARAMETERS: ADVANCES TOWARDS OPTIMAL INFERENCE (Room: D1) ES63: MIXTURE MODELS FOR MODERN APPLICATIONS I (Room: L1) ES67: SMALL AREA ESTIMATION (Room: C1) ES74: STATISTICAL INFERENCE FOR LIFETIME DATA WITH APPLICATION TO RELIABILITY (Room: F1) ES80: ADVANCES IN SPATIAL FUNCTIONAL DATA ANALYSIS (Room: M1) ES94: STATISTICS WITH ACTUARIAL/ECONOMIC APPLICATIONS (Room: I1) ES99: METHODS FOR SEMIPARAMETRIC ANALYSIS OF COMPLEX SURVIVAL DATA (Room: E1) ES122: ADVANCES IN LATENT VARIABLES (Room: G1) ES39: BAYESIAN SEMI- AND NONPARAMETRIC MODELLING I (Room: Q1) Parallel Session D CFE (Saturday at 14:35-16:15) 17 CSI02: MODELING AND FORECASTING HIGH DIMENSIONAL TIME SERIES (Room: Sala Convegni) CS01: FINANCIAL ECONOMETRICS (Room: M2) CS03: ADVANCES IN IDENTIFICATION OF STRUCTURAL VECTOR AUTOREGRESSIVE MODELS (Room: C2) CS17: LIQUIDITY AND CONTAGION (Room: N2) CS32: CO-MOVEMENTS IN MACROECONOMICS AND FINANCE (Room: P2) CS42: TIME-SERIES ECONOMETRICS (Room: B2) CS53: ADVANCES IN DSGE MODELLING (Room: O2) CS58: STATISTICAL MODELLING IN BANKING AND INSURANCE REGULATIONS (Room: H2) CS62: FILTERS WAVELETS AND SIGNALS II (Room: D2) CS64: REGIME CHANGE MODELING IN ECONOMICS AND FINANCE I (Room: G2) CS80: FINANCIAL MODELLING (Room: E2) CMStatistics and CFEnetwork c XIII

14 CFE-ERCIM 2014 Parallel Session E ERCIM (Saturday at 14:35-16:15) 24 ESI01: ROBUSTNESS AGAINST ELEMENTWISE CONTAMINATION (Room: A1) ES07: STATISTICAL METHODS FOR DEPENDENT SEQUENCES (Room: H1) ES14: SUFFICIENCY AND DATA REDUCTIONS IN REGRESSION (Room: E1) ES20: CHANGE-POINT ANALYSIS (Room: P1) ES41: ROBUST ESTIMATION IN EXTREME VALUE THEORY (Room: B1) ES50: DIRECTIONAL STATISTICS (Room: G1) ES60: SIMULTANEOUS EQUATION MODELS CONTROLLING FOR UNOBSERVED CONFOUNDING (Room: D1) ES65: GOODNESS-OF-FIT TESTS (Room: L1) ES92: CURE MODELS AND COMPETING RISKS IN SURVIVAL ANALYSIS (Room: F1) ES101: FUNCTIONAL REGRESSION MODELS AND APPLICATIONS (Room: M1) ES129: STATISTICAL METHODS AND APPLICATIONS (Room: I1) ES77: CONTRIBUTIONS TO CLASSIFICATION AND CLUSTERING (Room: O1) ES29: DEPENDENCE MODELS AND COPULAS: THEORY II (Room: N1) ES45: BAYESIAN SEMI- AND NONPARAMETRIC MODELLING II (Room: Q1) ES06: BIG DATA ANALYSIS: PENALTY, PRETEST AND SHRINKAGE ESTIMATION I (Room: C1) Parallel Session E CFE (Saturday at 16:45-18:50) 33 CS02: VOLATILITY AND CORRELATION MODELLING FOR FINANCIAL MARKETS (Room: N2) CS103: CONTRIBUTIONS TO APPLIED ECONOMETRICS I (Room: F2) CS06: NON-STATIONARY TIME SERIES AND THE BOOTSTRAP (Room: B2) CS16: MODELLING FINANCIAL CONTAGION (Room: E2) CS18: STATISTICAL SIGNAL PROCESSING IN ASSET MANAGEMENT (Room: O2) CS21: BEHAVIOURAL AND EMOTIONAL FINANCE: THEORY AND EVIDENCE (Room: A2) CS47: CYCLICAL COMPOSIT INDICATORS (Room: P2) CS55: VOLATILITY MODELS AND THEIR APPLICATIONS (Room: G2) CS57: RISK MEASURES (Room: C2) CS60: CONTRIBUTIONS ON TIME SERIES ECONOMETRICS I (Room: D2) CS100: EMPIRICAL APPLICATIONS IN MACROECONOMICS AND TIME SERIES ANALYSIS (Room: M2) CS46: MACRO AND FORECASTING (Room: I2) Parallel Session F ERCIM (Saturday at 16:45-18:00) 42 ES03: STATISTICAL MODELLING WITH R (Room: I1) ES13: HIGH-DIMENSIONAL CAUSAL INFERENCE (Room: B1) ES33: STATISTICAL METHODS IN HIGH DIMENSIONS (Room: L1) ES36: STATISTICS IN FUNCTIONAL AND HILBERT SPACES (Room: M1) ES38: INFERENCE BASED ON THE LORENZ AND GINI INDEX OF INEQUALITY (Room: E1) ES53: BAYESIAN SEMI AND NONPARAMETRIC ESTIMATION IN COPULA MODELS (Room: Q1) ES58: HEURISTIC OPTIMIZATION AND ECONOMIC MODELLING (Room: D1) ES78: DIRECTED AND UNDIRECTED GRAPH MODELS (Room: G1) ES97: CHANGE-POINTS IN TIME SERIES I (Room: P1) ES103: TAIL DEPENDENCE AND MARGINALS WITH HEAVY TAILS (Room: N1) ES106: ROBUST CLUSTERING WITH MIXTURES (Room: A1) ES116: INTELLIGENT DATA ANALYSIS FOR HIGH DIMENSIONAL SYSTEMS (Room: H1) ES125: MISSING COVARIATE INFORMATION IN SURVIVAL ANALYSIS (Room: F1) ES127: BIOSTATISTICS, EPIDEMIOLOGY AND TWIN STUDIES (Room: C1) ES153: MULTIVARIATE STATISTICS I (Room: O1) Parallel Session F CFE (Sunday at 08:45-10:25) 49 CS11: BAYESIAN NONLINEAR ECONOMETRICS (Room: A2) CS14: RECENT DEVELOPMENTS IN VOLATILITY MODELLING (Room: N2) CS20: BANKS AND THE MACROECONOMY: EMPIRICAL MODELS FOR STRESS TESTING (Room: H2) CS30: MIXTURE MODELS FOR FINANCIAL AND MACROECONOMIC TIME SERIES (Room: I2) CS48: NOWCASTING AND FORECASTING MACRO-ECONOMIC TRENDS (Room: P2) CS73: REGIME SWITCHING, FILTERING, AND PORTFOLIO OPTIMIZATION (Room: G2) CS76: TOPICS IN FINANCIAL ECONOMETRICS (Room: M2) CS82: BOOTSTRAPPING TIME SERIES AND PANEL DATA (Room: B2) CS96: ECONOMETRIC MODELS FOR MIXED FREQUENCY DATA (Room: E2) CP01: POSTER SESSION (Room: First floor Hall) Parallel Session G ERCIM (Sunday at 08:45-10:25) 56 ESI03: TIME SERIES MODELING AND COMPUTATION (Room: Sala Convegni) ES12: MULTIVARIATE SURVIVAL ANALYSIS AND MULTI-STATE MODELS (Room: F1) ES26: ADVANCES IN CLUSTER ANALYSIS (Room: O1) ES31: APPLICATIONS IN ENGINEERING AND ECONOMICS (Room: I1) ES32: DIRECTIONAL STATISTICS (Room: G1) ES46: GENERALIZED ADDITIVE MODELS FOR LOCATION, SCALE AND SHAPE (Room: E1) ES49: STATISTICAL INFERENCE IN HIGH DIMENSIONS (Room: B1) XIV CMStatistics and CFEnetwork c

15 CFE-ERCIM 2014 ES54: WHAT IS NEW IN MODELING AND DESIGN OF EXPERIMENTS (Room: N1) ES56: ROBUST STATISTICAL MODELING (Room: A1) ES69: ANALYSIS OF COMPLEX FUNCTIONAL DATA (Room: M1) ES75: RECENT ADVANCES IN GENETIC ASSOCIATION STUDIES (Room: C1) ES96: SOCIAL NETWORK DATA ANALYSIS (Room: D1) ES108: BAYESIAN NONPARAMETRIC AND ROBUSTNESS (Room: Q1) ES114: LATENT VARIABLES AND FEEDBACK IN GRAPHICAL MARKOV MODELS (Room: H1) ES121: STOCHASTIC MODELS FOR POPULATION DYNAMICS (Room: P1) ES126: RESAMPLING TESTS (Room: L1) EP02: POSTER SESSION I (Room: First floor Hall) Parallel Session I CFE (Sunday at 10:55-13:00) 67 CSI03: VAR MODELING, COINTEGRATION AND UNCERTAINTY (Room: Sala Convegni) CS54: CONTRIBUTIONS ON VOLATILITY MODELS AND ESTIMATION (Room: N2) CS12: MODELLING UNCERTAINTY IN MACROECONOMICS (Room: P2) CS23: A NOVEL PERSPECTIVE IN PREDICTIVE MODELLING FOR RISK ANALYSIS (Room: Q2) CS26: STATISTICAL INFERENCE ON RISK MEASURES (Room: Q2) CS27: INFERENCE FOR FINANCIAL TIMES SERIES (Room: B2) CS63: FORECASTING (Room: E2) CS74: REGIME CHANGE MODELING IN ECONOMICS AND FINANCE II (Room: G2) CS92: REAL-TIME MODELLING WITH DATA SUBJECT TO DIFFERENT COMPLICATIONS (Room: O2) CS94: MACROECONOMETRICS (Room: I2) CS97: RISK MODELING AND APPLICATIONS (Room: C2) CS98: BAYESIAN ECONOMETRICS IN FINANCE (Room: A2) CS110: CONTRIBUTIONS TO APPLIED ECONOMETRICS II (Room: F2) CS78: CONTRIBUTIONS ON TIME SERIES ECONOMETRICS II (Room: D2) Parallel Session J ERCIM (Sunday at 10:55-13:00) 77 ES04: CLASSIFICATION AND DISCRIMINANT PROCEDURES FOR DEPENDENT DATA (Room: O1) ES05: ADVANCES IN SPATIO-TEMPORAL ANALYSIS (Room: H1) ES09: STATISTICAL INFERENCE FOR NONREGULAR MODELS (Room: E1) ES11: MODELING DEPENDENCE FOR MULTIVARIATE TIME TO EVENTS DATA (Room: F1) ES15: MODEL SPECIFICATION TESTS (Room: L1) ES22: DEPENDENCE MODELS AND COPULAS: THEORY III (Room: N1) ES24: COMPONENT-BASED MULTILAYER PATH MODELS (Room: D1) ES25: MULTIVARIATE EXTREMES (Room: B1) ES82: ROBUSTNESS IN SURVEY SAMPLING (Room: A1) ES83: DATA ON MANIFOLDS AND MANIFOLD DATA (Room: M1) ES87: DATA SCIENCE AND THEORY (Room: I1) ES95: BAYESIAN INFERENCE FOR BIG DATA (Room: Q1) ES115: ELECTRICITY LOAD FORECASTING (Room: C1) ES123: SPARSITY AND NETWORK (Room: G1) Parallel Session L CFE (Sunday at 14:45-16:25) 87 CS05: MEASURING SYSTEMIC RISK (Room: Q2) CS15: MULTIVARIATE MODELLING OF ECONOMIC AND FINANCIAL TIME SERIES (Room: B2) CS19: BANKS, INTEREST RATES AND PROFITABILITY AFTER THE FINANCIAL CRISIS (Room: H2) CS31: DYNAMIC CONDITIONAL SCORE MODELS (Room: O2) CS45: DEPENDENCE MODELING AND COPULAS (Room: F2) CS67: FINANCIAL AND MACROECONOMIC FORECASTING (Room: P2) CS77: MODELLING UNIVERSITY OUTCOMES THROUGH SURVIVAL ANALYSIS (Room: N2) CS79: MODELLING AND COMPUTATION IN MACRO-ECONOMETRICS (Room: I2) CS87: STATISTICAL ANALYSIS OF FINANCIAL RETURNS (Room: E2) CS93: TIME SERIES ANALYSIS IN ECONOMICS (Room: A2) CS44: CONTRIBUTIONS IN FINANCIAL ECONOMETRICS I (Room: M2) Parallel Session L ERCIM (Sunday at 14:45-16:25) 94 ESI04: STATISTICAL ANALYSIS OF EVENT TIMES (Room: Sala Convegni) ES01: OODA-TREE STRUCTURED DATA OBJECTS (Room: N1) ES08: STATISTICS FOR IMPRECISE-VALUED DATA (Room: I1) ES28: RECENT ADVANCES IN STATISTICAL GENETICS (Room: C1) ES40: NEW APPROACHES IN SPATIAL STATISTICS (Room: H1) ES42: BAYESIAN ANALYSIS OF GRAPHICAL MODELS (Room: P1) ES44: MODEL-BASED CLUSTERING FOR CATEGORICAL AND MIXED-TYPE DATA (Room: L1) ES51: BAYESIAN SEMI- AND NONPARAMETRIC MODELLING III (Room: Q1) ES79: COUNTING PROCESSES (Room: D1) ES90: ROBUST AND NONPARAMETRIC METHODS (Room: A1) ES105: FLEXIBLE MODELS FOR DIRECTIONAL DATA (Room: G1) CMStatistics and CFEnetwork c XV

16 CFE-ERCIM 2014 ES117: HIGH-DIMENSIONAL BAYESIAN MODELING (Room: B1) ES119: HIGH-DIMENSIONAL DATA ANALYSIS (Room: M1) ES132: COMPUTATIONAL STATISTICS I (Room: O1) ES37: CHANGE-POINTS IN TIME SERIES II (Room: F1) ES130: NON-PARAMETRIC AND SEMI-PARAMETRIC METHODS (Room: E1) EP01: POSTER SESSION II (Room: First floor Hall) EP03: POSTER SESSION III (Room: First floor Hall) Parallel Session M CFE (Sunday at 16:55-18:35) 107 CSI01: VOLATILITY MODELLING (Room: Sala Convegni) CS24: NONSTATIONARY TIME SERIES AND FINANCIAL BUBBLES (Room: A2) CS28: FINANCIAL ECONOMETRICS (Room: M2) CS37: COMMON FEATURES IN FINANCE AND MACROECONOMICS (Room: I2) CS49: DEVELOPMENT ON SEASON ADJUSTMENT AND SEASONAL MODELLING (Room: P2) CS56: MODELING MULTIVARIATE FINANCIAL TIME SERIES (Room: D2) CS84: TAIL RISKS (Room: Q2) CS85: FUNDS PERFORMANCE MEASUREMENT (Room: E2) CS89: ANALYSING COMPLEX TIME SERIES (Room: B2) CS91: ESTIMATION OF MACROECONOMIC MODELS WITH TIME VARYING VOLATILITY (Room: N2) CS99: STATISTICAL METHODS FOR MODELLING SPATIO-TEMPORAL RANDOM FIELDS (Room: O2) CS107: ENERGY ECONOMICS (Room: F2) Parallel Session N ERCIM (Sunday at 16:55-19:00) 114 ES18: STATISTICAL METHODS FOR SPATIAL EPIDEMIOLOGY (Room: H1) ES23: DEPENDENCE MODELS AND COPULAS: APPLICATIONS (Room: N1) ES30: ADVANCES IN ROBUST DATA ANALYSIS (Room: A1) ES34: REGRESSION MODELS FOR EXTREME VALUES (Room: B1) ES52: RECENT DEVELOPMENTS IN THE DESIGN OF EXPERIMENTS (Room: G1) ES55: NEW SOFTWARE DEVELOPMENTS FOR COMPETING RISKS AND MULTI-STATE MODELS (Room: F1) ES70: NONPARAMETRIC FUNCTIONAL DATA ANALYSIS (Room: M1) ES76: STATISTICAL METHODS FOR INSURANCE AND FINANCE (Room: D1) ES93: INFERENCE UNDER IMPRECISE KNOWLEDGE (Room: I1) ES102: BAYESIAN NETWORKS IN OFFICIAL STATISTICS (Room: Q1) ES113: ADVANCES IN ORDINAL AND PREFERENCE DATA (Room: C1) ES120: CHANGE-POINT DETECTION, DEPENDENCE MODELLING AND RELATED ISSUES (Room: P1) ES124: SURVIVAL AND RELIABILITY (Room: E1) Parallel Session O CFE (Monday at 08:45-10:05) 124 CS09: VOLATILITY MEASURING, MODELING AND APPLICATIONS (Room: N2) CS29: SOLUTION AND ESTIMATION OF NON-LINEAR GENERAL EQUILIBRIUM MODELS (Room: O2) CS41: APPLIED ECONOMIC ISSUES (Room: E2) CS69: RISK PREMIA TIME SERIES (Room: B2) CS75: QUANTITATIVE RISK MANAGEMENT (Room: C2) CS102: CONTRIBUTIONS IN FINANCIAL ECONOMETRICS II (Room: M2) CS104: CONTRIBUTIONS TO BAYESIAN ECONOMETRICS I (Room: A2) CS106: COMPUTATIONAL ECONOMETRICS I (Room: F2) CS65: REGIME CHANGE MODELING IN ECONOMICS AND FINANCE III (Room: G2) Parallel Session O ERCIM (Monday at 08:45-10:05) 129 ES145: DIMENSIONALITY REDUCTION AND VARIABLE SELECTION (Room: O1) ES148: DISCRETE STATISTICS (Room: M1) ES57: RECENT APPLICATIONS OF GRAPHICAL MARKOV MODELS (Room: H1) ES151: DEPENDENCE IN APPLIED STATISTICS AND DATA ANALYSIS (Room: N1) ES68: MARKOV PROCESSES (Room: F1) ES155: MULTIVARIATE MODELLING (Room: P1) ES84: BAYESIAN METHODS I (Room: Q1) ES131: METHODOLOGICAL STATISTICS I (Room: E1) ES139: CONTRIBUTIONS TO ROBUST DATA ANALYSIS (Room: A1) ES133: APPLIED STATISTICS AND DATA ANALYSIS I (Room: I1) ES138: CONTRIBUTIONS TO BIOSTATISTICS AND BIOINFORMATICS (Room: C1) ES61: MIXTURE MODELS FOR MODERN APPLICATIONS II (Room: D1) ES134: STATISTICAL INFERENCE I (Room: L1) ES137: CONTRIBUTIONS TO NETWORK AND SPATIAL DATA ANALYSIS (Room: G1) ES135: CONTRIBUTIONS TO STATISTICS OF EXTREMES AND APPLICATIONS (Room: B1) XVI CMStatistics and CFEnetwork c

17 CFE-ERCIM 2014 Parallel Session P CFE (Monday at 10:35-12:15) 138 CS112: FINANCIAL APPLICATIONS I (Room: E2) CS95: CONTRIBUTIONS ON PANEL DATA (Room: M2) CS101: CONTRIBUTIONS ON TIME SERIES ECONOMETRICS III (Room: B2) CS04: CONTRIBUTIONS ON VOLATILITY AND CORRELATION MODELLING (Room: N2) CS66: CONTRIBUTIONS ON BANKING AND FINANCIAL MARKETS (Room: H2) CS40: CONTRIBUTIONS TO FORECASTING (Room: A2) CS43: CONTRIBUTIONS ON QUANTILE REGRESSION, NON/SEMI-PARAMETRIC METHODS (Room: O2) CS39: CONTRIBUTIONS TO DEPENDENCE MODELING AND COPULAS (Room: F2) CS109: CONTRIBUTIONS TO THE MACROECONOMY AND ASSET PRICES (Room: P2) CS81: CONTRIBUTIONS TO QUANTITATIVE RISK MANAGEMENT (Room: Q2) CS36: CONTRIBUTIONS TO MACRO AND FORECASTING (Room: I2) Parallel Session P ERCIM (Monday at 10:35-12:15) 147 ESI02: STATISTICS OF EXTREMES AND APPLICATIONS (Room: Sala Convegni) ES16: BAYESIAN SEMIPARAMETRIC INFERENCE (Room: Q1) ES43: STATISTICAL ANALYSIS OF FMRI DATA (Room: E1) ES59: COMPUTATIONAL APPROACHES IN FINANCIAL ECONOMICS (Room: D1) ES71: APPLICATIONS OF FUNCTIONAL DATA ANALYSIS (Room: M1) ES72: BIND SOURCE SEPARATION: STATISTICAL PRINCIPLES (Room: B1) ES73: STATISTICAL MODELS FOR THE HEALTH CARE ASSESSMENT (Room: P1) ES64: THEORETICAL ECONOMETRIC ADVANCES IN FINANCIAL RISK MEASUREMENT (Room: G2) ES86: OUTLIER AND ANOMALY DETECTION IN MODERN DATA SETTINGS (Room: A1) ES100: THE THEORY OF GRAPHICAL MARKOV MODELS: RECENT DEVELOPMENTS (Room: H1) ES104: PARTICLE FILTERING (Room: L1) ES107: DESIGNING EXPERIMENTS WITH UNAVOIDABLE AND HEALTHY CORRELATION (Room: N1) ES109: ASTROSTATISTICS (Room: I1) ES111: SAE AND INEQUALITIES MEASUREMENT (Room: C1) ES112: RECENT DEVELOPMENTS IN LATENT VARIABLE MODELS (Room: F1) ES154: MULTIVARIATE STATISTICS II (Room: O1) Parallel Session Q CFE (Monday at 14:50-16:30) 157 CS33: STATISTICAL ANALYSIS OF CLIMATE TIME SERIES (Room: D2) CS114: CONTRIBUTIONS IN FINANCIAL ECONOMETRICS III (Room: M2) CS38: MACROECONOMETRICS (Room: N2) CS52: TEMPORAL DISAGGREGATION AND BENCHMARKING TECHNIQUES (Room: P2) CS70: MIDAS MODELS: APPLICATIONS IN ECONOMICS AND FINANCE (Room: F2) CS72: ECONOMETRIC AND QUANTITATIVE METHODS APPLIED TO FINANCE (Room: O2) CS86: TOPICS IN TIME SERIES AND PANEL DATA ECONOMETRICS (Room: B2) Parallel Session Q ERCIM (Monday at 14:50-16:30) 162 ES02: BIG DATA ANALYSIS: PENALTY, PRETEST AND SHRINKAGE ESTIMATION II (Room: C1) ES10: ROBUST METHODS ON FUNCTIONAL DATA AND COMPLEX STRUCTURES (Room: A1) ES21: BIOSTATISTICS AND BIOINFORMATICS (Room: O1) ES47: FUNCTIONAL DATA ANALYSIS (Room: M1) ES62: HIGH-FREQUENCY DATA STATISTICS: ADVANCES IN METHODOLOGY (Room: D1) ES81: INFERENCE IN MODELS FOR CATEGORICAL DATA (Room: L1) ES85: SPATIAL DEPENDENCE FOR OBJECT DATA (Room: N1) ES89: ROBUST STATISTICS IN R (Room: B1) ES91: BAYESIAN NONPARAMETRICS AND MCMC (Room: P1) ES98: ADVANCES IN QUANTILE REGRESSION (Room: E1) ES118: CURRENT ISSUES IN BAYESIAN NONPARAMETRICS (Room: Q1) ES144: STATISTICAL METHODS FOR PROCESSES IN ENGINEERING AND INDUSTRY (Room: I1) Parallel Session R CFE (Monday at 16:55-18:15) 170 CS111: CONTRIBUTIONS TO BAYESIAN ECONOMETRICS II (Room: A2) CS113: COMPUTATIONAL ECONOMETRICS II (Room: O2) CS59: CONTRIBUTIONS ON STOCHASTIC VOLATILITY (Room: N2) CS07: CONTRIBUTIONS ON NON-LINEAR TIME-SERIES MODELS (Room: B2) CS108: CONTRIBUTIONS TO APPLICATIONS IN MACROECONOMICS AND TIME SERIES (Room: P2) CS105: FINANCIAL APPLICATIONS II (Room: E2) CS51: CONTRIBUTIONS IN FINANCIAL ECONOMETRICS IV (Room: M2) CMStatistics and CFEnetwork c XVII

18 CFE-ERCIM 2014 Parallel Session R ERCIM (Monday at 16:55-18:15) 175 ES140: CONTRIBUTIONS TO ROBUSTNESS AND OUTLIER IDENTIFICATION (Room: A1) ES146: REGRESSION MODELS (Room: E1) ES152: STATISTICAL INFERENCE II (Room: L1) ES88: BAYESIAN METHODS II (Room: Q1) ES110: CONTRIBUTIONS TO DEPENDENCE MODELS AND COPULAS (Room: N1) ES156: CLUSTER-BASED METHODS (Room: O1) ES143: CONTRIBUTIONS ON FUNCTIONAL DATA AND TIME SERIES (Room: M1) ES136: CONTRIBUTIONS TO HIGH-DIMENSIONAL DATA ANALYSIS (Room: B1) ES142: CONTRIBUTIONS TO ORDINAL AND PREFERENCE DATA (Room: G1) ES147: METHODOLOGICAL STATISTICS II (Room: F1) ES150: APPLIED STATISTICS AND DATA ANALYSIS II (Room: I1) ES149: COMPUTATIONAL STATISTICS II (Room: D1) XVIII CMStatistics and CFEnetwork c

19 CFE-ERCIM 2014 Keynote Talks Saturday :10-10:00 Room: Auditorium Chair: Manfred Deistler MISURA PRIN Project CFE-ERCIM Keynote talk 1 Dynamic sparsity modelling Speaker: Mike West, Duke University, United States Jouchi Nakajima The research links to some of the history of Bayesian sparsity modelling in multivariate time series analysis and forecasting, and then reviews recent modelling innovations for dynamic sparsity modelling using the concept- and resulting methodology- of dynamic latent thresholding. Recent applications of this general approach to dynamic sparsity modelling have had demonstrable success in inducing parsimony into dynamic model structures in time series analysis, improving model interpretations, forecast accuracy and precision- and, as a result, decisions reliant on forecasts- in a series of econometric and financial studies. Emerging applications in dynamic network modelling further highlight the potential and opportunity provided by the dynamic latent thresholding approach in various areas. Saturday :55-12:45 Room: Auditorium Chair: Jean-Marie Dufour CFE Keynote talk 2 Tests for explosive financial bubbles in the presence of non-stationary volatility Speaker: Robert Taylor, University of Essex, UK David Harvey, Stephen Leybourne, Robert Sollis The aim is to study the impact of permanent volatility shifts in the innovation process on the performance of the test for explosive financial bubbles based on recursive right-tailed Dickey-Fuller-type unit root tests proposed previously. We show that, in this situation, their supremum-based test has a non-pivotal limit distribution under the unit root null, and can be quite severely over-sized, thereby giving rise to spurious indications of explosive behaviour. We investigate the performance of a wild bootstrap implementation of their test procedure for this problem, and show it is effective in controlling size, both asymptotically and in finite samples, yet does not sacrifice power relative to an (infeasible) size-adjusted version of their test, even when the shocks are homoskedastic. We also discuss an empirical application involving commodity price time series and find considerably less emphatic evidence for the presence of speculative bubbles in these data when using our proposed wild bootstrap implementation of the test. Saturday :10-19:00 Room: Auditorium Chair: Erricos John Kontoghiorghes ERCIM Keynote talk 2 Maximin effects in inhomogeneous large-scale data Speaker: Nicolai Meinhausen, ETH, Switzerland Large-scale data are often characterised by some degree of inhomogeneity as data are either recorded in different time regimes or taken from multiple sources. We look at regression models and the effect of randomly changing coefficients, where the change is either smoothly in time or some other dimension or even without any such structure. Fitting varying-coefficient models or mixture models can be appropriate solutions but are computationally very demanding and often try to return more information than necessary. If we just ask for a model estimator that shows good predictive properties for all regimes of the data, then we are aiming for a simple linear model that is reliable for all possible subsets of the data. We propose a maximin effects estimator and look at its prediction accuracy from a theoretical point of view in a mixture model with known or unknown group structure. Under certain circumstances the estimator can be computed orders of magnitudes faster than standard penalised regression estimators, making computations on large-scale data feasible. We also show how a modified version of bagging can accurately estimate maximin effects. Monday :25-13:15 Room: Auditorium Chair: Monica Pratesi CFE-ERCIM Keynote talk 3 Recent advances in estimation of conditional distributions, densities and quantiles Speaker: Irene Gijbels, Katholieke Universiteit Leuven, Belgium Starting point is the estimation of a conditional distribution function, which is at the basis of many statistical problems. A conditional distribution function describes how the distribution of a variable of interest Y changes with the value of a covariate X (or a set of covariates). Depending on some prior knowledge on how the covariate X influences Y, one can pre-adjust the response observations for obvious effects of the covariate, and as such provide opportunities to an improved estimation of the conditional distribution function. The developed techniques can be extended to a variety of other estimation settings, such as conditional quantile estimation and conditional copula estimation; as well as to more complex data settings (for example censored data). Monday :25-19:15 Room: Auditorium Chair: Helmut Luetkepohl University of Salerno CFE-ERCIM Keynote talk 4 Cluster-robust inference and the wild cluster bootstrap Speaker: James G. MacKinnon, Queens University, Canada Using cluster-robust standard errors is now standard in many areas of applied econometrics, because even very small levels of intra-cluster correlation can make conventional standard errors severely inaccurate when some clusters are large. However, using t statistics based on cluster-robust standard errors may not yield accurate inferences when the number of clusters is small and/or cluster sizes vary greatly. In such cases, it can be very attractive to use the wild cluster bootstrap, but just how that method is implemented matters. Studentized bootstrap intervals are easy to calculate but may not yield sufficiently reliable inferences. It is better to use restricted estimates in the bootstrap data generating process. This is easy to do when testing hypotheses, but it can be expensive to compute bootstrap confidence intervals in this way. Simulation evidence that illustrates the above points is presented. Modified wild cluster bootstrap procedures based on transforming the residuals is then discussed. These are similar to methods that are already used with the ordinary wild bootstrap for regression with heteroskedastic but independent disturbances. Unfortunately, these procedures can become computationally infeasible when some clusters are very large. CMStatistics and CFEnetwork c 1

20 Saturday :30-11:45 CFE-ERCIM 2014 Parallel Session B CFE Saturday :30-11:45 Parallel Session B CFE CS10 Room E2 QUANTILE REGRESSION APPLICATIONS IN FINANCE Chair: Massimiliano Caporin C044: Modeling and forecasting the range bipower variation conditional quantiles Presenter: Giovanni Bonaccolto, University of Padua, Italy Co-authors: Massimiliano Caporin The aim is focused on the Realized Range Volatility, an estimator of the quadratic variation of financial prices, taking into account the impact of microstructure noise and jumps in the context of high frequency data. In contrast to existing studies, the aim is to model and forecast the conditional quantiles of the Realized Range Volatility. Therefore, a quantile regression analysis is carried out, by using as predictors both the lagged values of the estimated volatility and some key macroeconomic and financial variables. Those give important information about the overall market trend and risk. In this way, and without distributional assumptions on the realized range innovations, it is possible to assess the entire conditional distribution of the estimated volatility. This is a critical issue for financial decision makers in terms of pricing, asset allocation and risk management. How the links among the involved variables change across the quantile levels is analyzed. Furthermore, a rolling analysis is performed in order to check how the relationships which characterize the proposed model evolve over time. The analysis is applied to 16 stocks of the U.S. market and the forecast goodness is validated by means of a suitable test statistic. C715: Dynamic model averaging for quantile regression Presenter: Lea Petrella, Sapienza University of Rome, Italy Co-authors: Mauro Bernardi, Roberto Casarin A general dynamic model averaging (DMA) approach is provided to sequential estimation of quantile regression models with time-varying parameters. We build a new sequential Markov-chain Monte Carlo (MCMC) algorithm to approximate the posterior distribution of models and parameters. The efficiency and the effectiveness of the proposed DMA approach and the MCMC algorithm is shown through simulation studies and applications to macro-economics and finance. C412: Spillover effect to bailout expectation: an empirical study of Denmark Presenter: Massimiliano Caporin, University of Padova, Italy Co-authors: Francesco Ravazzolo, Paolo Santucci de Magistris The spillover effect to bailout expectations of bond holders during the recent financial crisis is studied with a focus to Denmark. Up to the beginning of 2011, investors thought that Scandinavian governments were providing an implicit government guarantee for bank bond holders. This stems from the experience linked to the real estate bubble hitting the area in the late 1980 s, where stock holders lost everything but bond holders were guaranteed. However, two small Danish banks defaulted in early 2011 and in that case bond holders were not rescued, and they were sharing losses. Starting from this evidence, we verify the spillover effect of this breaking event on the bailout expectations to Danish banks. We focus on a specific Danish institution and use non-danish banks as control cases. In determining the change in bailout expectations we make use of different techniques, starting from cumulated abnormal return analyses on the CDS (that monitors bond holders risks), to linear models with a number of control covariates up to quantile regression methods. Results show evidence of a change in bailout expectations after CS13 Room A2 BAYESIAN ECONOMETRICS Chair: Richard Gerlach C484: Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution Presenter: Toshiaki Watanabe, Hitotsubashi University, Japan Co-authors: Makoto Takahashi, Yasuhiro Omori The realized volatility, which is the sum of squared intraday returns, is subject to the bias caused by microstructure noise and non-trading hours. The realized stochastic volatility model incorporates the stochastic volatility model with the realized volatility taking account of the bias. It is extended using the generalized hyperbolic skew Student s t-distribution as the return distribution conditional on volatility for quantile forecast such as value-at-risk and expected shortfall. It includes the Student s t and normal distributions as special cases. A Bayesian method via Markov chain Monte Carlo is developed for the analysis of the extended model, which makes it possible to estimate the parameters and forecast volatility and return quantile jointly by sampling them from their joint posterior distribution. Using daily returns and realized volatility for the S&P 500 stock index, the stochastic volatility and realized stochastic volatility models with the normal, Student s t and generalized hyperbolic skew Student s t- distributions are applied to volatility and quantile forecasts. The performance is compared using several backtesting procedures. The performance of the realized stochastic volatility model with the realized kernel calculated taking account of the bias caused by microstructure noise is also compared. C499: Cholesky realized stochastic volatility model with leverage Presenter: Yasuhiro Omori, University of Tokyo, Japan Co-authors: Shinichiro Shirota, Hedibert Lopes, Hai Xiang Piao Multivariate stochastic volatility models play important roles in financial applications, including asset allocation and risk management. However, estimation of multivariate stochastic volatility models has some difficulties such as the curse of dimension and preserving the positive definiteness of estimated covariance matrix. We consider the dynamic modelling of the Cholesky decomposition for high dimensional covariance matrices, where we incorporate stylized facts of financial markets such as leverage effects and correlations among volatilities. Further, the realized covariance is considered as another source of information for the true covariance matrix. A simple efficient estimation is proposed using MCMC algorithm. C865: Bayesian hierarchical spatial-temporal modeling Presenter: Mike So, The Hong Kong University of Science and Technology, China Spatial time series are commonly observed in environmental sciences and epidemiological research where, under most circumstances, the time series are non-stationary. For better estimation and prediction, the temporal-variability must be captured in traditional spatial models. We propose a Bayesian hierarchical spatial-temporal model to describe the dependence of time series data on spatial locations while accounting for time series properties. The first layer of the hierarchical model specifies a measurement process for the observed spatial data. The second layer characterizes a latent mean process and a latent variance process. The hierarchical formulation concludes with a third layer of priors on parameters. A key idea is to model spatial and temporal dependence simultaneously. Statistical inference is performed by Markov chain Monte Carlo methods which involve spatial dependence parameters. The methodology is applied to simulated data and real environmental data for illustration. CS22 Room Q2 DYNAMIC MODELING OF VARIANCE RISK PREMIA Chair: Matthias Fengler C1281: A dynamic partial equilibrium model for asset pricing with volatility risk premium and reference dependent preferences Presenter: Maria Grith, HU Berlin, Germany The aim is to reconciliate the empirical findings about U-shaped and hump-shaped pricing kernels implied from index options and historical returns of the stock index by using the volatility risk premium as a state variable - proxy for the economic conditions - in a model with heterogeneous agents whose preferences are reference-dependent. In this model, a more pronounced humped-shaped pricing kernel is consistent with relatively more risk loving investors who are targeting the performance relative to the market index. In these periods the volatility risk premium is low and 2 CMStatistics and CFEnetwork c