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Non-homogeneous volatility correlations in the bivariate multifractal model

EUROPEAN JOURNAL OF FINANCE 2015年第12期21卷 971-991页

作者： Liu, Ruipeng Lux, Thomas Deakin Univ Sch Accounting Econ & Finance Melbourne Vic 3125 Australia Univ Kiel Dept Econ D-24118 Kiel Germany Inst World Econ D-24105 Kiel Germany Univ Jaume 1 Banco Espana Chair Computat Econ Castellon de La Plana Spain

In this paper, we consider an extension of the recently proposed bivariate Markov-switching multifractal model of Calvet, Fisher, and Thompson [2006. Volatility Comovement: A Multifrequency Approach. Journal of Econometrics 131: 179-215]. In particular, we allow correlations between volatility components to be non-homogeneous with two different parameters governing the volatility correlations at high and low frequencies. Specification tests confirm the added explanatory value of this specification. In order to explore its practical performance, we apply the model for computing value-at-risk statistics for different classes of financial assets and compare the results with the baseline, homogeneous bivariate multifractal model and the bivariate DCC-GARCH of Engle [2002. Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business & Economic Statistics 20 (3): 339-350]. As it turns out, the multifractal model with heterogeneous volatility correlations provides more reliable results than both the homogeneous benchmark and the DCC-GARCH model.

关键词： long memory multifractal models simulation-based inference value-at-risk C11 C13 G15

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It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model

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JOURNAL OF EMPIRICAL FINANCE 2015年 30卷 62-78页

作者： Grassi, Stefano de Magistris, Paolo Santucci Univ Kent Sch Econ Canterbury CT2 7NZ Kent England Sch Econ Keynes CREATES Canterbury Kent England Aarhus Univ Dept Econ & Business CREATES DK-8210 Aarhus V Denmark

The persistent nature of equity volatility is investigated by means of a multi-factor stochastic volatility model with time varying parameters. The parameters are estimated by means of a sequential matching procedure which adopts as an auxiliary model a time-varying generalization of the HAR model for the realized volatility series. It emerges that during the recent financial crisis the relative weight of the daily component dominates over the monthly term. The estimates of the two factor stochastic volatility model suggest that the change in the dynamic structure of the realized volatility during the financial crisis is due to the increase in the volatility of the persistent volatility term. A set of Monte Carlo simulations highlights the robustness of the methodology adopted in tracking the dynamics of the parameters. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Time-varying parameters On-line Kalman filter simulation-based inference Predictive likelihood Volatility factors

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ESTIMATING THE CANONICAL DISEQUILIBRIUM MODEL - ASYMPTOTIC THEORY AND FINITE-SAMPLE PROPERTIES

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JOURNAL OF ECONOMETRICS 1994年第2期62卷 165-210页

作者： LAROQUE, G SALANIE, B ENSAE PARISFRANCE

The purpose of this paper is to review the various available estimation techniques for the canonical disequilibrium model: full information maximum likelihood (FIML), pseudo maximum likelihood methods, and their simulated counterparts. The use of the latter was advocated by Laroque-Salame (1989) when the model is analytically untractable. We provide some new asymptotic results for all of these methods and compare their behavior on finite samples using Monte Carlo simulations. It appears that FIML performs only slightly better than the second-order PML method, which in turn performs only slightly better than its simulated counterpart. Given the ease of implementation of the latter technique, these results strongly militate for its use.

关键词： DISEQUILIBRIUM ECONOMETRICS simulation-based inference

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Classroom Investigations of Recent Research Concerning the Hot Hand Phenomenon

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JOURNAL OF STATISTICS EDUCATION 2017年第3期25卷 145-157页

作者： Ross, Kevin Calif Polytech State Univ San Luis Obispo Dept Stat San Luis Obispo CA 93407 USA

Many players and fans of basketball believe in the "hot hand" phenomenon, yet for years there has been little statistical evidence that such a phenomenon exists (hence the "hot hand fallacy"). However, recent research of Miller and Sanjurjo suggests that previous analyses of the hot hand have been subject to a bias, and after correcting for this bias, there is in fact evidence that the hot hand is real. Miller and Sanjurjo's analyses are based on permutation tests. In this work, we discuss the ideas behind the permutation test procedure, illustrate an online Shiny app we developed for conducting the test, and present related simulation-based inference activities for introductory statistics courses. Our examples are based on data from the NBA Three Point Contest, in which we do find evidence of an average hot hand effect. Furthermore, we discuss additional topics concerning the bias in previous hot hand studies which can be introduced in courses with a stronger emphasis on probability or mathematical statistics. In particular, we discuss a simple coin flipping problem with a surprising solution which has been the subject of much recent media coverage and debate.

关键词： Bias Conditional probability Expectation Hot hand in basketball Hot hand fallacy Permutation test Probability puzzles Shiny app simulation-based inference

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Using Financial Investment Measures to Proactively Engage Students in the introductory Business Statistics Course

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JOURNAL OF STATISTICS EDUCATION 2018年第1期26卷 17-30页

作者： Berenson, Mark L. Koppel, Nicole B. Lord, Richard A. Chapdelaine, Laura L. Montclair State Univ Informat Management & Business Analyt Dept 1 Normal Ave Montclair NJ 07043 USA Montclair State Univ Accounting & Finance Dept Montclair NJ USA Montclair State Univ Feliciano Sch Business Montclair NJ USA

Typically, the core-required undergraduate business statistics course covers a broad spectrum of topics with applications pertaining to all functional areas of business. The recently updated American Statistical Association's GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report once again stresses the pedagogical importance of topic and application relevancy in an increasingly data-centered world. To this end, only two introductory textbooks have incorporated some financial investment measures (Sharpe ratio and beta coefficient) in the teaching of numerical descriptive measures and simple linear regression analysis, respectively, while a few others include them as real-data application exercises at the end of their respective chapters. Although this latter coverage is in compliance with GAISE College Report recommendations on the importance of using relevant real data applications in the teaching of introductory business statistics, it forgoes an opportunity to provide more detailed discussion within the text itself and add value to a business student's learning. Given that all business students will have an opportunity to learn about financial investment, regardless of their functional area major, this paper offers a more proactive use of these and other financial investment measures as part of the current, traditional course or as part of a suggested dedicated introductory business statistics course for finance majors.

关键词： Alpha and beta coefficients Bootstrap percentile confidence interval Capital asset pricing model GAISE Permutation test Resampling method Sharpe ratio simulation-based inference Treynor ratio

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Detecting Model Misspecification in Amortized Bayesian inference with Neural Networks 45th

Detecting Model Misspecification in Amortized Bayesian Infer...

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45th Annual Conference of the German-Association-for-Pattern-Recognition (DAGM GCPR)

作者： Schmitt, Marvin Buerkner, Paul-Christian Koethe, Ullrich Radev, Stefan T. Univ Stuttgart Cluster Excellence SimTech Stuttgart Germany TU Dortmund Univ Dept Stat Dortmund Germany Heidelberg Univ Visual Learning Lab Heidelberg Germany Heidelberg Univ Cluster Excellence STRUCT Heidelberg Germany

ISBN: (纸本)9783031546044;9783031546051

Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation program (simulation-based inference;SBI). But how faithful is such inference if the simulation represents reality somewhat inaccurately-that is, if the true system behavior at test time deviates from the one seen during training? We conceptualize the types of model misspecification arising in SBI and systematically investigate how the performance of neural posterior approximators gradually deteriorates under these misspecifications, making inference results less and less trustworthy. To notify users about this problem, we propose a new misspecification measure that can be trained in an unsupervised fashion (i.e., without training data from the true distribution) and reliably detects model misspecification at test time. Our experiments clearly demonstrate the utility of our new measure both on toy examples with an analytical ground-truth and on representative scientific tasks in cell biology, cognitive decision making, and disease outbreak dynamics. We show how the proposed misspecification test warns users about suspicious outputs, raises an alarm when predictions are not trustworthy, and guides model designers in their search for better simulators.

关键词： simulation-based inference Model Misspecification Robustness

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Normal estimators for cointegrating relationships

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ECONOMICS LETTERS 1997年第2期55卷 185-189页

作者： Laroque, G Salanie, B INSEE CRESTPARISFRANCE CNRS PARISFRANCE

This paper proposes a strategy for estimating cointegrating relationships based on the first-differenced final form. We show that our procedure yields estimators of all parameters that are consistent and asymptoticall... 详细信息

关键词： time series cointegration simulation-based inference

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DIGS: deep inference of galaxy spectra with neural posterior estimation

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2022年第4期3卷 04LT04页

作者： Khullar, Gourav Nord, Brian Ciprijanovic, Aleksandra Poh, Jason Xu, Fei Fermilab Natl Accelerator Lab Batavia IL 60510 USA Univ Chicago Dept Astron & Astrophys Chicago IL 60637 USA Univ Chicago Kavli Inst Cosmol Phys Chicago IL 60637 USA MIT Kavli Inst Astrophys & Space Res 77 Massachusetts Ave Cambridge MA 02139 USA Univ Pittsburgh Dept Phys & Astron Pittsburgh PA 15260 USA Univ Pittsburgh PITT PACC Pittsburgh PA 15260 USA

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties;and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 x 10(6) spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI-with its combination of fast and amortized posterior estimations-is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9-1.2 x 10(5) galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

关键词： simulation-based inference neural posterior estimation galaxy evolution spectroscopy spectral energy distribution fitting deep learning sky surveys

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A hybrid stochastic model and its Bayesian identification for infectious disease screening in a university campus with application to massive COVID-19 screening at the University of Liege

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MATHEMATICAL BIOSCIENCES 2022年 347卷 108805-108805页

作者： Arnst, M. Louppe, G. Van Hulle, R. Gillet, L. Bureau, F. Denoel, V Univ Liege Fac Appl Sci B-4000 Liege Belgium Univ Liege FARAH Immunol Vaccinol B-4000 Liege Belgium Univ Liege GIGA Inst Lab Cellular & Mol Immunol B-4000 Liege Belgium

Amid the COVID-19 pandemic, universities are implementing various prevention and mitigation measures. Identifying and isolating infectious individuals by using screening testing is one such a measure that can contribute to reducing spread. Here, we propose a hybrid stochastic model for infectious disease transmission in a university campus with screening testing and its surrounding community. based on a compartmental modeling strategy, this hybrid stochastic model represents the evolution of the infectious disease and its transmission using continuous-time stochastic dynamics, and it represents the screening testing as discrete stochastic events. We also develop, in a Bayesian framework, the identification of parameters of this hybrid stochastic model, including transmission rates. These parameters were identified from the screening test data for the university population and observed incidence counts for the surrounding community. We implement the exploration of the Bayesian posterior using a machine-learning simulation-based inference approach. The proposed methodology was applied in a retrospective modeling study of a massive COVID-19 screening conducted at the University of Liege in Fall 2020. The emphasis of the paper is on the development of the hybrid stochastic model to assess the impact of screening testing as a measure to reduce spread. The hybrid stochastic model allows various factors to be represented and examined, such as interplay with the surrounding community, variability of the transmission dynamics, the rate of participation in the screening testing, the test sensitivity, the test frequency, the diagnosis delay, and compliance with isolation. The application in the retrospective modeling study suggests that a high rate of participation and a high test frequency are important factors to reduce spread.

关键词： Infectious disease transmission COVID-19 Screening testing Stochastic dynamics Bayesian inference simulation-based inference

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Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第1期5卷 015008页

作者： Jeffrey, Niall Wandelt, Benjamin D. UCL Dept Phys & Astron Gower St London England Sorbonne Univ CNRS IAP F-75014 Paris France Flatiron Inst Ctr Computat Astrophys 162 5th Ave New York NY USA

Evidence Networks can enable Bayesian model comparison when state-of-the-art methods (e.g. nested sampling) fail and even when likelihoods or priors are intractable or unknown. Bayesian model comparison, i.e. the computation of Bayes factors or evidence ratios, can be cast as an optimization problem. Though the Bayesian interpretation of optimal classification is well-known, here we change perspective and present classes of loss functions that result in fast, amortized neural estimators that directly estimate convenient functions of the Bayes factor. This mitigates numerical inaccuracies associated with estimating individual model probabilities. We introduce the leaky parity-odd power (l-POP) transform, leading to the novel 'l-POP-Exponential' loss function. We explore neural density estimation for data probability in different models, showing it to be less accurate and scalable than Evidence Networks. Multiple real-world and synthetic examples illustrate that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function. This simple yet powerful approach has broad implications for model inference tasks. As an application of Evidence Networks to real-world data we compute the Bayes factor for two models with gravitational lensing data of the Dark Energy Survey. We briefly discuss applications of our methods to other, related problems of model comparison and evaluation in implicit inference settings.

关键词： Bayesian model comparison deep learning simulation-based inference applications

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