INFORMATION ABOUT PROJECT,
SUPPORTED BY RUSSIAN SCIENCE FOUNDATION

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COMMON PART


Project Number20-18-00365

Project titleRobust methods and models for resilient markets and efficient production lines

Project LeadProkhorov Artem

AffiliationFederal State Budgetary Educational Institution of Higher Education "Saint-Petersburg State University",

Implementation period 2020 - 2022 

Research area 08 - HUMANITIES AND SOCIAL SCIENCES, 08-154 - Finance, credit, money circulation, market infrastructure

KeywordsFinancial and economic markets; insurance markets; robust statistical and econometric inference; dependent and heterogeneous data; heavy-tailed distributions; copula dependence models; econometric and mathematical modeling in finance, economics and actuarial science; multivariate statistical methods for economic analysis; stochastic frontier models; nonparametric estimators of production function; endogenous factors of production; stochastic frontier models with environmental factors


 

PROJECT CONTENT


Annotation
Fundamentally this project targets the development and application of mathematical methods suitable for modelling complex financial markets and production processes, affected by a large number of dependent and extreme random shocks. Examples of such markets and processes include the market for financial derivatives, including mortgage-backed, actuarial markets including reinsurance, and a large class of production processes where technical efficiency is determined by the so called environmental variables, e.g. in agriculture and oil/gas production. The data available to modern markets and firms -- financial, economic, actuarial -- exhibit highly complex dependence patterns, including extreme dependence at times of stress. This proposal presents novel, robust and reliable alternatives to the existing models and methods to analyse such data, expanding the knowledge base in the field of econometrics and, more generally, in the field of statistical inference as it applies to modern business and economic data. Importantly, the project will relax the unrealistic assumptions made in the traditional models of production and market behavior. The proposed research program is situated in the intersection of at least four disciplines: economics, finance and actuarial science, mathematics, and statistics. As an outcome, the project will produce a system of econometric models and estimation methods, accompanied by a freely available toolbox of prototype software. A related task is to create a world-class research laboratory in theoretical and applied econometrics at St.Petersburg State University. The most profound impact will likely take place in the field of econometrics, that is in the intersection of economics and mathematics, where the proposed research will generate a new generation of models and methods that reflect the realities of today's manufacturing and the sophistication of today's financial and actuarial products. To this end, the project will establish a productive international research network consisting of leading scientists who will consistently produce world-class research in the field of statistical and mathematical analysis and modeling of market's and firm's dynamic using a wide range of available statistical resources, including massive data sets. In this way, the proposal will meet the objectives of the Russian Science Foundation by enhancing international collaboration and expanding Russia's research capability.

Expected results
A key scientific outcome of this research project will be a series of papers published in leading Scopus-cited journals in the field, targeting those journals that have a 5-year impact factor of 2 and above, such as Journal of Econometrics. One practical benefit of this work for the Russian economy is that the novel methods will be used to achieve a better understanding and a smoother operation of Russian economic and financial markets, a higher productivity of Russian firms and industries. Unanticipated extreme movements in economic and financial markets have profound detrimental effects on real economy. The increased uncertainty usually slows down growth and reduces productivity. Similar effects follow when production decisions do not account for important dependencies of productivity on other factors than inputs. The new robust models and estimation methods this proposal develops provide means by which market participants can better assess such risks and minimize their effects. Correspondingly, the project will inform Russia's policy by building capacity in the analysis, management and prevention of extreme market behavior, ensuring smooth economic development, crucial for the long term prosperity of the Russian economy. The methodology developed in the project will have direct implications for statistical inference by providing robust and reliable alternatives to the existing methods. One of the main planned outcomes of the study is to provide a toolbox of statistical methodologies to be used in many areas of knowledge, beyond economics, finance and insurance. An appealing property of some of the inference procedures to be developed, especially in robust copula inference, is that they are easily accessible, among others, to graduate and undergraduate students and practitioners. It is planned that some of the results of the study will be included in the courses the research team members are currently teaching at respective universities. In addition, the project will involve collaborations with Ph.D. students, and open problems related to the project will be suggested to them as possible thesis topics. The research network of mid-career researchers that will emerge as a result of this research project will provide spill-over effects outside the outlined proposal and will have long term benefits. Overall, the project will encourage research and research training in a high-quality research environment and enhance international collaboration in research.


 

REPORTS


Annotation of the results obtained in 2020
In the reporting period, the project participants have conducted research in the main directions planned for 2020. In particular, they have conducted research in the following directions: 1. The development of new mathematical methods and models for analysis of complex markets. The new methods were used for econometric analysis of financial markets in the US and Asia as well as of the international art-market. 2. The development of new mathematical methods and models for analysis of complex production processes affected by a large number of dependent shocks. The new methods and models were used in the analysis of the US banking sector, agricultural and energy sectors. 3. The development of new mathematical and computational methods and algorithms for time series analysis. The new methods and algorithms are applicable to monitoring an electronic trading system in securities and to a generalization of the concept of cointegration of time series. 4.The development of new methods and models of production frontier analysis for oil and gas production, with application of Russian data. 5. The development of new methods of joint modelling of several dependent processes using copula functions, with application to the analysis of economic behavior of Russian secondary school students in terms of savings as connected with the level of financial literacy. 6. The development and application of new methods of network analysis as applied to the interaction between businesses present on the largest Russian social media network VKontakte. 7. The development and application of new machine learning methods to the analysis of price formation on the art-market as well as to the generation of titles for objects of art. 8. The development of a simulation model of a security exchange using a multi-agent framework of interaction of players. 9. The study of the effect from the introduction of restrictive measures on the performance of museums, art galleries and other actors of the art-market in St.Petersburg using methods of econometric analysis. 10. The development of an improved system of Multi-Armed Bandits and its application to tasks in business analytics, marketing and management. 11. Integration of the development methods and models into statistical and econometrics software packages and provision of software prototypes to public domain.

 

Publications

1. Prokhorov, A, Kien Tran and Mike Tsionas Estimation of Semi- and Nonparametric Stochastic Frontier Models with Endogenous Variables Empirical Economics, - (year - 2020).

2. Amsler Ch, Prokhorov A, Schmidt P A New Family of Copulas, with Application to Estimation of a Production Frontier System Journal of Productivity Analysis, - (year - 2020).

3. Liu D, Hirukawa M, Prokhorov A msreg: A STATA Command for Consistent Estimation of Linear Regression Models Using Matched Data The Stata Journal, - (year - 2020).