Imperial College London

ProfessorRustamIbragimov

Business School

Professor of Finance and Econometrics
 
 
 
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Contact

 

+44 (0)20 7594 9344i.rustam Website CV

 
 
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Location

 

40953 Prince's GateSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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77 results found

Mansurov K, Semenov A, Grigoriev D, Radionov A, Ibragimov Ret al., 2024, Cryptocurrency Exchange Simulation, Computational Economics, ISSN: 0927-7099

In this paper, we consider the approach of applying state-of-the-art machine learning algorithms to simulate some financial markets. In this case, we choose the cryptocurrency market based on the assumption that such markets more active today. As a rule, they have more volatility, attracting riskier traders. Considering classic trading strategies, we also introduce an agent with a self-learning strategy. To model the behavior of such agent, we use deep reinforcement learning algorithms, namely Deep Deterministic policy gradient. Next, we develop an agent-based model with following strategies. With this model, we will be able to evaluate the main market statistics, named stylized-facts. Finally, we conduct a comparative analysis of results for constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. As a result, we conclude that our model with a self-learning agent gives a better approximation to the real market than a model with classical agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern stock markets.

Journal article

Mansurov K, Semenov A, Grigoriev D, Radionov A, Ibragimov Ret al., 2023, Impact of self-learning based high-frequency traders on the stock market, Expert Systems with Applications, Vol: 232, Pages: 1-17, ISSN: 0957-4174

In this paper we investigate the role of self-learning agents in multi-agent models of financial markets. We develop an agent-based simulation model of a financial market and, in addition to the agents with fixed strategies used in previous research, we introduce an agent with a self-learning strategy. To model the behavior of such an agent, we use deep reinforcement learning algorithms, namely deep deterministic policy gradient (DDPG). Next, we conduct a comparative analysis of the results of the constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. To conduct comparative analysis, we use stylized facts of asset returns that allow us to evaluate and compare the characteristics of the markets. Our results show that a model with a self-learning agent gives a better approximation of the real market than a model with classic agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern financial markets.Finally, we present the python package1, which was developed by us as part of the research implementation. This package allows to simulate the financial market, as well as create your own agents and evaluate their impact on the market.

Journal article

Ibragimov R, Pedersen RS, Skrobotov A, 2023, New approaches to robust inference on market (non-)efficiency, volatility clustering and nonlinear dependence, Journal of Financial Econometrics, Pages: 1-23, ISSN: 1479-8409

We present novel, robust methods for inference on market (non-)efficiency, volatility clustering, and nonlinear dependence in financial return series. In contrast to existing methodology, our proposed methods are robust against nonlinear dynamics and tail-heaviness of returns. Specifically, our methods only rely on return processes being stationary and weakly dependent (mixing) with finite moments of a suitable order. This includes robustness against power-law distributions associated with nonlinear dynamic models such as GARCH and stochastic volatility. The methods are easy to implement and perform well in realistic settings. We revisit a recent study by Baltussen, van Bekkum, and Da (2019, J. Financ. Econ., 132, 26–48) on autocorrelation in major stock indexes. Using our robust methods, we document that the evidence of the presence of negative autocorrelation is weaker, compared with the conclusions of the original study.

Journal article

Kim J, Skrobotov A, Ibragimov R, 2023, New robust inference for predictive regressions, Econometric Theory, ISSN: 0266-4666

Journal article

Xing Z, Ibragimov R, 2023, A market crash or tail risk? Heavy tails and asymmetry of returns in the Chinese Stock Market, Advances in Econometrics, Vol: 45B, Pages: 181-205, ISSN: 0731-9053

Rapid stock market growth without real economic back-up has led to the 2015 Chinese Stock Market Crash with thousands of stocks hitting the down limit simultaneously multiple times. We provide a detailed analysis of structural breaks in heavy-tailedness and asymmetry properties of returns in Chinese A-share markets due to the crash using recently proposed robust approaches to tail index inference. The empirical analysis points out to heavy-tailedness properties often implying possibly infinite second moments and gain/loss asymmetry for daily returns on individual stocks. We further present an analysis of the main determinants of heavy-tailedness in Chinese financial markets. It points out to liquidity and company size as being the most important factors affecting the returns’ heavy-tailedness properties. At the same time, we do not observe statistically significant differences in tail indices of the returns on A-shares and the coefficients on factors affecting them in the pre-crisis and post-crisis periods.

Journal article

Distaso W, Ibragimov R, Semenov A, Skrobotov Aet al., 2022, COVID-19: tail risk and predictive regressions, PLoS One, Vol: 17, Pages: 1-13, ISSN: 1932-6203

The paper focuses on econometrically justified robust analysis of the effects of the COVID-19 pandemic on financial markets in different countries across the World. It provides the results of robust estimation and inference on predictive regressions for returns on major stock indexes in 23 countries in North and South America, Europe, and Asia incorporating the time series of reported infections and deaths from COVID-19. We also present a detailed study of persistence, heavy-tailedness and tail risk properties of the time series of the COVID-19 infections and death rates that motivate the necessity in applications of robust inference methods in the analysis. Econometrically justified analysis is based on heteroskedasticity and autocorrelation consistent (HAC) inference methods, recently developed robust t-statistic inference approaches and robust tail index estimation.

Journal article

He S, Ibragimov R, 2022, Predictability of cryptocurrency returns: evidence from robust tests, Dependence Modeling, Vol: 10, Pages: 191-206, ISSN: 2300-2298

The paper provides a comparative empirical study of predictability of cryptocurrency returns and prices using econometrically justified robust inference methods. We present robust econometric analysis of predictive regressions incorporating factors, which were suggested by Liu, Y., & Tsyvinski, A. (2018). Risks and returns of cryptocurrency. NBER working paper no. 24877; Liu, Y., & Tsyvinski, A. (2021). Risks and returns of cryptocurrency. The Review of Financial Studies, 34(6), 2689–2727, as useful predictors for cryptocurrency returns, including cryptocurrency momentum, stock market factors, acceptance of Bitcoin, and Google trends measure of investors’ attention. Due to inherent heterogeneity and dependence properties of returns and other time series in financial and crypto markets, we provide the analysis of the predictive regressions using both heteroskedasticity and autocorrelation consistent (HAC) standard-errors and also the recently developed t -statistic robust inference approaches, Ibragimov, R., & Müller, U. K. (2010). t-statistic based correlation and heterogeneity robust inference. Journal of Business and Economic Statistics, 28, 453–468; Ibragimov, R., & Müller, U. K. (2016). Inference with few heterogeneous clusters. Review of Economics and Statistics, 98, 83–96. We provide comparisons of robust predictive regression estimates between different cryptocurrencies and their corresponding risk and factor exposures. In general, the number of significant factors decreases as we use more robust t-tests, and the t-statistic robust inference approaches appear to perform better than the t-tests based on HAC standard errors in terms of pointing out interpretable economic conclusions. The results in this paper emphasize the importance of the use of robust inference approaches in the analysis of economic and financial data affected by the problems of heterogeneity and dependence.

Journal article

Huang Z, Ibragimov R, 2022, Equity returns and sentiment, Dependence Modeling, Vol: 10, Pages: 159-176, ISSN: 2300-2298

This paper analyzes approximately 100 Gigabytes of raw text data from Twitter with keywords “AAPL,” “S&P 500,” “FTSE100” and “NASDAQ” to explore the relationship between sentiment and the returns and prices on the Apple stock and the S&P 500, FTSE 100 and NASDAQ indices. The findings point to significant relationship and dependence between sentiment measures and the S&P 500 and FTSE 100 indices’ returns and prices. The econometric analysis of dependence between the aforementioned variables in the paper is presented in some detail for illustration of the methodology employed.

Journal article

Brown D, Ibragimov R, 2019, Sign tests for dependent observations, Econometrics and Statistics, Vol: 10, Pages: 1-8, ISSN: 2452-3062

New sign tests for testing equality of conditional distributions of two (arbitrary) adapted processes as well as for testing conditionally symmetric martingale-difference assumptions are introduced. The analysis is based on results that demonstrate that randomization over ties in sign tests for equality of conditional distributions of two adapted sequences produces a stream of i.i.d. symmetric Bernoulli random variables. This reduces the problem of evaluating the critical values of the tests to computing the quantiles or moments of Binomial or normal distributions. Similar properties also hold under randomization over zero values of signs of a conditionally symmetric martingale-difference sequence.

Journal article

Chen Z, Ibragimov R, 2019, One country, two systems? The heavy-tailedness of Chinese A- and H- share markets, Emerging Markets Review, Vol: 38, Pages: 115-141, ISSN: 1566-0141

Chinese A- and H– share markets operate in different institutional environments (emerging/developing v.s. developed) and thus may have different tail risk properties. This paper focuses on the analysis of heavy-tailedness properties of these two markets using recently developed robust inference methods. The equality of tail indices of returns for A and H dual-listed companies cannot be rejected, and some A- and H– share returns may have infinite second moments. Their heavy-tailedness properties did not change significantly with respect to the 2008 financial crisis and the date when the corresponding company starts to be dual-listed.

Journal article

Ibragimov M, Ibragimov R, Kattuman P, Ma Jet al., 2018, Income inequality and price elasticity of market demand: the case of crossing Lorenz curves, Economic Theory, Vol: 65, Pages: 729-750, ISSN: 0938-2259

This paper extends Ibragimov and Ibragimov (Econ Theory 32:579–587,2007) in which the effect of changes income inequality on the price elasticity ofmarket demand is characterized for the class of income distribution changes occurringthrough non-intersecting Lorenz curve shifts. We derive sufficient conditionsfor increase/decrease in price elasticity of market demand, under general changes inincome distribution, allowing Lorenz curves to intersect as they shift. We conclude bydrawing out implications of different types of tax policy changes for demand elasticity.

Journal article

Gu Z, Ibragimov R, 2018, The "Cubic Law of the Stock Returns" in emerging markets, JOURNAL OF EMPIRICAL FINANCE, Vol: 46, Pages: 182-190, ISSN: 0927-5398

Excess volatility in main emerging and developed stock markets is carefully analysed in this study. Tail distribution of returns of both stock market index and individual stocks is evaluated and compared with the theoretical distribution found by Gabaix et al. (2003, 2006). For stock market index, recursive and rolling estimation are used. In recursive estimation, we find that all the developed markets obey “the Cubic Law of the Stock Returns”, while most of the emerging countries exhibit heavier tail with a tail index lower than 3 at 95% significance level. In rolling estimation, the tail index in the developed markets does not stabilise around 3, and after 2008 financial crisis, all the developed markets and most emerging ones suffer a drop in the tail index. For individual stocks, the tail distributions of stock returns, trading volume, and the number of trades in each emerging country behave quite differently from the theoretical model by Gabaix et al. (2006), especially the stock returns.

Journal article

Ibragimov R, Jaffee D, Walden J, 2018, Equilibrium with monoline and multiline structures, Review of Finance, Vol: 22, Pages: 595-632, ISSN: 1573-692X

We study a competitive market for risk-sharing, in which risk-tolerant providers of risk protection, who face frictional costs in holding capital, offer coverage over a range of risk classes to risk-averse agents. We distinguish monoline and multiline industry structures and characterize when each structure is optimal. Markets for which the risks are limited in number, asymmetric or correlated will be served by monoline structures, whereas markets characterized by a large number of essentially independent risks will be served by many multiline firms. Our results are consistent with observed structures within insurance, and also have general implications for the financial services industry.

Journal article

Ankudinov A, Ibragimov R, Lebedev O, 2017, Heavy tails and asymmetry of returns in the Russian stock market, Emerging Markets Review, Vol: 32, Pages: 200-219, ISSN: 1566-0141

The paper presents the robust estimates of tail indices for financial returns and returns asymmetry in the Russian stock market. We also investigate the relation between individual characteristics of companies and the degree of heavy-tailedness and asymmetry of returns. According to our estimates, the degree of heavy-tailedness is strongly related to the liquidity of stocks and the company size. At the same time, no significant effects on estimates of the tail indices of sectoral affiliation, cross-listing, adding into quotation lists, state ownership are revealed. As for the influence of the above-mentioned factors on the asymmetry of returns, the statistical reliability of relevant models is rather low. However, certain indicators of the asymmetry are observed for medium-sized regional companies, the majority showing a heavier right tail compared to the left tail. We also discuss the implications of our findings for managerial decisions and economic modeling. Our results may be useful for risk-managers, financial regulators, investors and policy-makers.

Journal article

Pinelis I, Peña VHDL, Ibragimov R, Shevtsova I, Osȩkowski AOet al., 2017, Inequalities and Extremal Problems in Probability and Statistics, Publisher: Academic Press, ISBN: 9780128098189

The book enables the reader to grasp the importance of inequalities and how they relate to probability and statistics.

Book

Pinelis I, de la Peña VH, Ibragimov R, Osȩkowski A, Shevtsova Iet al., 2017, Inequalities and extremal problems in probability and statistics: Selected topics, ISBN: 9780128098189

Inequalities and Extremal Problems in Probability and Statistics: Selected Topics presents various kinds of useful inequalities that are applicable in many areas of mathematics, the sciences, and engineering. The book enables the reader to grasp the importance of inequalities and how they relate to probability and statistics. This will be an extremely useful book for researchers and graduate students in probability, statistics, and econometrics, as well as specialists working across sciences, engineering, financial mathematics, insurance, and mathematical modeling of large risks.

Book

Ibragimov M, Ibragimov R, 2017, Heavy tails and upper-tail inequality: the case of Russia., Empirical Economics, Vol: 54, Pages: 823-837, ISSN: 0377-7332

Motivated, in part, by the recent surge of interest in robust inequality measurement, cross-country inequality comparisons, applications of heavy-tailed distributions and the study of global and upper-tail inequality, this paper focuses on robust analysis of heavy-tailedness properties and inequality in the upper tails of income distribution in Russia, as measured, mainly, by its tail indices. The study is based on recently developed approaches to robust inference on the degree of heavy-tailedness and their implications for the analysis of upper-tail inequality discussed in the paper. Among other results, the paper provides robust estimates of heavy-tailedness parameters and tail indices for Russian income distribution and their comparisons with the benchmark values in developed economies reported in the previous literature. The estimates point out to important similarity between heavy-tailedness properties of income distribution and their implications for the analysis of upper-tail income inequality in Russia and those in developed markets.

Journal article

Ankudinov A, Ibragimov R, Lebedev O, 2017, Sanctions and the Russian stock market, Research in International Business and Finance, Vol: 40, Pages: 150-162, ISSN: 0275-5319

The article presents the robust estimates of extreme movements and heavy-tailedness properties for Russian stock indices returns before and after sanctions were introduced. The obtained results show that almost for all sectoral indices there was a statistically significant increase in volatility. At the same time there is not enough evidence of structural breaks in heavy-tailedness, though some indications of heavier both right and left tails in the post-imposition period can be observed for some indices. However, we cannot with complete certainty directly link the increase in heavy-tailedness with the imposed sanctions. The latter to a considerable extent could be caused by higher country-specific risks due to geopolitical tensions as well as oil prices volatility. Whatever is the cause, any increases in heavy-tailedness can have grave consequences for corporate management, economic modeling and financial stability analysis.

Journal article

Ibragimov R, Prokhorov A, 2017, Heavy Tails and Copulas

Journal article

Ibragimov M, Ibragimov R, 2017, Unemployment and output dynamics in CIS countries: Okun's law revisited, Applied Economics, Vol: 49, Pages: 3453-3479, ISSN: 0003-6846

Okun’s law is a well-known relationship between the change in the unemployment rate and output growth. The main objective of this article is to provide a rigorous econometric analysis of Okun’s law for several CIS countries using different models and theoretically justified econometric methods. The traditional approach to Okun’s law estimation using OLS regressions does not account for possible endogeneity of regressors and the implied inconsistency of the estimates obtained. These problems point out to incorrectness of applications of the standard OLS estimation techniques. Our study addresses these issues by using econometrically justified instrumental variable regression methods. The article provides the results and discussions on practical use of Okun’s relationships for evaluation of average effects of economic growth on the unemployment rate, and vice versa; importance of accounting for confidence intervals in applications of Okun’s models to economic development analysis and cross-country comparisons and evaluation of effects of crises and other structural shocks on the economies considered. We also discuss in detail the results of formal econometric tests and economic motivation for validity of instrumental variables used in the study. The formal econometric tests, together with economic arguments, allow us to determine the most appropriate Okun-type models for each of the CIS countries under consideration.

Journal article

Ankudinov A, Ibragimov R, Lebedev O, 2017, Extreme movements of the Russian stock market and their consequences for management and economic modeling, Applied Econometrics, Vol: 45, Pages: 75-92, ISSN: 1993-7601

The article presents the results of testing the degree of heavy-Tailedness for Russian companies' returns distributions. The estimation is based on the robust approach of log-log rank-size regressions with the optimal shift and correct standard errors. The obtained results indicate that the Russian stock market is somewhat more prone to extreme movements when compared to those of the developed as well as some developing countries. In certain cases such behavior may lead to unreliability of standard statistical methods based on variance and correlations, to negative value of portfolio diversification as well as to unreliability of some popular risk-management techniques. The obtained results may be also relevant for macroeconomic forecasting.

Journal article

Ibragimov R, Lentzas G, 2017, Copulas and long memory, Probability Surveys, Vol: 14, ISSN: 1549-5787

Journal article

Ibragimov R, Prokhorov A, 2017, Heavy Tails and Copulas: Topics in Dependence Modelling in Economics and Finance, Publisher: World Scientific, ISBN: 978-981-4689-79-3

This book offers a unified approach to the study of crises, large fluctuations, dependence and contagion effects in economics and finance. It covers important topics in statistical modeling and estimation, which combine the notions of copulas and heavy tails — two particularly valuable tools of today's research in economics, finance, econometrics and other fields — in order to provide a new way of thinking about such vital problems as diversification of risk and propagation of crises through financial markets due to contagion phenomena, among others. The aim is to arm today's economists with a toolbox suited for analyzing multivariate data with many outliers and with arbitrary dependence patterns. The methods and topics discussed and used in the book include, in particular, majorization theory, heavy-tailed distributions and copula functions — all applied to study robustness of economic, financial and statistical models, and estimation methods to heavy tails and dependence.

Book

de la Pena VH, Ibragimov R, 2017, Sharp Probability Inequalities for Random Polynomials, Generalized Sample Cross-Moments, and Studentized Processes, INEQUALITIES AND EXTREMAL PROBLEMS IN PROBABILITY AND STATISTICS: SELECTED TOPICS, Editors: Pinelis, Publisher: ACADEMIC PRESS LTD-ELSEVIER SCIENCE LTD, Pages: 159-187, ISBN: 978-0-12-809818-9

Book chapter

Ibragimov R, Prokhorov A, 2016, Heavy tails and copulas: limits of diversification revisited, Economics Letters, Vol: 149, Pages: 102-107, ISSN: 0165-1765

We show that diversification does not reduce Value-at-Risk for a large class of dependent heavy tailed risks. The class is characterized by power law marginals with tail exponent no greater than one and by a general dependence structure which includes some of the most commonly used copulas.

Journal article

Ibragimov R, Müller UK, 2016, Inference with few heterogeneous clusters, Review of Economics and Statistics, Vol: 98, Pages: 83-96, ISSN: 0034-6535

Suppose estimating a model on each of a small number of potentially heterogeneous clusters yields approximately independent, unbiased, and Gaussian parameter estimators. We make two contributions in this setup. First, we show how to compare a scalar parameter of interest between treatment and control units using a two-sample t-statistic, extending previous results for the one-sample t-statistic. Second, we develop a test for the appropriate level of clustering; it tests the null hypothesis that clustered standard errors from a much finer partition are correct. We illustrate the approach by revisiting empirical studies involving clustered, time series, and spatially correlated data.

Journal article

Brown DJ, Ibragimov R, Walden J, 2015, Bounds for path-dependent options, Annals of Finance, Vol: 11, Pages: 433-451, ISSN: 1614-2446

We develop new semiparametric bounds on the expected payoffs and prices of European call options and a wide range of path-dependent contingent claims. We first focus on the trinomial financial market model in which, as is well-known, an exact calculation of derivative prices based on no-arbitrage arguments is impossible. We show that the expected payoff of a European call option in the trinomial model with martingale-difference log-returns is bounded from above by the expected payoff of a call option written on an asset with i.i.d. symmetric two-valued log-returns. We further show that the expected payoff of a European call option in the multiperiod trinomial option pricing model is bounded by the expected payoff of a call option in the two-period model with a log-normal asset price. We also obtain bounds on the possible prices of call options in the (incomplete) trinomial model in terms of the parameters of the asset’s distribution. Similar bounds also hold for many other contingent claims in the trinomial option pricing model, including those with an arbitrary convex increasing payoff function as well as for path-dependent ones such as Asian options. We further obtain a wide range of new semiparametric moment bounds on the expected payoffs and prices of path-dependent Asian options with an arbitrary distribution of the underlying asset’s price. These results are based on recently obtained sharp moment inequalities for sums of multilinear forms and U-statistics and provide their first financial and economic applications in the literature. Similar bounds also hold for many other path-dependent contingent claims.

Journal article

Ibragimov M, Ibragimov R, Walden J, 2015, Heavy-Tailed distributions and robustness in economics and finance, Lecture Notes in Statistics, Pages: 1-119

Book chapter

Ibragimov M, Ibragimov R, Walden J, 2015, Preface, Pages: ix-xi, ISSN: 0930-0325

Conference paper

Embrechts P, 2015, Inference and Empirical Examples, HEAVY-TAILED DISTRIBUTIONS AND ROBUSTNESS IN ECONOMICS AND FINANCE, Publisher: SPRINGER, Pages: 83-109, ISBN: 978-3-319-16876-0

Book chapter

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