Publications
Articles
"Detecting Pump-and-Dumps with Crypto-Assets: Dealing with Imbalanced Datasets and Insiders’ Anticipated Purchases"
(with Yufeng Xiao)
Econometrics, 11(3), 22, (2023)
Abstract: Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced datasets using resampling methods and flagging a pump-and-dump from the moment of public announcement up to 60 min beforehand. We validated our proposals using data from Pumpolymp and the CryptoCurrency eXchange Trading Library to identify 351 pump signals relative to the Binance crypto exchange in 2021 and 2022. We found that the most effective approach was using the original imbalanced dataset with pump-and-dumps flagged 60 min in advance, together with a random forest model with data segmented into 30-s chunks and regressors computed with a moving window of 1 h. Our analysis revealed that a better balance between sensitivity and specificity could be achieved by simply selecting an appropriate probability threshold, such as setting the threshold close to the observed prevalence in the original dataset. Resampling methods were useful in some cases, but threshold-independent measures were not affected. Moreover, detecting pump-and-dumps in real-time involves high-dimensional data, and the use of resampling methods to build synthetic datasets can be time-consuming, making them less practical.
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"Assessing the Credit Risk of Crypto-Assets Using Daily Range Volatility Models"
Information, 14(5), 254, (2023)
Abstract: In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of “dead coins” and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022.
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"Forecasting oil prices with penalized regressions, variance risk premia and Google data"
(with Maria Lycheva, Alexey Mironenkov, and Alexey Kurbatskii)
Applied Econometrics, 68, 28-49, (2022)
Abstract: This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil market. Our evidence shows that penalized regressions provided the best forecasting performances across most of the forecasting horizons. Moreover, we found that models using the VRP as an additional predictor performed best for forecasts up to 6–12 months ahead forecasts, while models using Google data as an additional predictor performed better for longer-term forecasts up to 12–24 months ahead. However, we found that the differences in forecasting performances were not statistically different for most models, and only the Principal Component Regression (PCR) and the Partial least squares (PLS) regression were consistently excluded from the set of best forecasting models. These results also held after a set of robustness checks that considered model specifications using a wider set of influential variables, a Hierarchical Vector Auto-Regression model estimated with the LASSO, and a set of forecasting models using a simplified specification for Google Trends data.
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"Using Crypto-Asset Pricing Methods to Build Technical Oscillators for Short-Term Bitcoin Trading"
Information, 13(12), 560, (2022)
Abstract: This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max draw-downs. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.
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"Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death"
Journal of Risk and Financial Management, 15(7), 304, (2022)
Abstract: This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice; alternative forecasting models, ranging from credit scoring models to machine learning and time-series-based models; and different forecasting horizons. We found that the choice of the coin-death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the cauchit and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit-scoring models and machine-learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins’ market capitalization.
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"Forecasting Internal Migration in Russia Using Google Trends:
Evidence from Moscow and Saint Petersburg"
(with Julia Pushchelenko, Alexey Mironenkov, and Alexey Kurbatskii)
Forecasting, 3(4), 774-804, (2021)
Abstract: This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors.
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"Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure"
(with Raffaella Calabrese )
Journal of Risk and Financial Management, 14(11), 516, (2021)
Abstract: While there is increasing interest in crypto assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we considered a unique dataset of 144 exchanges, active from the first quarter of 2018 to the first quarter of 2021. We analyzed the determinants surrounding the decision to close an exchange using credit scoring and machine learning techniques. Cybersecurity grades, having a public developer team, the age of the exchange, and the number of available traded cryptocurrencies are the main significant covariates across different model specifications. Both in-sample and out-of-sample analyzes confirm these findings. These results are robust in regard to the inclusion of additional variables, considering the country of registration of these exchanges and whether they are centralized or decentralized.
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"Asymmetry and hysteresis in the Russian gasoline market: The rationale for green energy exports"
(with Anna Kolesnikova)
Energy Policy, 157, 112466, (2021)
Abstract: Using monthly data of 79 Russian regions from 2003 to 2017, we study the long-run relationship of the retail gasoline prices with the crude oil price and the nominal exchange rate. We find that models that were successfully applied to deal with asymmetries in other countries are not suitable for Russia without taking structural breaks into account. Once breaks are allowed, we find that there is no asymmetry in the long-run elasticities between the gasoline prices and the crude oil price, and no significant hysteresis. However, there is an asymmetric relation between the gasoline price and the exchange rate that has decreased over time. These results also hold after several robustness checks. The evidence reported in this work shows that the effects of the exchange rate on gasoline prices are much more difficult to control than the oil price, and they require a larger set of policy measures: the recent development of a plan to decrease the importance of hydrocarbons exports by producing clean hydrogen using electrolysis and pyrolysis and the potential future export of electricity generated using nuclear power and onshore wind farms may help to diversify the local economy and to shield it from new sanctions.
Download the published version from Elsevier
Energy Policy is an international peer-reviewed journal addressing the policy implications of energy supply and use from their economic, social, planning and environmental aspects. In 2020 it was ranked among the top world journals in the economic sector, with an impact factor of 6.142.
"Forecasting and Backtesting of Market Risks in Emerging Markets"
RISK ASSESSMENT AND FINANCIAL REGULATION IN EMERGING MARKETS' BANKING,
Springer, p. 199-223, (2021)
Abstract: Emerging markets often go through periods of financial turbulence and the estimation of market risk measures may be problematic. Online search queries and implied volatility may (or may not) improve the model estimates. In these situations a step-by-step analysis with R and Russian market data is provided. Four classes of models are considered (GARCH, HAR, ARFIMA, and realized-GARCH), and a detailed forecasting and backtesting investigation is performed.
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"Discussing copulas with Sergey Aivazian: a memoir"
Model Assisted Statistics and Applications, 15(4), 363-370, (2020)
Abstract: Sergey Aivazian was the head of my department at the Moscow School of Economics, but he was much more than that. He played an important role in my life, and he contributed to my studies devoted to copula modelling. This small memoir reports how this amazingly polite and smart scientist helped me to develop my academic skills and to further stimulate my interest in multivariate modelling and risk management. Some open questions related to multivariate discrete models that were among the last topics I discussed with Sergey are reported, hoping they can be of interest to young researchers for further studies.
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"Does the Hashrate Affect the Bitcoin Price?"
(with Nikita Kolodin)
Journal of Risk and Financial Management, 13(11), 263, (2020)
Abstract: This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined subsample (01/08/2016–04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second subsample (11/12/2017–24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always unidirectional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from one week up to six weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to three months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.
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"Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries"
Applied Econometrics, 59, 33-54, (2020)
Abstract: The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample forecasting exercise with 18 competing models with a forecast horizon of 14 days ahead. This evidence shows that Google-augmented models outperform the competing models for most of the countries. This is significant because Google data can complement epidemiological models during difficult times like the ongoing COVID-19 pandemic, when official statistics maybe not fully reliable and/or published with a delay. Moreover, real-time tracking with online-data is one of the instruments that can be used to keep the situation under control when national lockdowns are lifted and economies gradually reopen.
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The supplementary materials can be found here
This paper is dedicated to the memory of my mother Maria Pirazzini, who passed away at Imola hospital in Italy on 16/07/2020:
Rest in peace.
"A multivariate approach for the simultaneous modelling of market risk and credit risk for cryptocurrencies"
(with Stephan Zimin)
Journal of Industrial and Business Economics, 47, 19-69 (2020)
Abstract: This paper proposes a set of models which can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously to estimate also their credit risk using the Zero Price Probability (ZPP) model by Fantazzini et al. (Comput Econ 31(2):161–180, 2008), which is a methodology to compute the probabilities of default using only market prices. For this purpose, both univariate and multivariate models with different specifications are employed. Two special cases of the ZPP with closed-form formulas in case of normally distributed errors are also developed using recent results from barrier option theory. A backtesting exercise using two datasets of 5 and 15 coins for market risk forecasting and a dataset of 42 coins for credit risk forecasting was performed. The Value-at-Risk and the Expected Shortfall for single coins and for an equally weighted portfolio were calculated and evaluated with several tests. The ZPP approach was used for the estimation of the probability of default/death of the single coins and compared to classical credit scoring models (logit and probit) and to a machine learning algorithm (Random Forest). Our results reveal the superiority of the t-copula/skewed-t GARCH model for market risk, and the ZPP-based models for credit risk.
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"The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades"
(with T. Shangina)
Applied Econometrics, 55, 5-31, (2019) .
Abstract: This paper focuses on the forecasting of market risk measures for the Russian RTS index future, and examines whether augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the estimated risk measures. We considered a time sample of daily data from 2006 till 2019, which includes several episodes of large-scale turbulence in the Russian future market. We found that the predictive power of several models did not increase if these two variables were added, but actually decreased. The worst results were obtained when these two variables were added jointly and during periods of high volatility, when parameters estimates became very unstable. Moreover, several models augmented with these variables did not reach numerical convergence. Our empirical evidence shows that, in the case of Russian future markets, TGARCH models with implied volatility and student’s t errors are better choices if robust market risk measures are of concern.
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The full color figure reporting the violations in % for all competing models using a rolling out-of-sample of 250 days,
for the forecasted VaR at the 1% probability level, can be found here.
"Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility"
(with T. Bazhenov)
Russian Journal of Industrial Economics, 12(1), 79-88, (2019)
Abstract: This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The out-of-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1 % probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.
Download the published version from here (open access)
"Big Data for computing social well-being indices of the Russian population"
(with Marina Shakleina, Natalia Yuras)
Applied Econometrics, 50, 43-66, (2018)
Abstract: The article builds indices of social well-being based on Google Trends Data for predicting VCIOM indices. The Google indices were computed using a Google Trends dataset for 2006–2016 containing 512 search queries relative to housing conditions, income, education, etc., and applying factor analysis. Bayesian Model Averaging was then used to select the indexes of individual social well-being mostly associated with the VCIOM indices which measure the social well-being of the Russian population. Additional regression models and forecasting exercises confirmed the previous results. Based on the Google Trends Data, the indices of the subjective social well-being are statistically reliable, as evidenced by a strong correlation between the observed and predicted values of the VCIOM indices.
Download the published version (in Russian) from Repec
"Everything you always wanted to know about bitcoin modelling but were afraid to ask" - Part 2
(with Erik Nigmatullin, Vera Sukhanovskaya, and Sergey Ivliev)
Applied Econometrics, 45, 5-28, (2017)
Abstract: This second part completes the consultation series dealing with bitcoin price modelling. Particularly, the analysis focuses on the econometric approaches suggested to model bitcoin price dynamics, the tests used for detecting the existence of financial bubbles in bitcoin prices and the methodologies suggested to study the price discovery at bitcoin exchanges.
Download the working paper version (in English) from Repec and SSRN
Download the published version (in Russian) from Repec
"The Oil Price Crash in 2014/15: Was There a (Negative) Financial Bubble?"
Energy Policy, 96, 383–396, (2016)
Abstract: This paper suggests that there was a negative bubble in oil prices in 2014/15, which decreased them beyond the level justified by economic fundamentals. This proposition is corroborated by two sets of bubble detection strategies: the first set consists of tests for financial bubbles, while the second set consists of the log-periodic power law (LPPL) model for negative financial bubbles. Despite the methodological differences between these detection methods, they provided the same outcome: the oil price experienced a statistically significant negative financial bubble in the last months of 2014 and at the beginning of 2015. These results also hold after several robustness checks which consider the effect of conditional heteroskedasticity, model set-ups with additional restrictions, longer data samples, tests with lower frequency data and with an alternative proxy variable to measure the fundamental value of oil.
Download the working paper version from Repec and SSRN
Download the published version from Elsevier
Energy Policy is an international peer-reviewed journal addressing the policy implications of energy supply and use from their economic, social, planning and environmental aspects. In 2015 it was ranked among the top world journals in the energy sector, with a 5-year impact factor of 3.701.
"Everything you always wanted to know about bitcoin modelling but were afraid to ask" - Part I
(with Erik Nigmatullin, Vera Sukhanovskaya, and Sergey Ivliev)
Applied Econometrics, 44, 5-24, (2016)
Abstract: Bitcoin is an open source decentralized digital currency and a payment system. It has raised a lot of attention and interest worldwide and an increasing number of articles are devoted to its operation, economics and financial viability. This article reviews the econometric and mathematical tools which have been proposed so far to model the bitcoin price and several related issues, highlighting advantages and limits. We discuss the methods employed to determine the main characteristics of bitcoin users, the models proposed to assess the bitcoin fundamental value, the econometric approaches suggested to model bitcoin price dynamics, the tests used for detecting the existence of financial bubbles in bitcoin prices and the methodologies suggested to study the price discovery at bitcoin exchanges.
Download the working paper version (in English) from Repec and SSRN
Download the published version (in Russian) from Repec
A simple Excel file replicating the example of the lower bound for the bitcoin price computed using the marginal cost of production based on electricity consumption
"Forecasting German Car Sales Using Google Data and Multivariate Models"
(with Zhamal Toktamysova)
International Journal of Production Economics, 170, 97-135, (2015)
Abstract: Long-term forecasts are of key importance for the car industry due to the lengthy period of time required for the development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales data using economic variables and Google online search data. An out-of-sample forecasting comparison with forecast horizons up to 2 years ahead was implemented using the monthly sales of ten car brands in Germany for the period from 2001M1 to 2014M6. Models including Google search data statistically outperformed the competing models for most of the car brands and forecast horizons. These results also hold after several robustness checks which consider nonlinear models, different out-of-sample forecasts, directional accuracy, the variability of Google data and additional car brands.
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The International Journal of Production Economics focuses on topics treating the interface between engineering and management. All aspects of the subject in relation to manufacturing and process industries, as well as production in general are covered. The journal is interdisciplinary in nature, considering whole cycles of activities, such as the product life cycle - research, design, development, test, launch, disposal - and the material flow cycle - supply, production, distribution In 2014 it was ranked among the top world journals in the business, economics and engineering sectors, with a 5-year impact factor of 3.069.
"Proposed Coal Power Plants and Coal-To-Liquids Plants in the US: Which Ones Survive and Why?"
(with M. Maggi)
Energy Strategy Reviews, 7, 9-17, (2015)
Abstract: The increase of oil and natural gas prices since the year 2000 stimulated the planning and construction of new coal-fired electricity generating plants and coal-to-liquids (CTL) plants in the US. However, many of these projects have been canceled or abandoned since 2007. Using a set of 145 proposed coal power plants and 25 CTL plants, the determinants that influence the decision to abandon a project or to proceed with it are examined using binary data models and 20 regressors. In the case of coal power plants, the number of searches performed on Google relating to coal power plants, the project duration and the prices of alternative fuels for electricity generation are found to be statistically significant at the 5% level. As for CTL plants, the political affiliation of the state governor is the only variable significant at the 5% level across several model specifications. An out-of-sample exercise confirms these findings. These results also hold with robustness checks considering alternative Google search keywords, the potential effects of the recession between 2008 and 2009 and the inclusion of the two dimensions of the Dynamic-Weighted Nominate (DWN) database.
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The technical appendix accompanying the paper can be found here
"Nowcasting and Forecasting the Monthly Food Stamps Data in the US
Using Online Search Data"
Plos One, 9(11), e11189, (2014)
Abstract: We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search data statistically outperform the competing models at all considered horizons. These results hold also with several robustness checks, considering alternative keywords, a falsification test, different out-of-samples, directional accuracy and forecasts at the state-level..
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The R code for the Range Unit Root test (RUR) and the Forward-Backward RUR test by Aparicio et al. (2006) can be found here
Plos One is an open-access peer-reviewed scientific journal published by the Public Library of Science (PLOS) since 2006. It covers primary research from any discipline within science and medicine. In 2013 it was ranked among the top world journals in the multi-disciplinary sector, with a 5-year impact factor of 3.534 .
International Journal of Computational Economics and Econometrics,
4(1-2), 1-3, (2014)
Editorial for the special issue on "Computational Methods for Russian Economic and Financial Modelling"
Download the published version here
The full issue can be found at Inderscience
"Long Memory and Periodicity in Intraday Volatility "
(with E. Rossi)
Journal of Financial Econometrics, 13(4), 922-961, (2015)
Abstract: Intraday return volatility is characterized by the contemporaneous presence of periodicity and long memory. This article proposes two new parameterizations of the intraday volatility process that account for both features: the Fractionally Integrated Periodic EGARCH and the Seasonal Fractional Integrated Periodic EGARCH. The analysis of hourly E-mini S&P 500 futures returns shows that the volatility is characterized by a statistically significant long-range dependence coupled with a periodic leverage effect, with negative return shocks having a larger effect on volatility during the US trading period. Long memory estimates obtained with nonperiodic long memory models are greater than those obtained with FI-PEGARCH and SFI-PEGARCH models. A simulation experiment shows that the long memory component can be strongly biased when periodic patterns are not properly modelled at the intraday level. An out-of-sample forecasting comparison with alternative models shows that a constrained version of the FI-PEGARCH provides superior forecasts.
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Download the published version from Oxford Journals
"Reviewing electricity production cost assessments "
(with S. Larsson, S. Davidsson, S. Kullander, and M. Höök)
Renewable & Sustainable Energy Reviews, 30, 170-183, (2014)
Abstract: A thorough review of twelve recent studies of production costs from different power generating technologies was conducted and a wide range in cost estimates was found. The reviewed studies show differences in their methodologies and assumptions, making the stated cost figures not directly comparable and unsuitable to be generalized to represent the costs for entire technologies. Moreover, current levelized costs of electricity methodologies focus only on the producer's costs, while additional costs viewed from a consumer perspective and on external costs with impact on society should be included if these results are to be used for planning. Although this type of electricity production cost assessments can be useful, the habit of generalizing electricity production cost figures for entire technologies is problematic. Cost escalations tend to occur rapidly with time, the impact of economies of scale is significant, costs are in many cases site-specific, and country-specific circumstances affect production costs. Assumptions on the cost-influencing factors such as discount rates, fuel prices and heat credits fluctuate considerably and have a significant impact on production cost results. Electricity production costs assessments similar to the studies reviewed in this work disregard many important cost factors, making them inadequate for decision and policy making, and should only be used to provide rough ballpark estimates with respect to a given system boundary. Caution when using electricity production cost estimates is recommended, and further studies investigating cost under different circumstances, both for producers and society as a whole are called for. Also, policy makers should be aware of the potentially widely different results coming from electricity production cost estimates under different assumptions.
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Download the published version from Elsevier
Renewable and Sustainable Energy Reviews is a peer-reviewed scientific journal covering research on sustainable energy. In 2013 it was ranked among the top five world journals in the energy sector, with a 5-year impact factor of 6.577.
International Journal of Computational Economics and Econometrics,
4(1-2), 4-31, (2014),
"Forecasting the Real Price of Oil Using Online Search Data" (with N. Fomichev)
Abstract: New models to forecast the real price of oil on the basis of macroeconomic indicators and Google search data are proposed. A large-scale out-of-sample forecasting analysis comparing the different models is performed. It is found that models including both Google data and macroeconomic aggregates statistically outperform the competing models in the short term, while multivariate models including only Google data perform best also for medium and long term forecasts up to 24 months ahead. This finding is confirmed by different robustness checks.
Download the published version from Inderscience
The R code for the Range Unit Root test (RUR) and the Forward-Backward RUR test by Aparicio et al. (2006) can be found here
"Hydrocarbon liquefaction: viability as a peak oil mitigation strategy "
(with M. Höök, A. Angelantoni and S. Snowden)
Philosophical Transactions of the Royal Society A , 372(2006), 1-36, (2014)
Abstract: Current world capacity of hydrocarbon liquefaction is around 400,000 barrels per day (kb/d), providing a marginal share of the global liquid fuel supply. This study performs a broad review of technical, economic, environmental, and supply chains issues related to coal-to-liquids (CTL) and gas-to-liquids (GTL). We find three issues predominate. First, significant amounts of coal and gas would be required to obtain anything more than a marginal production of liquids. Second, the economics of CTL plants are clearly prohibitive, but are better for GTL. Nevertheless, large scale GTL plants still require very high upfront costs, and for three real world GTL plants out of four, the final cost has been so far approximately three times that initially budgeted. Small scale GTL holds potential for associated gas. Third, CTL and GTL both incur significant environmental impacts, ranging from increased greenhouse gas emissions (in the case of CTL) to water contamination. Environmental concerns may significantly affect growth of these projects until adequate solutions are found .
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Download the published version from Royal Society Publishing
The Philosophical Transactions of the Royal Society is a scientific journal published by the Royal Society of London. It was established in 1665, making it the first journal in the world exclusively devoted to science, and it has remained in continuous publication ever since, making it the world's longest-running scientific journal. In 1887 the journal expanded and divided into two separate publications, one serving the Physical Sciences (Philosophical Transactions of the Royal Society A: Physical, Mathematical and Engineering Sciences) and the other focusing on the life sciences (Philosophical Transactions of the Royal Society B: Biological Sciences). In its formative years Isaac Newton had seventeen papers published in the journal including his first paper -New Theory about Light and Colours - which effectively served to launch his scientific career in 1672. Philosophical Transactions has also published the work of Charles Darwin, Michael Faraday, William Herschel and many more celebrated names in science. More information on the history of Philosophical Transactions can be found here and at Wikipedia. Nowadays, it is among the world top journals in the "Multidisciplinary sciences" category.
The slides of the presentation at the "Second international conference: GTL and CTL technologies 2014" can be found here
RETHINKING VALUATION AND PRICING MODELS, Elsevier, p. 241-255, (2013)
"Computing Reliable Default Probabilities in Turbulent Times " (with M. Maggi)
Download the published version from Elsevier
A excerpt can be found at Google Books
Abstract: In this paper, we compare different methods for computing default probabilities using a sample of banks that experienced financial distress during the 2007–2009 global financial crisis. The traditional KMV-Merton model for firm valuation, credit ratings by rating agencies and a recently proposed zero price probability model are discussed and compared. An empirical application with the acquired or bankrupt banks during the financial crisis is presented to show the differences among the three approaches and to discuss their suitability during financial distresses.
Table 1: Number of days before the default (or bailout) when the estimated PD was higher than 1%
Our analysis highlights the superiority of the ZPP model, for which the estimated PDs were higher than 1% in 19 out of 20 analyzed banks already more than 100 days before the default / bailout, while for 15 out of 20 banks this took place already more than 1 year in advance. On the opposite side of the spectrum, Moody's ratings and its implied PDs performed the worst, with only two (very small) banks which had the assigned (implied) PDs higher than 1% approximately 50 days before the credit event. Merton type models performed in between these two extreme cases. However, the Merton-GARCH model does much better than the classic Merton models with constant volatilities: for most of the considered banking stocks, the Merton-GARCH model delivered early warnings very close to the ZPP. The average number of days for the early warning is 403.8 for the ZPP model and 344.85 for the Merton-GARCH model, while it is only 88.4 for the classic Merton model and a poor 5.3 value for the Moody's rating system
"Everything You Always Wanted to Know about Log Periodic Power Laws for Bubble Modelling but Were Afraid to Ask " (with P. Geraskin)
European Journal of Finance, 19(5), 366-391, (2013)
Download the published version from Taylor and Francis
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Abstract: Sornette, Johansen, and Bouchaud (1996), Sornette and Johansen (1997), Johansen, Ledoit, and Sornette (2000) and Sornette (2003a) proposed that, prior to crashes, the mean function of a stock index price time series is characterized by a power law decorated with log-periodic oscillations, leading to a critical point that describes the beginning of the market crash. This article reviews the original log-periodic power law model for financial bubble modeling and discusses early criticism and recent generalizations proposed to answer these remarks. We show how to fit these models with alternative methodologies, together with diagnostic tests and graphical tools, to diagnose financial bubbles in the making in real time. An application of this methodology to the gold bubble which burst in December 2009 is then presented.
"Credit default swaps and CDS-bond basis with Russian companies: a review and an analysis of the effects of the short selling ban during the second great contraction"
Applied Econometrics, 25 (1), 3- 24, (2012)
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"Short Selling in Emerging Markets: A Comparison of Market Performance during the Global Financial Crisis " (with M. Maggi)
HANDBOOK OF SHORT SELLING, Elsevier, p. 339-352, (2012)
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The (short) official presentation slides can be found at Elsevier
"Short Selling in Russia: Main Regulations and Empirical Evidence from Medium- and Long-Term Portfolio Strategies " (with A. Kudrov, A. Zlotnik, and E. Dukhovnaya )
HANDBOOK OF SHORT SELLING, Elsevier, p. 387-400, (2012)
Download the published version from Elsevier
The (short) official presentation slides can be found at Elsevier
“Forecasting the Global Financial Crisis in the Years 2009-2010: Ex-post Analysis "
Economics Bulletin, 31(4), 3259-3267, (2011)
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Figure 1: SP500 ex-ante forecast, SP500 realized values, 95% and 99.9% confidence bands over the time sample 14/04/2009 - 29/04/2011. The vertical black line on the 27/08/2010 signals the day of the Chairman’s speech at Jackson Hole (Wyo., USA). Time t converted in units of one year (0 is set at Jan. 1st 2000).
The ex-ante forecast was made on the 14/04/2009, covering the time sample 14/04/2009 - 09/10/2010, and the paper containing it was submitted to the Economics Bulletin on the 15/05/2009 (submission number: EB-09-00287), and was later published in 2010 (see also below). Frankly speaking, this was the last ex-ante forecast that I did just before the paper submission: in reality, I started working on this topic in the summer 2008, when the parameters of the anti-bubble stabilized. In this regard, the parameters' stability is the key, both for understanding when the pattern is real and when it is finishing (for many people this is obvious, but for many others it represents -unfortunately- only an optional):
Figure 2: Recursive estimates of ω and β with the log-periodic equation (2) in the text, where t_last ranges from 2009/04/14 till 2011/04/29. The vertical black line on the 27/08/2010 signals the day of the FED Chairman’s speech at Jackson Hole (Wyo., USA). Time t converted in units of one year.
Clearly, the estimates of the key parameters changed completely after the Chairman's speech: however, it is interesting to note that already during the summer months June-August 2010 the estimates started wavering, highlighting that the end of the anti-bubble pattern was near.
"Global Oil Risks in the Early 21st Century"
(with M. Höök and A. Angelantoni)
Energy Policy , 39(12), 7865-7873, (2011)
Abstract: The Deepwater Horizon incident demonstrated that most of the oil left is deep offshore or in other difficult to reach locations. Moreover, obtaining the oil remaining in currently producing reservoirs requires additional equipment and technology that comes at a higher price in both capital and energy. In this regard, the physical limitations on producing ever-increasing quantities of oil are highlighted as well as the possibility of the peak of production occurring this decade. The economics of oil supply and demand are also briefly discussed showing why the available supply is basically fixed in the short to medium term. Also, an alarm bell for economic recessions is shown to be when energy takes a disproportionate amount of total consumer expenditures. In this context, risk mitigation practices in government and business are called for. As for the former, early education of the citizenry of the risk of economic contraction is a prudent policy to minimize potential future social discord. As for the latter, all business operations should be examined with the aim of building in resilience and preparing for a scenario in which capital and energy are much more expensive than in the business-as-usual one.
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"Small Sample Properties of Copula - GARCH Modelling: a Monte Carlo Study" (with C. Bianchi, M.E. DeGiuli, M. Maggi)
Applied Financial Economics, 21 (21), 1587 - 1597, (2011)
Download the published version from Taylor and Francis
Download the working paper version from SSRN (it contains the full set of plots not reported in the published version due to space limits)
"Моделирование Многомерных Распределений С Использованием Копула-Функций. Часть III" (Analysis of Multidimensional Probability Distributions with Copula Functions - Part 3)
Applied Econometrics, (Прикладная эконометрика) ,24 (4), 100 - 130, (2011)
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"Моделирование Многомерных Распределений С Использованием Копула-Функций - Часть II" (Analysis of Multidimensional Probability Distributions with Copula Functions - Part 2)
Applied Econometrics, (Прикладная эконометрика), 23 (3), 98 - 132, (2011)
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"МОДЕЛИРОВАНИЕ МНОГОМЕРНЫХ РАСПРЕДЕЛЕНИЙ С ИСПОЛЬЗОВАНИЕМ КОПУЛА-ФУНКЦИЙ. Часть I" (Analysis of multidimensional probability distributions with copula functions - Part 1)"
Applied Econometrics, (Прикладная эконометрика), 22 (2), 98 - 134, (2011)
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"Fractionally Integrated Models for Volatility: A Review "
Nonlinear Financial Econometrics: (Markov Switching Models, Persistence and Nonlinear Cointegration), Palgrave Macmillan, p. 104-123, (2011)
You can find the Abstract at SSRN
You can find the paper here at Springer
"The Intraday Analysis of Volatility, Volume and Spreads: A Review with Applications to Futures Markets "
FINANCIAL ECONOMETRICS MODELING : (MARKET MICROSTRUCTURE, FACTOR MODELS AND FINANCIAL RISK MEASURES), Palgrave Macmillan, p. 92-131, (2011)
You can find the Abstract at SSRN
You can find the chapter at Springer
“Modelling and Forecasting the Global Financial Crisis: Initial Findings using Heterosckedastic Log-Periodic Models "
Economics Bulletin, 30(3), 1833-1841, (2010)
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"Modelling Bubbles and Anti-Bubbles in Bear Markets: A Medium-Term Trading Analysis"
THE HANDBOOK OF TRADING, McGraw-Hill, p. 365-388, (2010)
You can find the Abstract at SSRN
You can find the book here at McGraw-Hill
A long excerpt can be found at Google Books
"A Copula-VAR-X approach for Industrial Production Modelling and Forecasting" (with C. Bianchi, A. Carta, M.E. Degiuli, M. Maggi – University of Pavia)
Applied Economics, 42(25), 3267-3277, (2010)
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Download the working paper version from SSRN
"Dangers and Opportunities for the Russian Banking Sector: 2007 - 2008" (with A. Kudrov and A. Zlotnik)
THE BANKING CRISIS HANDBOOK, Chapman & Hall / CRC Finance, p. 383-405, (2010)
You can find the Abstract at SSRN
You can find the book here at Chapman & Hall / CRC Finance
"Three-Stage Semi-parametric Estimation of T-Copulas: Asymptotics, Finite-Sample Properties and Computational Aspects"
Computational Statistics and Data Analysis, 54(11), 2562-2579 (2010)
Download the published version from Elsevier
Download the working paper version from SSRN (it contains the full set of tables, plots and proofs not reported in the published version due to space limits)
The presentation at the EEA-ESEM 2008 Milan, on August the 28th can be found at Scribd.com
"Small-Samples and EVT Estimators for Computing Risk Measures: Simulation and Empirical Evidences" (with A. Kudrov)
THE RISK MODELING EVALUATION HANDBOOK, McGraw-Hill, p. 339-361, (2010)
You can find the Abstract at SSRN
You can find the book here at McGraw-Hill
A long excerpt can be found at Google Books
"Copula-VAR and Copula-VAR-GARCH Modeling: Dangers for Value at Risk and Impulse Response Functions" (with C. Bianchi, M.E. DeGiuli and M. Maggi)
THE RISK MODELING EVALUATION HANDBOOK, McGraw-Hill, p. 321-338, (2010)
You can find the Abstract at SSRN
You can find the book here at McGraw-Hill
A long excerpt can be found at Google Books
"Discrete-Time Affine Term Structure Models: An ARCH Formulation" (with A. Carta, and M. Maggi)
International Journal of Risk Assessment and Management, 11(1/2), 164-179,(2009)
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Download the working paper from SSRN
"Market Risk Management for Emerging Markets: Evidence from the Russian Stock Market"
EMERGING MARKETS: PERFORMANCE, ANALYSIS AND INNOVATION, Chapman & Hall / CRC Finance, p. 533-554, (2009)
You can find the Abstract at SSRN
You can find the book here at Chapman & Hall / CRC Finance
A long excerpt can be found at Google Books
The presentation at the VII-th International School Seminar "Multivariate Statistical Analysis and Econometrics", Tsahkadzor, Armenia, September 24th 2008, can be found at Scribd.com
"Value at Risk for High-Dimensional Portfolios: A Dynamic Grouped-T Copula Approach"
The VAR IMPLEMENTATION HANDBOOK, McGraw-Hill, p. 253-282, (2009)
You can find the Abstract at SSRN
You can find the book here at McGraw-Hill
A long excerpt can be found at Google Books
The presentation at the International Workshop on Computational and Financial Econometrics, Geneva (Switzerland), April 20-22, 2007 can be found at Scribd.com
"Экономические факторы в модели голосования: пример Нидерландов, Великобритании и Израиля"
(with Alexei V. Zakharov , - HSE / MSE)
Applied Econometrics (Прикладная эконометрика), 2(14), 57-73 (2009)
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"Enhanced Credit Default Models for Heterogeneous SME Segments" (with M.E. DeGiuli, S. Figini, P. Giudici, – University of Pavia).
Journal of Financial Transformation, 25(1), 31-39, (2009)
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Download the working paper version from SSRN (it contains the full set of tables and plots not reported in the published version due to space limits)
"Эконометрический анализ финансовых данных в задачах управления риском.
Часть 5. Управление кредитным риском (окончание)"
Applied Econometrics (Прикладная эконометрика), 2(14), 100-127 (2009)
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"Эконометрический анализ финансовых данных в задачах управления риском.
Часть 4. Управление кредитным риском (продолжение)"
Applied Econometrics (Прикладная эконометрика), 1(13), 105-138 (2009)
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"Forecasting Default Probability without Accounting Data: Evidence from Russia"
STOCK MARKET VOLATILITY, Chapman & Hall / CRC, 527-548,(2009)
You can find the Abstract at SSRN
You can find the book here at Chapman & Hall / CRC
A long excerpt can be found at Google Books
"Default forecasting for small-medium enterprises: does heterogeneity matter?" (with S. Figini– University of Pavia).
International Journal of Risk Assessment and Management, 11(1/2), 138-163,(2009)
Download the published version from Inderscience
"The Effects of Misspecified Marginals and Copulas on Computing the Value at Risk: A Monte Carlo Study"
Computational Statistics and Data Analysis, 53(6), 2168-2188, (2009)
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Download the working paper version from SSRN (it contains the full set of tables not reported in the published version due to space limits)
"Multivariate Models for Operational Risk: A Copula Approach using Extreme Value Theory and Poisson Shock Models",
(with O. Rachedi, Cass Business School, London)
OPERATIONAL RISK TOWARD BASEL III: BEST PRACTICES AND ISSUES IN MODELING, MANAGEMENT, AND REGULATION, Wiley, 197-216, (2009).
You can find the Abstract at SSRN
You can find the book here at Wiley
A long excerpt can be found at Google Books
The presentation at HSE dealing with a general review of Operational Risk Management can be found at Scribd.com
"Random Survival Forest models for SME Credit Risk Measurement" (with S. Figini– University of Pavia).
Methodology and Computing in Applied Probability, 11(1), 29-45 (2009)
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Download the working paper version (before revision) from SSRN
" Управление кредитным риском "
Applied Econometrics (Прикладная эконометрика), 4(12), 84-137 (2008)
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" Управление операционным риском "
Applied Econometrics (Прикладная эконометрика), 3(11), 87-122 (2008)
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"Эконометрический анализ финансовых данных в задачах управления риском"
Applied Econometrics (Прикладная эконометрика), 2(10), 91-137 (2008)
Download the published version from Repec
“A New Approach for Firm Value and Default Probability Estimation beyond Merton Models”
Computational Economics, 31(2), 161-180, (2008)
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Download the working paper version from SSRN
“Dynamic Copulas for Value at Risk"
Frontiers in Finance and Economics, 5(2), 72-108, (2008)
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Download the working paper version from SSRN
"Empirical Studies with Operational Loss Data: DallaValle, Fantazzini and Giudici Study",
OPERATIONAL RISK: A GUIDE TO BASEL II CAPITAL REQUIREMENTS, MODELS, AND ANALYSIS, Wiley,
p. 274-277, (2007)
You can find the book here at Wiley
“Copulae and Operational Risks”,
(with L. Dalla Valle and P. Giudici – University of Pavia).
International Journal of Risk Assessment and Management, 9(3), 238-257,(2008)
Download the published version from Inderscience
Download the working paper from SSRN
“Leaves and Cigarettes: Modelling the Tobacco Industry (With applications to Italy and Greece)"
(with F. Arfini – University of Parma, F. Ferretti – Univesity of Reggio Emilia, K. Mattas – University of Thessaloniki), Book Review by Kenneth J. Thomson.
European Review of Agricultural Economics, 34 (1), 129-131, (2007)
Download the published version here.
“Evidence from a Time-Changing Regulated Agricultural Market: The Italian Tobacco Industry”
(with F. Ferretti - University of Modena and Reggio Emilia)
Agribusiness, Landscape and Environmental Management, 10(2), 1-13, (2007)
Download the published version here.
“The Econometric Modelling of Copulas: a Review with Extensions”,
(in) S.Co 2005, (edit by C. Provasi), p. 215-220
“Modelli Multivariati per la Gestione dei Rischi Operativi: L'approccio delle Copulae”
(i.e. "Multivariate Models for Operational Risk Modelling: The Copulae Approach")
Journal of the Italian Society of Financial Risk Management (AIFIRM), 2, 2 -10, (2005)
“Investment grade financial corporate bonds: Term structure estimation and relative value”
(with E. Bernini – Banca Intesa).
Capital Market Notes, Research Department, Banca Intesa, (January / March 2002)
“Term structure estimation and relative value for European financial names”
(with E. Bernini – Banca Intesa).
Credit market strategies, Research Department , Banca Intesa, (November 2001)
“Funzioni spline per la stima di strutture a termine: il caso dei corporate spread finanziari”, (i.e., “Spline functions for term structure estimation: The case of financial corporate spreads”)
(with E. Bernini – Banca Intesa).
Collana Ricerche, Studi e Ricerche, Banca Intesa, (September 2001)
pdf (Appendix VBA code)
Books
Quantitative Finance with R and Cryptocurrencies
Amazon KDP (2019), ISBN-13: 978-1090685315
The R scripts, the datasets used in the text, and any potential updates can be found on the book’s companion website
https://sites.google.com/view/quafirc ,
while the two R packages that accompany the book can be found here:
Методы эконометрикию Том 2: Эконометрика-2:продвинутый курс с приложениями в финансах
М.: Экономист (2014)
(Айвазян Сергей Артемьевич, Деан Фантаццини).
"Leaves and Cigarettes: Modelling the Tobacco Industry (With applications to Italy and Greece)"
Franco Angeli Editore (edited by F. Ferretti), November 2005:
(with F. Arfini – University of Parma, F. Ferretti – Univesity of Reggio Emilia, K. Mattas – University of Thessaloniki)
The published book can be found here or at Amazon.it.
Listed on SSRN's Top Ten download list for "ANRES: Other (Topic)" and "European Economics: Agriculture, Natural Resources & Environmental Studies".
"Financial Markets Microstructure and High Frequency Data: Theoretical Issues, Stylized Facts and Econometric Tools"
Digital University Press (November 2004)
The published book can be found here