Genaro Sucarrat is tenured associate professor of econometrics at the department of economics. He studied economics and politics (Cand.Mag. in economics, politics and philosopy at the University of Oslo, MA in international political economy at the University of Warwick) before obtaining an MA and a PhD in Economics at Universite Catolique de Louvain. After his doctoral studies, he worked a total of four years as a Marie Curie individual fellow and visiting professor in economics at Universidad Carlos III de Madrid, before joining BI Norwegian Business School. In addition, he has spent several research visits at other institutions, including University of Cambridge, University of Oxford, CREST (Paris), Universite de Lille, and Pontificia universidad catolica de Chile.
Sucarrat's research has been published in international peer-reviewed journals like Journal of Financial Econometrics, Journal of Multivariate Analysis, Oxford Bulletin of Economics and Statistics, International Journal of Forecasting, Computational Statistics and Data Analysis, Energy Economics, The European Journal of Finance, Journal of Economic Methodology, Journal of Statistical Software and The R journal. He has also developed several R packages (statistical software), available via the Comprehensive R-Archive Network (CRAN), which has been downloaded more than 200 000 times from the 0-cloud server of RStudio. Since 2010 he has been responsible for a forecasting prize ("Prognoseprisen") of the Norwegian Association of Economists, which is awarded every year to the best forecaster of the Norwegian economy.
Research areas Econometric modelling and forecasting; computational econometrics; empirical finance and macroeconomics;
research methodology and philosophy
Teaching areas DRE 7008 Advanced Statistics
MET 3590 Metode og statistisk dataanalyse
Recent studies show that the zero-process of observed intraday financial returns is frequently characterized by non-stationary periodicity. As liquidity varies across the trading day, not only does unconditional volatility change, but also the unconditional zero-probability. While scaling returns by the time-varying intraday volatility may stabilize volatility, it does not make the zero-process of scaled returns stationary. This invalidates standard methods of risk estimation, and recent studies document that the use of such invalid methods can have major effects on risk estimates. Formal tests for nonstationary periodicity in the zero-process can therefore be of great value in guiding the choice of a suitable risk estimation procedure. Despite this, little attention has been devoted to the derivation of such tests. Here, we help filling this gap by developing user-friendly yet flexible and powerful tests that hold under mild assumptions. Next, our empirical study reveals that intraday financial returns are widely characterized by non-stationary periodicity in the zero-process. This has important and potentially wide-ranging implications for future research
Campos-Martins, Susana & Sucarrat, Genaro (2024)
Modeling Nonstationary Financial Volatility with the R Package tvgarch
Certain events can make the structure of volatility of financial returns to change, making it nonstationary. Models of time-varying conditional variance such as generalized autoregressive conditional heteroscedasticity (GARCH) models usually assume stationarity. However, this assumption can be inappropriate and volatility predictions can fail in the presence of structural changes in the unconditional variance. To overcome this problem, in the time-varying (TV-)GARCH model, the GARCH parameters are allowed to vary smoothly over time by assuming not only the conditional but also the unconditional variance to be time-varying. In this paper, we show how useful the R package tvgarch (Campos-Martins and Sucarrat 2023) can be for modeling nonstationary volatility in financial empirical applications. The functions for simulating, testing and estimating TV-GARCH-X models, where additional covariates can be included, are implemented in both univariate and multivariate settings.
The garchx package provides a user-friendly, fast, flexible, and robust framework for the estimation and inference of GARCH(p, q, r)-X models, where p is the ARCH order, q is the GARCH order, r is the asymmetry or leverage order, and ’X’ indicates that covariates can be included. Quasi Maximum Likelihood (QML) methods ensure estimates are consistent and standard errors valid, even when the standardized innovations are non-normal or dependent, or both. Zero-coefficient restrictions by omission enable parsimonious specifications, and functions to facilitate the non-standard inference associated with zero-restrictions in the null-hypothesis are provided. Finally, in the formal comparisons of precision and speed, the garchx package performs well relative to other prominent GARCH-packages on CRAN.
Francq, Christian & Sucarrat, Genaro (2021)
Volatility Estimation When the Zero-Process is Nonstationary
Financial returns are frequently nonstationary due to the nonstationary distribution of zeros. In daily stock returns, for example, the nonstationarity can be due to an upwards trend in liquidity over time, which may lead to a downwards trend in the zero-probability. In intraday returns, the zero-probability may be periodic: It is lower in periods where the opening hours of the main financial centers overlap, and higher otherwise. A nonstationary zero-process invalidates standard estimators of volatility models, since they rely on the assumption that returns are strictly stationary. We propose a GARCH model that accommodates a nonstationary zero-process, derive a zero-adjusted QMLE for the parameters of the model, and prove its consistency and asymptotic normality under mild assumptions. The volatility specification in our model can contain higher order ARCH and GARCH terms, and past zero-indicators as covariates. Simulations verify the asymptotic properties in finite samples, and show that the standard estimator is biased. An empirical study of daily and intradaily returns illustrate our results. They show how a nonstationary zero-process induces time-varying parameters in the conditional variance representation, and that the distribution of zero returns can have a strong impact on volatility predictions.
Sucarrat, Genaro (2021)
Identification of volatility proxies as expectations of squared financial returns
Volatility proxies like realised volatility (RV) are extensively used to assess the forecasts of squared financial returns produced by volatility models. But are volatility proxies identified as expectations of the squared return? If not, then the results of these comparisons can be misleading, even if the proxy is unbiased. Here, a tripartite distinction is introduced between strong, semi-strong, and weak identification of a volatility proxy as an expectation of the squared return. The definition implies that semi-strong and weak identification can be studied and corrected for via a multiplicative transformation. Well-known tests can be used to check for identification and bias, and Monte Carlo simulations show that they are well sized and powerful—even in fairly small samples. As an illustration, 12 volatility proxies used in three seminal studies are revisited. Half of the proxies do not satisfy either semi-strong or weak identification, but their corrected transformations do. It is then shown how correcting for identification can change the rankings of volatility forecasts.
Mauritzen, Johannes & Sucarrat, Genaro (2021)
Increasing Or Diversifying Risk?Tail Correlations, Transmission Flows And Prices Across Wind Power Areas
The probability of an observed financial return being equal to zero is not necessarily zero, or constant. In ordinary models of financial return, however, e.g. ARCH, SV, GAS and continuous-time models, the zero-probability is zero, constant or both, thus frequently resulting in biased risk estimates (volatility, Value-at-Risk, Expected Shortfall, etc.). We propose a new class of models that allows for a time varying zero-probability that can either be stationary or non-stationary. The new class is the natural generalisation of ordinary models of financial return, so ordinary models are nested and obtained as special cases. The main properties (e.g. volatility, skewness, kurtosis, Value-at-Risk, Expected Shortfall) of the new model class are derived as functions of the assumed volatility and zero-probability specifications, and estimation methods are proposed and illustrated. In a comprehensive study of the stocks at New York Stock Exchange (NYSE) we find extensive evidence of time varying zero-probabilities in daily returns, and an out-of-sample experiment shows that corrected risk estimates can provide significantly better forecasts in a large number of instances.
Gharsallah, Sofian & Sucarrat, Genaro (2020)
Hvor presise er prognosene i Nasjonalbudsjettet?
Samfunnsøkonomen, 134(3), s. 13- 20.
Årlige prognoser av norsk økonomi er av stor viktighet for beslutningstakere. Dette gjelder spesielt stortingspolitikerne som vedtar Statsbudsjettet basert på prognosene i Nasjonalbudsjettet. Disse prognosene utarbeides av Finansdepartementet. I dette studiet evaluerer vi presisjonen til et utvalg prognoser i perioden 1999-2018. Vi finner ingen generell støtte for hypotesen om at prognosene til enkle modeller er mer presise enn de til nasjonalbudsjettet. Videre finner vi at Nasjonalbudsjettets prognoser er: Generelt litt mer presise enn de til enkle modeller, på nivå med prognosene til Norges Bank og SSB, og at de i gjennomsnitt treffer det de sikter på.
Sucarrat, Genaro (2019)
The log-GARCH model via ARMA representations
Chevallier, Julien; Goutte, Stéphane, Guerreiro, David, Saglio, Sophie & Sanhaji, Bilel (red.). Financial mathematics, volatility and covariance modelling
Pretis, Felix; Reade, J James & Sucarrat, Genaro (2018)
Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks
This paper provides an overview of the R package gets, which contains facilities for automated general-to-specific (GETS) modeling of the mean and variance of a regression, and indicator saturation (IS) methods for the detection and modeling of outliers and structural breaks. The mean can be specified as an autoregressive model with covariates (an "AR-X" model), and the variance can be specified as an autoregressive log-variance model with covariates (a "log-ARCH-X" model). The covariates in the two specifications need not be the same, and the classical linear regression model is obtained as a special case when there is no dynamics, and when there are no covariates in the variance equation. The four main functions of the package are arx, getsm, getsv and isat. The first function estimates an AR-X model with log-ARCH-X errors. The second function undertakes GETS modeling of the mean specification of an 'arx' object. The third function undertakes GETS modeling of the log-variance specification of an 'arx' object. The fourth function undertakes GETS modeling of an indicator-saturated mean specification allowing for the detection of outliers and structural breaks. The usage of two convenience functions for export of results to EViews and Stata are illustrated, and LATEX code of the estimation output can readily be generated.
Escribano, Alvaro & Sucarrat, Genaro (2018)
Equation-by-equation estimation of multivariate periodic electricity price volatility
Electricity prices are characterised by strong autoregressive persistence, periodicity (e.g. intraday, day-of-the week and month-of-the-year effects), large spikes or jumps, GARCH and – as evidenced by recent findings – periodic volatility. We propose a multivariate model of volatility that decomposes volatility multiplicatively into a non-stationary (e.g. periodic) part and a stationary part with log-GARCH dynamics. Since the model belongs to the log-GARCH class, the model is robust to spikes or jumps, allows for a rich variety of volatility dynamics without restrictive positivity constraints, can be estimated equation-by-equation by means of standard methods even in the presence of feedback, and allows for Dynamic Conditional Correlations (DCCs) that can – optionally – be estimated subsequent to the volatilities. We use the model to study the hourly day-ahead system prices at Nord Pool, and find extensive evidence of periodic volatility and volatility feedback. We also find that volatility is characterised by (positive) leverage in one third of the hours, and that a DCC model provides a better fit of the conditional correlations than a Constant Conditional Correlation (CCC) model.
Francq, Christian & Sucarrat, Genaro (2018)
An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation
