Genaro Sucarrat

Sucarrat, Genaro & Grønneberg, Steffen (2020)

Risk Estimation with a Time-Varying Probability of Zero Returns

Journal of Financial Econometrics Doi: https://doi.org/10.1093/jjfinec/nbaa014

The probability of an observed financial return being equal to zero is not necessarily zero, or constant. In ordinary models of financial return, however, for example, autoregressive conditional heteroskedasticity, stochastic volatility, Generalized Autoregressive Score, and continuous-time models, the zero probability is zero, constant, or both, thus frequently resulting in biased risk estimates (volatility, value-at-risk [VaR], expected shortfall [ES], etc.). We propose a new class of models that allows for a time-varying zero probability that can either be stationary or nonstationary. The new class is the natural generalization of ordinary models of financial return, so ordinary models are nested and obtained as special cases. The main properties (e.g., volatility, skewness, kurtosis, VaR, ES) 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, 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

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

Journal of Statistical Software, 86(3), s. 1- 44. Doi: 10.18637/jss.v086.i03 - Full text in research archive

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

Energy Economics, 74(August), s. 287- 298. Doi: 10.1016/j.eneco.2018.05.017 - Full text in research archive

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.

Sucarrat, Genaro (2017)

Metode og økonometri - en moderne innføring (2. utgave)

[Textbook]. Fagbokforlaget.

Sucarrat, Genaro (2016)

Metode og økonometri - en moderne innføring (1. utgave)

[Textbook]. Fagbokforlaget.

Sucarrat, Genaro (2015)

Metode og økonometri -- en moderne innføring (midlertidig utgave)

[Textbook]. Fagbokforlaget.

Sucarrat, Genaro (2010)

Automated Model Selection in Finance: General-to-Specific Modelling of the Mean, Variance and Density

[Report]. Handelshøyskolen BI.

Sucarrat, Genaro (2010)

The Power Log-GARCH Model

[Academic lecture]. Fibe 2010.