Dr. Raquel Gaspar
ISEG-UL – Universidade de Lisboa, REM-CEMAPRE – Research in Economics and Mathematics
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Academic Career Length
23Years (2004-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
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Journal Article
In this study, we use Neural Networks (NNs) to price American put options. We propose two NN models—a simple one and a more complex one—and we discuss the performance of two NN models with the Least-Squares Monte Carlo (LSM) method. This study relies on American put option market prices, for four large U.S. companies—Procter and Gamble Company (PG), Coca-Cola Company (KO), General Motors (GM), and Bank of America Corp (BAC). Our dataset is composed of all options traded within the period December 2018 until March 2019. Although on average, both NN models perform better than LSM, the simpler model (NN Model 1) performs quite close to LSM. Moreover, the second NN model substantially outperforms the other models, having an RMSE ca. 40% lower than the presented by LSM. The lower RMSE is consistent across all companies, strike levels, and maturities. In summary, all methods present a good accuracy; however, after calibration, NNs produce better results in terms of both execution time and Root Mean Squared Error (RMSE).
1 February 2012 in Communications in Statistics - Simulation and Computation
Journal Article
JSJ. Beleza Sousa
J. Beleza SousaMEManuel Leote Esquivel
Manuel Leote EsquivelRaquel GasparIn this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.
Journal Article
Raquel GasparMOMadalena Oliveira
Madalena OliveiraThe rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility.
1 January 2004 in SSRN Electronic Journal
Preprint
Raquel GasparFor finite dimensional factor models, the paper studies general quadratic term structures. These term structures include as special cases the affine term struct
1 June 2008 in Journal of Credit Risk
Journal Article
Raquel GasparISIrina Slinko
Irina Slinko
About Me
Raquel M. Gaspar holds a PhD degree in Finance from the Stockholm School of Economics, with previous education in risk management and derivatives, applied mathematics for economics, and management and economics.
Her research focuses on mathematical finance, with special interest in its applications to fixed-income markets, risk management, structured products, and portfolio management. Her work has been presented at academic seminars and conferences worldwide and published both in academic journals and industry-oriented books. She collaborates with several academic journals, acting as referee, and belongs to various editorial boards. In particular, she is currently Associate Editor of the Quarterly Journal of Finance and Accounting (QJFA) and of the European Actuarial Journal (EAJ).
With more than 20 years as an experienced lecturer at various levels—undergraduate, master's, PhD, and executive education—both in Portugal and abroad, she is an Associate Professor with Habilitation at ISEG, Universidade de Lisboa, where she is the scientific coordinator of the Master in Finance and belongs to the scientific commission of executive postgraduate program in financial analysis.
Besides her academic career, she has collaborated with industry, mainly as consultant, since 1998. Most of her consultancy work is related to structured products and derivatives in general.