Rogemar Mamon

1.4k total citations
82 papers, 1.0k citations indexed

About

Rogemar Mamon is a scholar working on Finance, Economics and Econometrics and Management Science and Operations Research. According to data from OpenAlex, Rogemar Mamon has authored 82 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Finance, 36 papers in Economics and Econometrics and 27 papers in Management Science and Operations Research. Recurrent topics in Rogemar Mamon's work include Stochastic processes and financial applications (36 papers), Financial Risk and Volatility Modeling (24 papers) and Insurance, Mortality, Demography, Risk Management (21 papers). Rogemar Mamon is often cited by papers focused on Stochastic processes and financial applications (36 papers), Financial Risk and Volatility Modeling (24 papers) and Insurance, Mortality, Demography, Risk Management (21 papers). Rogemar Mamon collaborates with scholars based in Canada, Philippines and United Kingdom. Rogemar Mamon's co-authors include Robert J. Elliott, Marianito R. Rodrigo, Paresh Date, Matt Davison, Fred Espen Benth, Nicola Spagnolo, Fabio Spagnolo, Yiyang Chen, Tak Kuen Siu and R. Bhushan Gopaluni and has published in prestigious journals such as Applied Energy, European Journal of Operational Research and Expert Systems with Applications.

In The Last Decade

Rogemar Mamon

76 papers receiving 959 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Rogemar Mamon Canada 19 567 421 293 237 97 82 1.0k
Rüdiger Kiesel Germany 18 747 1.3× 518 1.2× 193 0.7× 215 0.9× 54 0.6× 58 1.2k
Yongzeng Lai Canada 17 565 1.0× 553 1.3× 392 1.3× 361 1.5× 37 0.4× 59 1.3k
Huyên Pham France 16 916 1.6× 488 1.2× 310 1.1× 176 0.7× 10 0.1× 30 1.2k
Phelim Boyle Canada 12 849 1.5× 347 0.8× 211 0.7× 305 1.3× 68 0.7× 30 1.1k
Guglielmo D’Amico Italy 17 292 0.5× 191 0.5× 137 0.5× 69 0.3× 33 0.3× 122 955
Hoi Ying Wong Hong Kong 23 1.1k 2.0× 611 1.5× 464 1.6× 486 2.1× 79 0.8× 128 1.5k
Sergio Ortobelli Lozza Italy 15 712 1.3× 377 0.9× 639 2.2× 80 0.3× 31 0.3× 101 958
Pilar Poncela Spain 19 202 0.4× 481 1.1× 143 0.5× 37 0.2× 33 0.3× 52 899
Tomas Björk Sweden 17 2.1k 3.7× 1.2k 2.8× 798 2.7× 762 3.2× 79 0.8× 43 2.7k
Marek Musiela Australia 15 1.9k 3.4× 759 1.8× 325 1.1× 377 1.6× 31 0.3× 29 2.2k

Countries citing papers authored by Rogemar Mamon

Since Specialization
Citations

This map shows the geographic impact of Rogemar Mamon's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rogemar Mamon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rogemar Mamon more than expected).

Fields of papers citing papers by Rogemar Mamon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rogemar Mamon. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rogemar Mamon. The network helps show where Rogemar Mamon may publish in the future.

Co-authorship network of co-authors of Rogemar Mamon

This figure shows the co-authorship network connecting the top 25 collaborators of Rogemar Mamon. A scholar is included among the top collaborators of Rogemar Mamon based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rogemar Mamon. Rogemar Mamon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Massabó, Ivar, et al.. (2025). A lattice-based approach for life insurance pricing in a stochastic correlation framework. Mathematics and Computers in Simulation. 235. 145–159.
2.
Mamon, Rogemar, et al.. (2025). A Direct Approach in the Pricing Analysis and Risk Role Matching of a Guaranteed Annuity Option Under Correlated Risks. American Journal of Mathematical and Management Sciences. 44(2). 131–153.
3.
Chen, Yiyang, Rogemar Mamon, Fabio Spagnolo, & Nicola Spagnolo. (2024). Stock market returns and climate risk in the U.S.. Journal of Multinational Financial Management. 77. 100887–100887. 4 indexed citations
4.
Chen, Weihua, et al.. (2024). Does uncertainty affect the limits of arbitrage? Evidence from the U.S. stock markets. The North American Journal of Economics and Finance. 74. 102221–102221. 1 indexed citations
5.
Mamon, Rogemar, et al.. (2023). A novel perspective on forecasting non-ferrous metals’ volatility: Integrating deep learning techniques with econometric models. Finance research letters. 58. 104482–104482. 3 indexed citations
6.
Mamon, Rogemar, et al.. (2023). The Price Tag of Cyber Risk: A Signal-Processing Approach. IEEE Access. 11. 44294–44318. 3 indexed citations
7.
Chen, Yiyang, Rogemar Mamon, Fabio Spagnolo, & Nicola Spagnolo. (2023). Sustainable developments, renewable energy, and economic growth in Canada. Sustainable Development. 31(4). 2950–2966. 12 indexed citations
8.
Mamon, Rogemar, et al.. (2022). A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model. Lifetime Data Analysis. 29(1). 142–187. 1 indexed citations
9.
Mamon, Rogemar, et al.. (2020). The Valuation of a Guaranteed Minimum Maturity Benefit under a Regime-Switching Framework. North American Actuarial Journal. 25(3). 334–359. 12 indexed citations
10.
Li, S., et al.. (2020). AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS. Astin Bulletin. 50(3). 853–871. 15 indexed citations
11.
Mamon, Rogemar, et al.. (2018). A two-decrement model for the valuation and risk measurement of a guaranteed annuity option. Econometrics and Statistics. 8. 231–249. 5 indexed citations
12.
Mamon, Rogemar, et al.. (2017). A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach. Journal Of Big Data. 4(1). 46–46. 8 indexed citations
13.
Mamon, Rogemar, et al.. (2014). A generalized pricing framework addressing correlated mortality and interest risks: a change of probability measure approach. Stochastics. 86(4). 594–608. 25 indexed citations
14.
Rodrigo, Marianito R. & Rogemar Mamon. (2011). A unified approach to explicit bond price solutions under a time-dependent affine term structure modelling framework. Quantitative Finance. 11(4). 487–493. 1 indexed citations
15.
Mamon, Rogemar, et al.. (2011). An accessible implementation of interest rate models with Markov-switching. Expert Systems with Applications. 39(5). 4679–4689. 36 indexed citations
16.
Date, Paresh, et al.. (2009). A partially linearized sigma point filter for latent state estimation in nonlinear time series models. Journal of Computational and Applied Mathematics. 233(10). 2675–2682. 6 indexed citations
17.
Mamon, Rogemar, et al.. (2008). Valuation of contingent claims with mortality and interest rate risks. Mathematical and Computer Modelling. 49(9-10). 1893–1904. 33 indexed citations
18.
Rodrigo, Marianito R. & Rogemar Mamon. (2008). A NEW REPRESENTATION OF THE LOCAL VOLATILITY SURFACE. International Journal of Theoretical and Applied Finance. 11(7). 691–703. 1 indexed citations
19.
Rodrigo, Marianito R. & Rogemar Mamon. (2005). An alternative approach to solving the Black–Scholes equation with time-varying parameters. Applied Mathematics Letters. 19(4). 398–402. 36 indexed citations
20.
Mamon, Rogemar. (2004). Three Ways to Solve for Bond Prices in the Vasicek Model. Journal of Applied Mathematics and Decision Sciences. 8(1). 1–14. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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