Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
There is a risk-return trade-off after all
2005726 citationsÉric Ghysels, Pedro Santa‐Clara et al.profile →
MIDAS Regressions: Further Results and New Directions
2007680 citationsÉric Ghysels, Rossen Valkanov et al.profile →
Predicting volatility: getting the most out of return data sampled at different frequencies
2005608 citationsÉric Ghysels, Pedro Santa‐Clara et al.Journal of Econometricsprofile →
Do industries lead stock markets?
2006484 citationsHarrison Hong, Walter N. Torous et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Rossen Valkanov
Since
Specialization
Citations
This map shows the geographic impact of Rossen Valkanov'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 Rossen Valkanov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rossen Valkanov more than expected).
This network shows the impact of papers produced by Rossen Valkanov. 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 Rossen Valkanov. The network helps show where Rossen Valkanov may publish in the future.
Co-authorship network of co-authors of Rossen Valkanov
This figure shows the co-authorship network connecting the top 25 collaborators of Rossen Valkanov.
A scholar is included among the top collaborators of Rossen Valkanov 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 Rossen Valkanov. Rossen Valkanov is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ghent, Andra C., Walter N. Torous, & Rossen Valkanov. (2019). Commercial Real Estate as an Asset Class. Annual Review of Financial Economics. 11(1). 153–171.33 indexed citations
Ghysels, Éric, Alberto Plazzi, & Rossen Valkanov. (2011). Conditional Skewness of Stock Market Returns in Developed and Emerging Markets and its Economic Fundamentals. RePEc: Research Papers in Economics.16 indexed citations
Torous, Walter N., Rossen Valkanov, & Alberto Plazzi. (2010). Expected Returns and the Expected Growth in Rents of Commercial Real Estate. SSRN Electronic Journal.27 indexed citations
Plazzi, Alberto, Walter N. Torous, & Rossen Valkanov. (2008). The cross-sectional dispersion of commercial real estate returns and rent growth. 36(3).
13.
Ghysels, Éric, Pedro Santa‐Clara, & Rossen Valkanov. (2005). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics. 131(1-2). 59–95.608 indexed citations breakdown →
Torous, Walter N. & Rossen Valkanov. (2000). Boundaries of Predictability: Noisy Predictive Regressions. eScholarship (California Digital Library).25 indexed citations
19.
Valkanov, Rossen. (1999). Long-Horizon Regressions: Theoretical Results and Applications to the Expected Returns/Dividend Yields and Fisher Effect Relations. eScholarship (California Digital Library).2 indexed citations
20.
Valkanov, Rossen. (1999). The Term Structure with Highly Persistent Interest Rates. eScholarship (California Digital Library).10 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.