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International Journal of Finance & Banking Studies
Publications
Articles by publication year
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Crossref citations
Latest lifetime totals · grouped by publication year
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OpenAlex citations
Latest lifetime totals · grouped by publication year
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OpenAlex · Citations received
By year of the citing work · 2017–2026
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2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Full analytics report All distributions, publications, comparisons & data provenance
Crossref citation coverage: 1 / 2 publications (50.0%). Latest observation: 21 Oct 2026, 00:21 UTC. Means and percentages use records with known counts.
OpenAlex / Separate citation source
A broader view of this research
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
OPENALEX CITATIONS1
DOI MATCH COVERAGE100.0%2 / 2 publications
MEAN · KNOWN COUNTS ONLY0.52 known · 0 unknown
MEDIAN · KNOWN COUNTS ONLY0.5
Observation range: 22 Oct 2026 00:44 UTC — 27 Oct 2026 19:05 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
Institutions
2 / 2 with data · 0 missing or unmatched. Percentages use all 2 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
Publications
View publications data & coverage
Publication year
Journal / scope
Publications
Publications
Crossref known
OpenAlex known
2013
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1
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2026
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Crossref citations
View crossref citations data & coverage
Publication year
Journal / scope
Crossref citations
Publications
Crossref known
OpenAlex known
2013
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1
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OpenAlex citations
View openalex citations data & coverage
Publication year
Journal / scope
OpenAlex citations
Publications
Crossref known
OpenAlex known
2013
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Citation year / OpenAlex
Citations received over time
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 2 / 2 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-22T00:44:39.232511+00:00 — 2026-10-27T19:05:58.300563+00:00. Older observations may be retained after failed or incomplete refreshes.
OpenAlex · Citations received by year
View openalex · citations received by year data & coverage
Citation year
Journal / scope
OpenAlex · Citations received by year
Publications
Annual history known
2017
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0
2
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2018
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2019
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2020
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Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
In recent years the financial markets known a rapid development and become more and more complex. So, many regulatory requirements, focused on banks as well as insurance sector, have been developed. These regulatory are concentrated essentially on business risk control and required capital to cover risks. These requirements have influenced the asset allocation issue in insurance industry. These requirements have influenced the asset allocation issue in insurance industry. This section is interested by this issue. In first time it highlights some research works in this issue. Then we will investigate the relation between Solvency and optimal asset allocation. Finally we will explore the principal used methods in modeling asset and in choosing the optimal portfolio composition.
The aim of this paper is to calibrate the Longstaff, Mithal and Neis (2005) model (LMN model) based on the J-law (see Jerbi (2015) to implement the credit risk in the economic scenarios generator. The LMN model, based on the assumptions of the normality of the risks is used to establish a link between the spread, the probability of default and the liquidity risk. The related assumptions seem unrealistic given the depth of the distribution that characterizes the financial and economic data. In this context, we develop an LMN model based on the J-law taking into account the effects of Skewness and Kurtosis related to the two series of the CDS premium and the corporate bond prices. We use a simulated data that are from January 2009 to December 2016. We estimate the model parameters. The LMN model based on the J-law fit better with the reality than the one based on the normal Law.
The aim of this paper is to calibrate the Longstaff, Mithal and Neis (2005) model (LMN model) based on the J-law (see Jerbi (2015) to implement the credit risk in the economic scenarios generator. The LMN model, based on the assumptions of the normality of the risks is used to establish a link between the spread, the probability of default and the liquidity risk. The related assumptions seem unrealistic given the depth of the distribution that characterizes the financial and economic data. In this context, we develop an LMN model based on the J-law taking into account the effects of Skewness and Kurtosis related to the two series of the CDS premium and the corporate bond prices. We use a simulated data that are from January 2009 to December 2016. We estimate the model parameters. The LMN model based on the J-law fit better with the reality than the one based on the normal Law.
In recent years the financial markets known a rapid development and become more and more complex. So, many regulatory requirements, focused on banks as well as insurance sector, have been developed. These regulatory are concentrated essentially on business risk control and required capital to cover risks. These requirements have influenced the asset allocation issue in insurance industry. These requirements have influenced the asset allocation issue in insurance industry. This section is interested by this issue. In first time it highlights some research works in this issue. Then we will investigate the relation between Solvency and optimal asset allocation. Finally we will explore the principal used methods in modeling asset and in choosing the optimal portfolio composition.