Journal of Risk
ISSN:
1465-1211 (print)
1755-2842 (online)
Editor-in-chief: Farid AitSahlia
About this journal
This international peer-reviewed journal publishes a broad range of original research papers which aim to further develop understanding of financial risk management. As the only publication devoted exclusively to theoretical and empirical studies in financial risk management, The Journal of Risk promotes far-reaching research on the latest innovations in this field, with particular focus on the measurement, management and analysis of financial risk.
The Journal of Risk is particularly interested in papers on the following topics:
- Risk management regulations and their implications
- Risk capital allocation and risk budgeting
- Efficient evaluation of risk measures under increasingly complex and realistic model assumptions
- Impact of risk measurement on portfolio allocation
- Theoretical development of alternative risk measures
- Hedging (linear and non-linear) under alternative risk measures
- Financial market model risk
- Estimation of volatility and unanticipated jumps
- Capital allocation
Abstracting and Indexing: Scopus; Web of Science - Social Science Index; EconLit; EconBiz; ABI Research; and Cabell’s Directory
Journal Metrics:
Journal Impact Factor: 0.915
5-Year Impact Factor: 0.756
CiteScore: 1.2
Latest papers
A dynamic program under Lévy processes for valuing corporate securities
The authors design and solve an extended structural model that accommodates arbitrary Lévy dynamics for the underlying firm’s asset value, realistic debt payment schedules, multiple seniority classes and various intangible assets.
The relationship between crude oil futures and exchange rates in the context of the Covid-19 shock: a tale of two markets
The authors investigate the high-frequency intraday return and volatility transmission between crude oil futures prices and exchange rates during the 2020 Covid-19 pandemic in the Brent and INE markets.
Value-at-risk models: a systematic review of the literature
The authors conduct a systematic literature review of value-at-risk models to determine which models are most often used and whether any change in model popularity occurred after the 2007-9 financial crisis.
A theory for combinations of risk measures
This paper investigates combinations of risk measures under no restrictive assumption on the set of alternatives, obtaining results regarding the preservation of properties and acceptance sets for these combinations of risk measures.
Allocating and forecasting changes in risk
This paper considers time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components.
Insurance institutional shareholding and banking systemic risk contagion: an empirical study based on a least absolute shrinkage and selection operator–vector autoregression high-dimensional network
The authors use a LASSO-VAR method and generalized variance decomposition to measure the systemic risk contagion effect of Chinese-listed banks.
The impacts of financial and macroeconomic factors on financial stability in emerging countries: evidence from Turkey’s nonperforming loans
The authors assess the impacts of financial and macroeconomic factors on financial stability in emerging economies, using Turkey's banking sector in the period 2005 Q1 to 2020 Q3 as their example.
Asymmetric risk spillovers between oil and the Chinese stock market: a Beta-skew-t-EGARCH-EVT-copula approach
The author uses the marginal expected shortfall method alongside the Beta-skew-t-exponential generalized autoregressive conditional heteroscedasticity-extreme value theory model and the CoVaR model to investigate risk spillover between the crude oil…
Modeling maxima with a regime-switching Fréchet model
The authors identify a regime-switching Fréchet model which can be used to identify the behavior of extreme values in financial series.
Assessing systemic fragility: a probabilistic perspective
Using new measure of systemic fragility, the author ranks euro area banks and sovereigns and according to their systemic risk contribution.
Semiparametric GARCH models with long memory applied to value-at-risk and expected shortfall
The authors introduce and apply new semiparametric GARCH models with long memory to obtain rolling one-step ahead forecasts for the value-at-risk and expected shortfall (ES) for market risk assets.
Explainable artificial intelligence for credit scoring in banking
The authors put forward an explainable machine learning model predicting credit default using a real-world data set provided by a Norwegian bank.
Nonparametric estimation of systemic risk via conditional value-at-risk
The authors propose four new nonparametric estimators of static CoVar and compare their performance in simulation studies.
Forecasting the realized volatility of stock markets with financial stress
This paper investigates the impact of financial stress on the predictability of the realized volatility of five stock markets
Counterparty risk allocation
This paper investigates the problem of minimizing the risk of exposure to a small number of defaultable counterparties based on spectral risk measures.
The statistics of capture ratios
This paper investigates the statistical problem of estimating the capture ratio based on a finite number of observations of a fund’s returns.
Distance to default based on the CEV–KMV model
The author applies the CEV process to the KMV model in order to assess default risk, finding that this method improves forecasting ability.
A two-component realized exponential generalized autoregressive conditional heteroscedasticity model
The authors propose a two-component EGARCH model for the modeling of asset returns and realized measures of volatility.
Shrinking beta
The authors shrink correlation and volatility separately and evaluate the predictive power of this approach, finding economically and statistically significant gains from applying more shrinkage to correlations than to volatilities.
Forecasting the European Monetary Union equity risk premium with regression trees
The authors use EMU data from the period between 2000 to 2020 to forecast equity risk premium and investigate Classification and Regression Trees.