Statistics & Risk Modeling

Scope & Guideline

Empowering research at the intersection of statistics and risk.

Introduction

Welcome to your portal for understanding Statistics & Risk Modeling, featuring guidelines for its aims and scope. Our guidelines cover trending and emerging topics, identifying the forefront of research. Additionally, we track declining topics, offering insights into areas experiencing reduced scholarly attention. Key highlights include highly cited topics and recently published papers, curated within these guidelines to assist you in navigating influential academic dialogues.
LanguageEnglish
ISSN2193-1402
PublisherWALTER DE GRUYTER GMBH
Support Open AccessNo
CountryGermany
TypeJournal
Convergefrom 1982 to 2002, from 2011 to 2024
AbbreviationSTATIST RISK MODEL / Statist. Risk Model.
Frequency4 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressGENTHINER STRASSE 13, D-10785 BERLIN, GERMANY

Aims and Scopes

The journal 'Statistics & Risk Modeling' focuses on advancing the understanding and application of statistical methodologies in the context of risk assessment and financial modeling. Its core areas include both theoretical developments and practical applications, targeting a diverse range of problems in finance, insurance, and risk management.
  1. Statistical Risk Measures:
    The journal emphasizes the development and application of statistical measures to quantify risk, including methodologies like Value at Risk (VaR) and Choquet risk measures.
  2. Portfolio and Investment Analysis:
    Research on portfolio selection strategies, emphasizing innovative approaches to optimize returns while managing risk, such as the use of Extended Gini Shortfall risk measures.
  3. Modeling Financial Time Series:
    A focus on time series analysis in finance, including VAR models and jump-diffusion models for better understanding and predicting market behavior.
  4. Robust Statistical Methods:
    An exploration of robust statistical techniques designed to handle massive datasets and ensure reliability in the presence of uncertainties.
  5. Bayesian Approaches in Risk:
    The application of Bayesian methods for investment and insurance strategies, incorporating dependencies between financial and insurance risks.
Recent publications in 'Statistics & Risk Modeling' indicate a shift towards more complex and nuanced themes in risk assessment and statistical analysis. These emerging topics are likely to influence future research and practice in the field.
  1. Complex Risk Models:
    There is an increasing focus on multi-dimensional and systemic risk models that account for interdependencies and equilibrium strategies, reflecting the complexity of real-world financial systems.
  2. Robust Estimation Techniques:
    Emerging methodologies for robust estimation, particularly in high-dimensional contexts and massive datasets, are gaining traction, highlighting the need for reliable statistical tools.
  3. Advanced Bayesian Methods:
    The application of advanced Bayesian techniques in investment and insurance decision-making is on the rise, indicating a growing interest in incorporating uncertainty in risk assessments.
  4. Non-linear and Asymptotic Analysis:
    Research exploring non-linear models and asymptotic properties is becoming more prominent, particularly in the context of risk measures, suggesting a deeper analytical approach to risk modeling.

Declining or Waning

While the journal has consistently focused on key themes in statistics and risk modeling, certain areas appear to be less prominent in recent publications. This decline may reflect shifts in research interest or the maturation of specific methodologies.
  1. Traditional VaR Backtesting:
    Research specifically centered on traditional backtesting methods for Value at Risk appears to be declining, as more comprehensive and innovative approaches are being developed.
  2. Simple Risk Models:
    There is a noticeable decrease in the publication of studies focusing on simpler, less nuanced risk models, suggesting a shift towards more complex, multi-dimensional models.
  3. Basic Statistical Techniques:
    The use of basic statistical techniques without robust enhancements is becoming less frequent, indicating a trend towards more sophisticated methodologies and tools.

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