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Evaluation of Value-at-Risk (VaR) using the Gaussian Mixture Models
Research in StatisticsThe normality of the distribution of stock returns is one of the basic assumptions in financial mathematics. Empirical studies, however, undermine the validity of this assumption.
Indrė Morkūnaitė +2 more
doaj +1 more source
Backtesting expected shortfall: a simple recipe? [PDF]
Journal of Risk, 2019 We propose a new backtesting framework for Expected Shortfall that could be used by the regulator. Instead of looking at the estimated capital reserve and the realised cash-flow separately, one could bind them into the secured position, for which risk measurement is much easier. Using this simple concept combined with monotonicity of Expected Shortfall
Moldenhauer, Felix, Pitera, Marcin
openaire +3 more sources
Is the World Getting Smaller? Network‐Driven Heterogeneity in Gravity‐Model Elasticities
The World Economy, Volume 49, Issue 11, Page 2478-2525, November 2026.ABSTRACT Gravity models constitute the workhorse of empirical international trade research, yet the temporal stability of their core parameters—distance and GDP elasticities—remains underexplored, particularly in relation to the evolving structure of trade networks.
Zsolt T. Kosztyán +2 more
wiley +1 more source
ESTIMATION OF VALUE AT RISK FOR GENERAL INSURANCE COMPANY STOCKS USING THE GARCH MODEL
BarekengInvestment plays a crucial role in supporting economic development by allocating funds to generate future profits. Among various investment options, stock investment is widely popular.
Edwin Setiawan Nugraha +3 more
doaj +1 more source
Introduction and Performance Comparison of some Common Multi-period VaR Forecasting Methods: A Case Study of the Tehran Stock Exchange [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2014 According to the Basel accords, financial institutions should forecast VaR of their portfolio over multi-period time horizons in order to determine their capital adequacy.
Seyed Mehdi Barakchian +1 more
doaj
Expected Shortfall is jointly elicitable with Value at Risk - Implications for backtesting [PDF]
, 2016 In this note, we comment on the relevance of elicitability for backtesting risk measure estimates. In particular, we propose the use of Diebold-Mariano tests, and show how they can be implemented for Expected Shortfall (ES), based on the recent result of
Fissler, Tobias +2 more
core
A review of backtesting for value at risk [PDF]
, 2017 There have been many backtesting methods proposed for value at risk. Yet they have rarely been applied in practice. Here, we provide a comprehensive review of all of the recent backtesting methods for VaR.
Y. Zhang +3 more
core +1 more source
Engineering Reports, Volume 8, Issue 10, October 2026.This study addresses multi‐focus constraint solving for high‐precision chip wiring. It proposes LPNIMT, CSDCFT, and FDSPRT to improve accuracy (97.5%), efficiency (+32.7%), and resource use (80.3%), overcoming limitations of prior methods. ABSTRACT With the growing deployment of industrial computing chip routing in high‐precision applications, research
Jie Li
wiley +1 more source
The Expected-Based Method of Value-at-Risk Prediction
Jambura Journal of MathematicsValue-at-Risk (VaR) remains a fundamental risk measure in financial risk management, providing an indicator for managing capital allocation and avoiding worst-case risk scenarios. Traditionally its defined as a quantile of the loss distribution. However,
Jacob Stevy Seleky +2 more
doaj +1 more source
Expert Systems, Volume 43, Issue 9, September 2026.ABSTRACT Forecasting cryptocurrency prices remains challenging due to extreme volatility, regime‐dependent dynamics, and unstable cross‐asset correlations. Statistical methods such as ARIMA and GARCH assume stationarity and linear dependence structures, making them inadequate for capturing non‐linear temporal patterns in high volatile cryptocurrency ...
Huali Zhao +2 more
wiley +1 more source