Aug 2026· Finance: Theory and Practice· Vol 30, pp. 19-37· 0 citations· 31 references
Abstract
The aim of the study is to prove that dynamic portfolios can effectively reflect the temporal dynamics of current risks of a higher order, providing greater reliability and stability compared to traditional portfolios. The subject is the economic imbalance in portfolio models, which occurs when different participants have different level of knowledge about market conditions and the performance of assets. In an environment where traditional portfolios have shown low returns due to the pronounced peaks and sharp declines in financial asset returns, as well as their inability to account for dynamic changes in financial risks. This study incorporates higher-order short-term risks into traditional portfolios in order to mitigate the effects of deviations from the normal distribution. The methodology is based on the concept of multiple financial time series and the VAR-ICA-GARCH model. This model effectively captures the conditional mean, the covariance matrix, the mutual asymmetry matrix, and the mutual kurtosis matrix, thereby characterizing temporal changes at higher-order moments. Due to the inherent nonlinearities of dynamic portfolio optimization tasks, we use a genetic algorithm to solve the dynamic portfolio model. The results of the study show that dynamic portfolios can effectively reflect the changing dynamics of current higher-order risks, while providing greater reliability and stability than traditional portfolios. Even when exposed to such complex risks, dynamic portfolios perform better. The practical significance of this research lies in determining the time-varying weighting coefficients for the portfolio and conducting both simulation experiments and empirical analysis.
In the context of normalized volatility in the A-share market, traditional VaR models are unable to effectively capture the tail risk of extreme market conditions, while Expected Shortfall (ES) can make up for this deficiency. This paper selects 12 A-shares from two equally weighted stock portfolios within and outside the CSI 300 index, with a sample period from May 2022 to April 2026. It uses three methods—historical simulation, parametric method (normal), and GARCH (1,1)-t—to estimate VaR and ES, and conducts backtesting using the Kupiec POFF test, the Christoffersen conditional coverage test, and the Acerbi-Székely Z2 test. The results show that GARCH (1,1)-t can more accurately characterize tail risk at a 99% confidence level. The historical simulation method is generally robust, while the normal parameter method tends to underestimate systematic risk. ES is greater than VaR in all scenarios, which verifies its stronger ability to capture extreme losses. The conclusion remains unchanged after replacing the confidence level and portfolio weights. This paper recommends that A-share investors and regulatory agencies use ES as an important supplementary indicator to VaR, and prioritize the use of GARCH-t or historical simulation methods for risk measurement.
Yuhui Gao· Advances in Economics, Manag...· 0 citations
The fluctuation characteristics of financial time series have always been one of the research hotspots in the academic community. Generally speaking, financial return series have the characteristics of volatility clustering, fat tails, conditional heteroskedasticity, asymmetric shocks, etc. The above phenomena can be explained from the perspective of dynamic conditional variance by GARCH models and their extensions. This paper first introduces the basic ideas of ARCH and GARCH models, with a focus on the issue of volatility clustering of financial returns. Then, it reviews the relevant research from three aspects: model evolution, application scenarios, and practical value. It also analyzes the role of GARCH-type models in capturing volatility persistence, asymmetric impact, and risk transmission through applications in cryptocurrencies, energy assets, and high-frequency financial data. The study shows that GARCH-type models capture the volatility clustering feature of financial returns well, but there is still room to improve the modeling of extreme risk, the handling of high-dimensional assets, and model interpretability.
Dianjun Yang· Advances in Economics, Manag...· 0 citations
This study asks whether environmental, social, and governance (ESG) screening changes the risk-return profile of an Indian large-cap equity portfolio. The Nifty 100 ESG index is compared with its unscreened parent, the Nifty 100, so the only systematic difference is the ESG screen and reweighting applied to a common constituent universe; the Nifty 50 is a secondary reference. Daily data from January 2021 to May 2026 show significant excess kurtosis and ARCH effects, so a constant-conditional-correlation GARCH(1,1) model with Student's t innovations generates correlated one-year price paths, with tail dependence imposed through a Cholesky-factorized multivariate-t shock. Headline tail-risk figures average five independent 100,000-path runs. A deterministic grid of 1,001 portfolios spanning 0% to 100% ESG weight is assessed on the Sharpe ratio, annualized volatility, Value at Risk and Conditional Value at Risk at 99%, the Sortino ratio, and downside deviation, at a risk-free rate and minimum acceptable return of 6.5%. Against the matched parent, daily returns correlate at 0.978, annualized volatility is 14.5% for both, and mean daily returns are identical to three decimal places (0.0453%). The optimal ESG weight is therefore interior, ranging from 50.7% to 68.1%, and annualized return holds at 11.4% across the efficient set. A Jobson-Korkie test with the Memmel correction finds no difference in Sharpe ratios (z = 0.0045, p = 0.996), corroborated by paired t and Newey-West HAC tests. Against the properly matched benchmark, ESG screening had no measurable effect on return, risk, or tail risk over this sample.
Vasudha Srivatsa, Bhavya Vikas· Journal of Computers, Mechan...· 0 citations
This study investigates the time variation of systematic risk for companies listed on the Warsaw Stock Exchange (WSE) during the period 2011 – 2025. The empirical analysis combines rolling-window estimation with Bayesian approaches to structural instability and stochastic risk dynamics. The results reveal instability of beta coefficients, particularly during the COVID-19 pandemic and the subsequent energy and inflation shocks. The evidence for structural changes is considerably stronger for residual variance than for beta coefficients themselves. Dynamic Bayesian models further indicate that systematic risk evolves continuously over time and exhibits persistent but stationary dynamics, strongly rejecting random-walk-type behavior of beta coefficients. Banking sector companies display relatively stable market exposure, whereas growth-oriented and energy-related firms exhibit significantly stronger beta instability. The findings suggest that the assumption of constant beta coefficients may be difficult to justify for companies listed on the WSE and support the application of dynamic asset pricing models in emerging equity markets.
T. Wójtowicz· Communications of Internatio...· 0 citations
The results suggest that including a covariance noise or normalization term in the traditional expected value and variance-based approach to portfolio management helps reduce the impact of estimation error and market volatility on portfolio performance.
Zichun Fu· Journal of Innovation and De...· 0 citations
This study examines and develops an enhanced Capital Asset Pricing Model (CAPM) based on anomaly factors. The proposed model aims to analyze the relationship between financial risk and the expected rate of return on assets in the capital market. The research is applied and quantitative in nature, adopting a correlational and ex post facto approach.The statistical population consists of all companies listed on the Tehran Stock Exchange. Using a systematic elimination method, 144 firms were selected as the statistical sample over the period 2012–2022 (1391–1401 in the Iranian calendar). The data are panel (pooled) data, and the F-Limer (Chow) test and Hausman test were employed to determine the appropriate estimation method. The models were estimated using the Ordinary Least Squares (OLS) method. The results indicate that the traditional CAPM and the Fama–French model have limited explanatory power in explaining variations in excess stock returns. In contrast, the current ratio and cash flow were identified as influential factors that enhance the predictive capability of the model. Portfolio formation results show that a strategy of buying winner portfolios and selling loser portfolios based on cash flow and momentum criteria led to negative excess returns, whereas portfolios formed on the basis of the current ratio and cash balance generated positive and statistically significant returns. These portfolios also increased the adjusted coefficient of determination and improved the explanatory power of the models. The findings suggest that the enhanced CAPM can be employed without losing key information relative to the Fama–French model, while providing improved explanatory and predictive performance.
Seyed Saeid Sefidgaran, Mohamad Ali Aghaie, Meysam Arabzadeh et al.· Economics and Financial Poli...· 0 citations
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