Mohammadreza Rostami; Fatemeh Rasti; Ebrahim Abbasi
Abstract
This study comparatively analyzes two advanced financial risk modeling frameworks: a copula-based Value-at-Risk (VaR) approach and the Multivariate Conditional Autoregressive Value-at-Risk (MCAViaR) model. We assess their effectiveness in capturing risk dynamics across diverse global markets, using daily ...
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This study comparatively analyzes two advanced financial risk modeling frameworks: a copula-based Value-at-Risk (VaR) approach and the Multivariate Conditional Autoregressive Value-at-Risk (MCAViaR) model. We assess their effectiveness in capturing risk dynamics across diverse global markets, using daily log returns from January 1, 2010, to December 31, 2024, for TEDPIX, S&P 500, and BIST 100. This research addresses limitations of traditional linear correlation, especially during market stress.The copula methodology involves two stages: fitting ARMA-GARCH models with Student’s t-distributed innovations for marginal distributions, then employing Gaussian, Student’s t, and Clayton copulas to model inter-market dependence, including tail dependence. MCAViaR, conversely, directly estimates conditional quantiles, adapting to evolving market conditions. Empirical validation is performed through rigorous backtesting, including Kupiec, Christoffersen, and Dynamic Quantile (DQ) tests.Results indicate significant differences. While Student’s t and Clayton copulas effectively capture tail dependence (evidenced by degrees of freedom and positive Clayton parameters), all models—both copula-based and MCAViaR—universally failed the stringent DQ tests across all indices and quantiles. This highlights systematic misspecification in capturing dynamic risk. Despite this, MCAViaR showed a more adaptive nature to sudden market shocks and provided visually more responsive VaR estimates than static copula specifications.The study underscores the necessity of robust, tail-sensitive models for accurate risk assessment in cross-border portfolios. Practical recommendations include adopting Student-t or Clayton copulas, integrating regime-switching mechanisms into MCAViaR, and employing multi-horizon stress testing to enhance dynamic risk management and account for market-specific behaviors.
Ali Tamoradi; Zoleikha Morsaliarzanagh; Zeinab Rezaei; Ebrahim Abbasi
Abstract
The present study aims to investigate the effect of corporate irresponsibility on stock price crash risk by emphasizing the moderating role of financial expertise of the audit committee in companies listed on the Tehran Stock Exchange. To estimate the multiple regression model to test the hypothesis, ...
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The present study aims to investigate the effect of corporate irresponsibility on stock price crash risk by emphasizing the moderating role of financial expertise of the audit committee in companies listed on the Tehran Stock Exchange. To estimate the multiple regression model to test the hypothesis, the aforementioned model was used using panel data by the pooled data method in companies listed on the Tehran Stock Exchange and Eviews 9 statistical software was used for statistical analysis. In this research, 150 companies (1050 company-years) were selected to test the research hypothesis between 2014 and 2020. The Levin, Lien and Wu tests were used to test the reliability of research data, the Jarque-Bera test was used to determine the normality of the data, the regression method was used to express the relationship between variables, t-test statistics to test the significance of regression coefficients, and finally the F-test statistic was used to determine the significance of the equation. In general, the results of testing the research hypotheses indicate that corporate irresponsibility has a significant positive effect on stock price crash risk. The results also show that the financial expertise of the audit committee has a significant moderating effect on the relationship between corporate irresponsibility and stock price crash risk. In fact, the financial expertise of the audit committee reduces the positive relationship between corporate irresponsibility and stock price crash risk.
emad koosha; Mohsen Seighaly; Ebrahim Abbasi
Abstract
The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to ...
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The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to the nature of price behavior in financial markets, have been reported to be well in studies. In this respect, measuring and comparing the accuracy and precision of random forest (RF), long-short-term memory (LSTM), and recurrent neural network (RNN) models in predicting the top and bottom of Bitcoin prices are the main objectives of the present study. The approach to predicting top and bottom prices using machine learning models can be considered as the innovative aspect of this research, while many studies seek to predict prices as time series, simple, or logarithmic price returns. Pricing top and bottom data as target variables and technical analysis indicators as feature variables in the 1-hour time frame from 1/1/2018 to 6/31/2022 served as input to the mentioned models for learning. Validation and testing are presented and used. 70% of the data are considered learning data, 20% as validation data, and the remaining 10% as test data. The result of this research shows over 80% accuracy in predicting the top and bottom Bitcoin price, and the random forest model’s prediction is more accurate than the LSTM and RNN models.
