Results 61 to 70 of about 2,923 (211)
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
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Sustainable Dissemination of Digital Music Artworks on TikTok: A Social Media Analysis
Complexity, Volume 2026, Issue 1, 2026.This study examines sustainable dissemination of digital music on TikTok by linking musical‐aspect discourse and network positioning to engagement and recommendation performance, using 1200 TikTok songs/videos, each represented by 52 platform and interaction features.
Wan na +4 more
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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
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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
International Journal of Mathematics and Mathematical Sciences, Volume 2026, Issue 1, 2026.Loss data may often exhibit features such as multimodality and skewness that render single distributions incapable of capturing all these features. Insurance data often comprise of extremely large losses, of which single distributions may inadequately capture their varying features of different sizes.
Williams Kumi +4 more
wiley +1 more source
Risks, 2017 This work proposes a backtesting analysis that compares the Lee–Carter and the Cairns–Blake–Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975 ...
Carlo Maccheroni, Samuel Nocito
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MSA‐xLSTM: Multimodal Stock Price Forecasting With Multiscale Emotional Attention and Extended LSTM
International Journal of Intelligent Systems, Volume 2026, Issue 1, 2026.Financial time series forecasting is a long‐standing challenge due to the high complexity, randomness, and nonstationary of data. While predictions are typically made based on historical data with multiple features, existing models often fail to effectively integrate and utilize these diverse inputs, limiting forecasting accuracy.
Shuheng Lyu +5 more
wiley +1 more source
Evaluation of VaR Estimates based on ARCH type Models [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2011 This paper studies four ARCH type models including ARCH, GARCH, EGARCH and TGARCH at Value at Risk (VaR) estimation. The four models were applied to daily Tehran stock market data to assess each model in estimating one day Value at Risk at various ...
Naser Khiabani, Maryam Sarooghi
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A Low Price Correction for Improved Volatility Estimation and Forecasting
Risks, 2017 In this work, we focus on volatility estimation which plays a crucial role in risk analysis and management. In order to improve value at risk (VaR) forecasts, we discuss the concept of low price effect and introduce the low price correction which does ...
George-Jason Siouris, Alex Karagrigoriou
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Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.In this paper, we model edge traffic with a conformable fractional partial differential equation that keeps memory in time and space. The solution represents a unit‐free attack pressure, built from a z‐scored edge series, a quiet period baseline, and a partially absorbing boundary that reflects scrubbing and rate limits.
Ahmad Alshanty +3 more
wiley +1 more source