A comprehensive market risk project comparing three volatility-adaptive VaR models across three correlation scenarios for a four-asset GBP-denominated equity portfolio (Nike, Citibank, Commerzbank, ...

Current Python alternatives for statistical models are slow, inaccurate and don't scale well. So we created a library that can be used to forecast in production environments or as benchmarks.

Volatility forecasting is a key component of modern finance, used in asset allocation, risk management, and options pricing. Investors and traders rely on precise volatility models to optimize ...

The study applies a Kalman filter (KF) to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to create a hybrid model, to estimate the parameters of the GARCH model in the ...

金融资产波动率建模在现代金融工程中具有重要地位，其应用涵盖风险管理、衍生品定价和投资组合优化等核心领域。本文着重探讨三种主流波动率建模方法：广义自回归条件异方差模型(GARCH)、Glosten-Jagannathan-Runkle-GARCH模型(GJR-GARCH)以及异质自回归模型(HAR)。

Abstract: The cost of renewable power price is coming down with increased modest methods in the electricity market. When it is to benefit both the producers and consumers of electricity, a next-day ...