Modeling Value-at-Risk of a Mongolian Exchange Rae (MNT/USD) using the GARCH type model in Python

Time series analysis python package

ARIMA and GARCH modelling

This is a capstone research project for my Certificate in Applied Data Science (CADS) at my undergraduate institution, Wesleyan University, on the topic of "Understanding the Variances in COVID-19 Pandemic Outcome - Excess Mortality - with Social, Cultural, and Environmental Factors", sponsored by Prof. Maryam Gooyabadi.

GJR-GARCH models with exogenous variance regressors

The UK Tech sector holds a market capitalisation of £860 billion, making it the third largest globally. Accurate forecasting of stock returns for FTSE TechMark listed firms is essential for investors to make precise investment decisions. This study comparatively evaluates ARIMA and GJR-GARCH(1,1) models across 10 top UK technical firms listed on th

Time Series forecasting and linear regression modelling of currency price action

GARCH models to forecast time-varying volatility and value-at-risk in R

Volatility modeling of major NIFTY sector indices using GARCH and EGARCH frameworks, including stationarity diagnostics, rolling out-of-sample forecasts, and analysis of sectoral risk dynamics during crisis periods.

Analisi finanziaria - Forecasting dei rendimenti dello stock "Vistra Energy Corp." tramite modelli GARCH

Analysis of 2025 US tariff announcements on GBP/USD volatility using event-study design and GARCH models, with GBP/EUR as a control to isolate UK-specific effects.

Time Series forecasting and linear regression modelling of currency price action.

This model predicts future market volatility.Portfolio managers, traders and financial institutions use this to manage risk, price options, and decide how much exposure to take in financial markets.

A quantitative framework for modeling time-varying volatility using GARCH/EGARCH specifications and forecasting daily Value-at-Risk (VaR) for financial assets.