Liunice649's starred repositories
Innovation-on-brazilian-economic-research
Scientific research project. The idea was to analyse the patterns of innovation in economics research. It was based on Topic Modellind and Information Theory concepts to make quantitative measures of innovation, transience and resonance of a given paper.
Time-Series-Analysis-in-R
Univariate Time Series Modeling (ARMA, ARIMA, ARFIMA), Volatility Modeling and Forecasting (Rolling Window), Value at Risk (VaR) Forecasting and Backtesting
pencopulaCond
:exclamation: This is a read-only mirror of the CRAN R package repository. pencopulaCond — Estimating Non-Simplified Vine Copulas Using Penalized Splines
CDVineCopulaConditional
:exclamation: This is a read-only mirror of the CRAN R package repository. CDVineCopulaConditional — Sampling from Conditional C- and D-Vine Copulas
VineCopula
:exclamation: This is a read-only mirror of the CRAN R package repository. VineCopula — Statistical Inference of Vine Copulas. Homepage: https://github.com/tnagler/VineCopula Report bugs for this package: https://github.com/tnagler/VineCopula/issues
Garch-Vine-Copula
Dependence Structure Modelling Using R-Vine Copula
r-vine-copula-with-sliding-window
calculate r vine copula with sliding window
Time-Series-Analysis
Financial time series analysis using ARMA, ARCH and GARCH models, followed by quantitative risk management using VaR( Value at Risk) and ES (Expected Shortfall) loss functions
Risk-Management-in-Finance
Produced risk metric tools as Var & ES using log returns and creating several scenarios/simulations with different distributions
VaR-Estimation
Value at Risk Models Simulation and Comparison in R
VAR-Estimation
Estimating VAR of a portfolio of two stock indices using copula theory and Monte Carlo simulation.
RiskMeasures
R code for VaR and ES computations
Risk-Package
R Package to calculate VaR and ES
HighFrequencyEconometrics-HAR-vs.-Neural-Networks
Inspired by Hillebrand & Medeiros (2009) and Corsi (2009), I put neural networks in a High frequency environment, and tested the performance of the two models (HAR & Neural Networks). - The data used in this project is 2 years worth of intraday 5-minute realized volatility (See: Sheppard, Patton, Liu, 2012) from 20 Dow Jones stocks, that has been scrutinized using bivariate analysis and manipulation into a single dimension.
affineModelR
R package for working with multifactor stochastic volatility models, as in Duffie, Pan and Singleton (2000) 'Transform Analysis and Asset Pricing for Affine Jump-Diffusions'
Trading-strategies-analysis
Explored variations of trading strategies (equal weight, risk parity, trend following, volatility targeting) based on Kenneth French dataset (daily returns for 17 US industries) and concluded key findings about volatility, returns, and sharp ratio.
Financial-Time-Series-Analysis-and-Forecasting-GARCH-models
Stock/Financial Time Series Analysis, Prediction and Forecasting using advanced Statistical methods and GARCH volatility-based models in R.
Financial-Derivative-Analysis-and-Simulation
Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)
Volatility_in_the_Cryptosphere
Bayesian analysis and forecasting of Bitcoin volatility and definition of GARCH and ARCH models through MCMC sampling.