210 skills found · Page 1 of 7
bashtage / ArchARCH models in Python
chibui191 / Bitcoin Volatility ForecastingGARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management
Topaceminem / DCC GARCHDCC GARCH modeling in Python
iankhr / ArmagarchARMA-GARCH
s-broda / ARCHModels.jlA Julia package for estimating ARMA-GARCH models.
mgao6767 / FrdsFinancial research data services for academics.
keblu / MSGARCHMSGARCH R Package
onnokleen / MfGARCHAn R package for using mixed-frequency GARCH models
duffau / RNN GARCHEstimating Value-at-Risk with a recurrent neural network (Jordan type) GARCH model
Blue-Universe / Time Series Analysis Statistical ArbitrageThis project used GARCH type models to estimate volatility and used delta hedging method to make a profit.
englianhu / Binary.com Interview Question次元期权应征面试题范例。 #易经 #道家 #十二生肖 #姓氏堂号子嗣贞节牌坊 #天文历法 #张灯结彩 #农历 #夜观星象 #廿四节气 #算卜 #紫微斗数 #十二时辰 #生辰八字 #命运 #风水 《始祖赢政之子赢家黄氏江夏堂联富•秦谏——大秦赋》 万般皆下品，唯有读书高。🚩🇨🇳🏹🦔中科红旗，歼灭所有世袭制可兰经法家回教徒巫贼巫婆、洋番、峇峇娘惹。https://gitee.com/englianhu
srivastavaprashant / MgarchDCC-GARCH(1,1) for multivariate normal distribution.
vishnukanduri / Time Series Analysis In PythonI perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.
Bturan19 / Neural Garch Hybrid Model ImplementationBy combining GARCH(1,1) and LSTM model implementing predictions.
olekssy / Quant FinanceOpen souce quantitative finance models and algorithms with tutorials
tlemenestrel / LSTM GARCHA Python implementation of a Hybrid LSTM-GARCH model for volatility forecasting
yitaohu88 / Empirical Method In FinanceWinter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices Class intro: Forecasting and Finance The random walk hypothesis Stationarity Time-varying volatility and General Least Squares Robust standard errors and OLS Topic 2: Time-dependence and predictability ARMA models The likelihood function, exact and conditional likelihood estimation Predictive regressions, autocorrelation robust standard errors The Campbell-Shiller decomposition Present value restrictions Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity Time-varying volatility in the data Realized Variance ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns Single- and multifactor models Economic factors: Models and data exploration Statistical factors: Principal Components Analysis Fama-MacBeth regressions and characteristics-based factors
JasonZhang2333 / GarchMidasR package for GARCH-MIDAS
Erfaniaa / High Frequency Trading GarchRetrieve data from Binance and simulate high-frequency trading on them using the GARCH model
blake-marsh / GARCH ReplicationReplication of key GARCH model papers
