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Tag: python

pythonpapergarchvolatilityafter-work

Introducing Unified GARCH-RNN Volatility Forecasting for after-work quants

Wei, Yang & Cui (2025) embed GARCH(1,1) inside GRU and LSTM cells so ω, α, β stay economically readable while the net learns nonlinear vol. Beats pipeline hybrids and Transformers on US indices. Plain-language intro. Not investment advice.

pythonpapergarch

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volatility

after-work

Introducing Realized GARCH for after-work quants

Hansen, Huang & Shek (2012) jointly model daily returns and realized measures: RV is a noisy observation of latent variance, plus a leverage polynomial. The GARCH workhorse with high-frequency data. Plain-language intro. Not investment advice.

pythonpapervarcnnafter-work

Introducing Quantile CNN VaR for after-work quants

Petneházi (2019) turns a dilated causal CNN into a VaR machine with quantile (pinball) loss, and shows joint training on many stocks beats one-ticker nets. Plain-language intro. Not investment advice.

pythonpapervarriskafter-work

Introducing Neural Networks and Value at Risk for after-work quants

Arimond et al. (2020) run Monte-Carlo VaR from generative regime-switching nets. HMM init, a balanced bull/bear loss, and data-hungry RNNs. Plain-language intro. Not investment advice.

pythonpapercovariancehigh-frequencyafter-work

Introducing Knowing Factors or Factor Loadings, or Neither? for after-work quants

Dai, Lu & Xiu (2019) compare TSR, CSR and PCA large-covariance estimators on noisy asynchronous high-frequency data—known factors, known loadings, or neither. Plain-language intro. Not investment advice.

pythonpaperhrpreinforcement-learningafter-work

Introducing MVO vs HRP vs RL Portfolio Comparison for after-work quants

Sen et al. (2023) compare mean–variance, López de Prado HRP, and deep Q-learning portfolios on NIFTY50 and Indian sector baskets. Plain-language intro. Not investment advice.

pythonpapergicscovarianceafter-work

Introducing Incorporating GICS into Portfolio Allocation for after-work quants

Fan, Furger & Xiu (2016) sort S&P 500 residuals by GICS, keep industry blocks, add Fama–French and sector ETFs on high-frequency data, then allocate. Plain-language intro. Not investment advice.

pythonpaperganbacktestafter-work

Introducing Improving Robustness of Trading Strategy Backtesting with Boltzmann/GANs for after-work quants

Lezmi, Roche, Roncalli & Xu (2020) use RBMs, GANs and Wasserstein models as market generators so backtest statistics have a distribution, not one lucky path. Plain-language intro. Not investment advice.

pythonpapercovarianceshrinkageafter-work

Introducing Honey, I Shrunk the Sample Covariance Matrix for after-work quants

Ledoit & Wolf (2004) shrink the noisy sample covariance toward a structured target so Markowitz weights stop exploding. Plain-language intro for after-work Python learners. Not investment advice.

pythonpapervolatilitygarchafter-work

Introducing Realized GARCH for after-work quants

Hansen, Huang & Shek (2012) complete GARCH-X with a measurement equation linking realised variance to latent h_t, plus a leverage function. The daily-return-only GARCH is the nested special case. Not investment advice.

pythonpapervarcnnafter-work

Introducing Quantile CNN VaR for after-work quants

Petneházi (2019) forecasts VaR with a dilated causal CNN + pinball/quantile loss, trained jointly across many stocks. Single-name QCNN overshoots; the joint net is the point. Not investment advice.

pythonpapervarneural-networksafter-work

Introducing Neural Networks and Value at Risk for after-work quants

Arimond, Borth, Hoepner, Klawunn & Weisheit (2020) train generative regime-switching nets for VaR: HMM init, a two-objective loss, and a data-size warning. Not investment advice.