Archived
386 lines
30 KiB
JSON
386 lines
30 KiB
JSON
{
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"HAR model realized volatility Bitcoin high-frequency forecasting accuracy": [
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{
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"title": "[PDF] High-Frequency Forecasting of Bitcoin Volatility: Evaluating HAR Models ...",
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"url": "https://spectrum.library.concordia.ca/995904/1/Sarrafshirazi_MSc_F2025.pdf",
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"desc": "HAR-J delivers the most accurate day-ahead, while HAR-RS leads at weekly and monthly horizons due to persistent downside risk. At quarterly ..."
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},
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{
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"title": "Forecasting volatility of Bitcoin - ScienceDirect.com",
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"url": "https://www.sciencedirect.com/science/article/pii/S0275531921001616",
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"desc": "# Forecasting volatility of Bitcoin\n## Highlights\n- •\nWe consider many GARCH and two heterogeneous autoregressive (HAR) models.\n- •\nHAR models perform better than GARCH models.\n\n## Abstract\nSince we utilize realized variance estimated from high frequency data as a proxy for true volatility, we can d"
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},
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{
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"title": "A Comparative Analysis of GARCH, EGARCH, IGARCH, GJR ... - MDPI",
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"url": "https://www.mdpi.com/2227-7072/14/4/90",
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"desc": "# Predicting the Volatility of Cryptocurrencies’ Returns Using High-Frequency Data: A Comparative Analysis of GARCH, EGARCH, IGARCH, GJR-GARCH, LRE, and HAR Models\n## 1. Introduction\n### 1.2. Literature Review\n#### 1.2.3. Realized Volatility and High-Frequency Approaches\nThe advent of high-frequency"
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},
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{
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"title": "High-Frequency Forecasting of Bitcoin Volatility: Evaluating HAR Models ...",
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"url": "https://spectrum.library.concordia.ca/id/eprint/995904/",
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"desc": "# High-Frequency Forecasting of Bitcoin Volatility: Evaluating HAR Models with Realised Semivariance and Jump Components\nSarrafshirazi, Raheleh\n\n(2025)\n\n_High-Frequency Forecasting of Bitcoin Volatility: Evaluating HAR Models with Realised Semivariance and Jump Components._\n\n## Abstract\nThis thesis "
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},
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{
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"title": "Crypto Volatility Forecasting: Mounting a HAR, Sentiment, and Machine ...",
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"url": "https://link.springer.com/article/10.1007/s10690-024-09510-6",
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"desc": "# Crypto Volatility Forecasting: Mounting a HAR, Sentiment, and Machine Learning Horserace\n## 4 Methodology\n### 4.1 Forecasting models\nWith this set of models, we generate one-step ahead forecasts of realized volatility, respectively. 80% of the total number of observations, i.e. 1,169 out of 1,461 "
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},
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{
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"title": "[PDF] GARCH-type Models with Jumps and Inverse Leverage Effect - UKM",
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"url": "http://www.ukm.edu.my/jsm/pdf_files/SM-PDF-51-3-2022/25.pdf",
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"desc": "The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized."
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}
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],
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"forecasting realized volatility cryptocurrency 5-minute intraday GARCH HAR comparison": [
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{
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"title": "Comparison of Cryptocurrency and Stock Market Volatility Forecast ...",
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"url": "https://ideas.repec.org/a/hig/ecohse/202313.html",
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"desc": "It is shown that GARCH models are inferior to HAR models in the accuracy of forecasting both the realized volatility of Bitcoin and the E-mini S&P 500."
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},
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{
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"title": "A Comparative Analysis of GARCH, EGARCH, IGARCH, GJR ... - MDPI",
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"url": "https://www.mdpi.com/2227-7072/14/4/90",
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"desc": "# Predicting the Volatility of Cryptocurrencies’ Returns Using High-Frequency Data: A Comparative Analysis of GARCH, EGARCH, IGARCH, GJR-GARCH, LRE, and HAR Models\n## Abstract\nThe exponential generalized autoregressive conditional heteroskedastic (EGARCH), integrated GARCH (IGARCH), standard GARCH, "
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},
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{
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"title": "[PDF] High-Frequency Forecasting of Bitcoin Volatility: Evaluating HAR Models ...",
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"url": "https://spectrum.library.concordia.ca/995904/1/Sarrafshirazi_MSc_F2025.pdf",
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"desc": "A GARCH(1,1) benchmark provides a parametric comparison, with HAR variants outperforming it at short horizons, while GARCH exceeds performance ..."
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},
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{
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"title": "Forecasting volatility of Bitcoin - ScienceDirect.com",
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"url": "https://www.sciencedirect.com/science/article/pii/S0275531921001616",
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"desc": "# Forecasting volatility of Bitcoin\n## Highlights\n- •\nWe consider many GARCH and two heterogeneous autoregressive (HAR) models.\n- •\nRealized variance estimated is used as a proxy for true volatility.\n- •\nEGARCH and APARCH perform best among the GARCH models.\n- •\nHAR models perform better than GARCH "
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},
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{
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"title": "Predicting volatility of cryptocurrencies: Deep learning and GARCH family ...",
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"url": "https://mf-journal.com/article/view/370/352",
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"desc": "# Predicting volatility of cryptocurrencies: Deep learning and GARCH family models\n## 3. Data and Methodology\n### 3.3. HAR-RV\nThe HAR-RV model incorporates lagged daily, weekly, and monthly realized volatilities to capture multiple volatility components.\n\n- Lagged 1-day realized volatility.\n- Lagged"
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},
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{
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"title": "GARCH and Multivariate LSTM forecasting models for Bitcoin ...",
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"url": "https://github.com/chibui191/bitcoin_volatility_forecasting",
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"desc": "The objective of this project is to forecast the average daily Realized Volatility of BTC-USD 7 days out, using an Interval Window of 30 days."
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}
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],
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"deep learning realized volatility forecasting crypto LSTM HAR outperform": [
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{
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"title": "Can machine learning models better volatility forecasting? A combined ...",
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"url": "https://www.tandfonline.com/doi/full/10.1080/1351847X.2025.2553053",
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"desc": "Our results indicate a significant clear improvement in volatility forecasting regarding both the model's in-sample and out-of-sample accuracy."
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},
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{
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"title": "Predicting volatility of cryptocurrencies: Deep learning and GARCH family ...",
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"url": "https://mf-journal.com/article/view/370/352",
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"desc": "# Predicting volatility of cryptocurrencies: Deep learning and GARCH family models\n## 2. Literature Review\nShaimaa Alghamdi et al. (2022) used an LSTM-based sentiment analysis model to forecast cryptocurrency volatility. This highlights LSTM's suitability for detecting nonlinear dynamics in crypto m"
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},
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{
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"title": "Crypto Volatility Forecasting: Mounting a HAR, Sentiment, and Machine ...",
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"url": "https://link.springer.com/article/10.1007/s10690-024-09510-6",
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"desc": "Our results show that the machine learning models outperform traditional volatility forecasting methods (i.e.,the HAR model), indicating the ..."
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},
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{
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"title": "Machine learning approaches to forecasting cryptocurrency volatility",
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"url": "https://www.sciencedirect.com/science/article/pii/S1057521923004301",
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"desc": "# Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants\n## 7. Empirical results\n### 7.2. Universal model\nFirst, LSTM outperforms RF regarding prediction power, effectively extracting hidden and meaningful information from financial time-"
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},
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{
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"title": "CRYPTO PRICE PREDICTION USING LSTM+XGBOOST - arXiv",
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"url": "https://arxiv.org/html/2506.22055v1",
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"desc": "the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods."
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},
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{
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"title": "GARCH and Multivariate LSTM forecasting models for Bitcoin ...",
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"url": "https://github.com/chibui191/bitcoin_volatility_forecasting",
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"desc": "Multivariate LSTM could potentially give investors an advantage in terms of higher forecasting accuracy. would translate to a magnitude accuracy of 94.65% on ..."
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}
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],
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"intraday volatility seasonality cryptocurrency hour of day UTC volume patterns": [
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{
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"title": "Intraday and daily dynamics of cryptocurrency - ScienceDirect.com",
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"url": "https://www.sciencedirect.com/science/article/pii/S1059056024006506",
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"desc": "# Intraday and daily dynamics of cryptocurrency\n## 1. Introduction\nThey documented higher volatility of Bitcoin on Mondays, which is in line with our results on Bitcoin. The periodicity in Bitcoin intraday and intraweek volatility and volume was examined in a more recent study of [Hansen et al. (202"
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},
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{
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"title": "Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?",
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"url": "https://quantpedia.com/are-there-seasonal-intraday-or-overnight-anomalies-in-bitcoin/",
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"desc": "# Are There Seasonal Intraday or Overnight Anomalies in Bitcoin?\n## Seasonality Effects in Various Asset Classes\nAs we observe markets, we tend to identify various patterns that continuously repeat. However, the seasonality is not stock market exclusive. Overall, plenty of seasonality effects can be"
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},
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{
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"title": "(PDF) Time-of-Day Periodicities of Trading Volume and Volatility in ...",
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"url": "https://www.researchgate.net/publication/334727071_Time-of-Day_Periodicities_of_Trading_Volume_and_Volatility_in_Bitcoin_Exchange_Does_the_Stock_Market_Matter",
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"desc": "# Time-of-Day Periodicities of Trading Volume and Volatility in Bitcoin Exchange: Does the Stock Market Matter?\n## Abstract and Figures\nEmpirical results indicate that the trading activity resembles a reverse V-shaped pattern throughout the day. The Bitcoin market exhibits noticeably higher trading "
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},
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{
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"title": "[PDF] Intraday Liquidity Patterns and Volatility Effects in Cryptocurrency ...",
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"url": "https://papers.ssrn.com/sol3/Delivery.cfm/6f2e36a8-b571-47bb-8166-834ef6d86e02-MECA.pdf?abstractid=7434697&mirid=1",
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"desc": "Volume peaks at the same hours too, so in the raw data volume and spreads appear to move together. Once we condition on the time of day, however ..."
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},
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{
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"title": "Bayesian Analysis of Bitcoin Volatility Using Minute-by-Minute Data and ...",
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"url": "https://www.mdpi.com/2227-7390/13/16/2691",
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"desc": "Intraday volatility follows a similar U-shaped pattern [22,23]. However, some studies attribute intraday seasonality and day-of-the-week effects ..."
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},
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{
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"title": "Time-of-day periodicities of trading volume and volatility i - IDEAS/RePEc",
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"url": "https://ideas.repec.org/a/eee/finlet/v34y2020ics1544612319301904.html",
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"desc": "# Time-of-day periodicities of trading volume and volatility in Bitcoin exchange: Does the stock market matter?\n## Abstract\nBoth variables are substantially higher on weekdays than weekends."
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}
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],
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"volatility targeting crypto Sharpe ratio improvement transaction costs": [
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{
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"title": "Volatility Targeting Improves Risk-Adjusted Returns",
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"url": "https://alphaarchitect.com/volatility-targeting-improves-risk-adjusted-returns/",
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"desc": "Sharpe ratio improves from 0.40 (unscaled) to between 0.48 and 0.51 (volatility scaled) of volatility estimate. reduce the likelihood of ..."
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},
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{
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"title": "An Introduction to Volatility Targeting | QuantPedia",
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"url": "https://quantpedia.com/an-introduction-to-volatility-targeting/",
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"desc": "# An Introduction to Volatility Targeting\n## What is volatility and how to target it?\nNumerous researches argue that Sharpe Ratios increase under volatility targeting."
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},
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{
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"title": "The Impact of Volatility Targeting | Man Group",
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"url": "https://www.man.com/insights/the-impact-of-volatility-targeting",
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"desc": "Volatility targeting improves the Sharpe ratio of “risk assets” (equities and credit), and that of “balanced” and “risk parity” portfolios"
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},
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{
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"title": "Volatility Targeting Strategies: How Funds Size Positions by Risk",
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"url": "https://riskhub.org/blogs/volatility-targeting-strategies-how-funds-size-positions-by-risk-395",
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"desc": "Volatility targeting tends to improve the Sharpe ratio — return per unit of risk — for two reasons. First, because high-volatility periods are often also low- ..."
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},
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{
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"title": "The Impact of Volatility Targeting (Digest summary)",
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"url": "https://rpc.cfainstitute.org/research/cfa-digest/2019/07/dig-v49-7-2",
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"desc": "# The Impact of Volatility Targeting (Digest summary)\nThis strategy effectively manages volatility and mean shortfall risk across several asset classes but improves the Sharpe ratio only for equity and credit portfolios."
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},
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{
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"title": "The Quality of Returns: Crypto Risk-Adjusted Performance - XBTO",
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"url": "https://www.xbto.com/resources/the-quality-of-returns-crypto-risk-adjusted-performance",
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"desc": "## **Case study: active management vs. passive Bitcoin exposure**\n| Metric | Passive Bitcoin | XBTO Trend | Winner |\n|-|-|-|-|\n| **Sharpe ratio** | ~0.95 | 1.62 | Trend (1.7x higher) |"
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}
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],
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"Moreira Muir volatility-managed portfolios alpha": [
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{
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"title": "[PDF] Volatility-Managed Portfolios - GitHub Pages",
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"url": "https://amoreira2.github.io/alan-moreira.github.io/VolPortfolios_published.pdf",
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"desc": "# Volatility-Managed Portfolios\nALAN MOREIRA and TYLER MUIR ∗\n\nManaged portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean-variance investors.\n\nWe document that this simple trading strategy earns large alphas a"
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},
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{
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"title": "Volatility Managed Portfolios | NBER",
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"url": "https://www.nber.org/papers/w22208",
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"desc": "# Volatility Managed Portfolios\nManaged portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors."
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},
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{
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"title": "Volatility-Managed Portfolios by Alan Moreira, Tyler Muir :: SSRN",
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"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2659431",
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"desc": "# Volatility-Managed Portfolios\n## [Tyler Muir](https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=1399128)\n### Abstract\nManaged portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-va"
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},
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{
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"title": "[PDF] Volatility Managed Portfolios | NYU Stern",
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"url": "https://www.stern.nyu.edu/sites/default/files/assets/documents/Volatility%20Managed%20Portfolios.pdf",
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"desc": "Managed portfolios that take less risk when volatility is high produce large, positive alphas and increase factor Sharpe ratios by substantial amounts.\n\nWe call these volatility managed portfolios.\n\nportfolio and our volatility managed portfolio.\n\nalpha of this strategy decreases somewhat for the ma"
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},
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{
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"title": "[PDF] A Multifactor Perspective on Volatility-Managed Portfolios",
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"url": "http://wp.lancs.ac.uk/fofi2022/files/2022/08/FoFI-2022-076-Alberto-Martin-Utrera.pdf",
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"desc": "# 1 Introduction\nFor instance, the bulk of the analysis byMoreira and Muir(2017, sections I.B and I.D) focuses on a portfolio of only an individual factor and its volatility-managed counterpart.\n\n# 3 Performance gains from volatility management\n## 3.1 Volatility-managed individual-factor portfolios\n"
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},
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{
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"title": "On the performance of volatility-managed portfolios",
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"url": "https://www.sciencedirect.com/science/article/abs/pii/S0304405X2030132X",
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"desc": "# On the performance of volatility-managed portfolios [☆](https://www.sciencedirect.com/science/article/abs/pii/S0304405X2030132X#aep-article-footnote-id1)\n## Introduction\nSecond, despite this mixed evidence from direct performance comparisons, we confirm that Moreira and Muir’s (2017)) finding of s"
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}
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],
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"Harvey Hoyle Korgaonkar Rattray van Hemert impact of volatility targeting": [
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{
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"title": "The Impact of Volatility Targeting",
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"url": "https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3175538",
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"desc": "# The Impact of Volatility Targeting\n28 PagesPosted: 17 May 2018Last revised: 11 Jul 2018\n\n[See all articles by Campbell R. Harvey](https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=16198)\n\n## [Otto Van Hemert](https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=346474)\n_affiliation not "
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},
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{
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"title": "[PDF] The Impact of Volatility Targeting - Duke University",
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"url": "https://people.duke.edu/~charvey/Research/Published_Papers/P135_The_impact_of.pdf",
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"desc": "# The Impact of Volatility Targeting\nCampbell R. Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison and Otto Van Hemert\n\n_JPM_ 2018, 45 (1) 14-33 doi: [[https://doi.org/10.3905/jpm.2018.45.1.014](https://doi.org/10.3905/jpm.2018.45.1.014)](https://doi.org/10.3905/jpm.2018.45."
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},
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{
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"title": "The Impact of Volatility Targeting - Portfolio Management Research",
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"url": "https://www.pm-research.com/content/iijpormgmt/45/1/14",
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"desc": "However, the impact of volatility targeting goes beyond the Sharpe ratio: It reduces the likelihood of extreme returns across all asset classes. Particularly ..."
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},
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{
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"title": "An Introduction to Volatility Targeting | QuantPedia",
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"url": "https://quantpedia.com/an-introduction-to-volatility-targeting/",
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"desc": "# An Introduction to Volatility Targeting\n## What is volatility and how to target it?\nIn other words, to ensure that the amount of dollar risk remains the same.\n\nIf the volatility goes up, he/she has to scale down the portfolio. On the other hand, if the volatility goes down, he/she should take more"
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},
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{
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"title": "Volatility Targeting Improves Risk-Adjusted Returns",
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"url": "https://alphaarchitect.com/volatility-targeting-improves-risk-adjusted-returns/",
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"desc": "# Volatility Targeting Improves Risk-Adjusted Returns\nHigh (low) volatility over the recent past _tends to_ be followed by high (low) volatility in the future.\n\nHarvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert, contribute to the literature on managing v"
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},
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{
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"title": "The Impact of Volatility Targeting | Man Group",
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"url": "https://www.man.com/insights/the-impact-of-volatility-targeting",
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"desc": "Volatility targeting seeks to counter the fluctuations in volatility.\n\n**Volatility targeting** seeks to counter the fluctuations in volatility:\n\n- It leads to leveraging a portfolio at times of low volatility, and scaling down exposures at times of high volatility.\n- This approach targets a constan"
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}
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],
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"intraday momentum cryptocurrency first half hour predict last half hour": [
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{
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"title": "Intraday return predictability in the cryptocurrency markets",
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"url": "https://www.sciencedirect.com/science/article/abs/pii/S1062940822000833",
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"desc": "the first half-hour return, which contain overnight market information, significantly and positively predicts the last half-hour return of the same trading day."
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},
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{
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"title": "Attention Prop Traders: The first half hour of trading predicts the last half ...",
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"url": "https://alphaarchitect.com/attention-prop-traders-the-first-half-hour-of-trading-predicts-the-last-half-hour/",
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"desc": "# Attention Prop Traders: The first half hour of trading predicts the last half hour…\n## **Abstract:**\nIn this paper, using intra data from January 4, 1999 to December 31, 2012, we document an intraday momentum pattern that the first half-hour return on the market predicts the market return in the l"
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},
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{
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"title": "[PDF] Intraday Momentum: The First Half-Hour Return Predicts the Last ...",
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"url": "https://c.mql5.com/forextsd/forum/173/intraday_momentum_-_the_first_half-hour_return_predicts_the_last_half-hour_return.pdf",
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"desc": "Intraday Momentum: The First Half-Hour\n\n# Intraday Momentum: The First Half-Hour Return Predicts the Last Half-Hour Return\nBased on high frequency data of the S&P 500 ETF from 1993 – 2013, we document an intraday momentum pattern: the first half-hour return on the market predicts the last half-hour "
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},
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{
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"title": "[PDF] Intraday return predictability in the cryptocurrency markets: momentum ...",
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"url": "https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID4135239_code2537556.pdf?abstractid=4080253&mirid=1&ref=aligrithm.com",
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"desc": "This paper reports evidence of intraday return predictability, consisting of both intraday momentum and reversal, in the cryptocurrency market."
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},
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{
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"title": "Bitcoin intraday time series momentum - University of Birmingham",
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"url": "https://research.birmingham.ac.uk/en/publications/bitcoin-intraday-time-series-momentum/",
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"desc": "# Bitcoin intraday time series momentum\n## Cite this\nTherefore, we use trading volume as a proxy for the market trading time and show that the first half-hour positively predicts the last half-hour return.\n\nTherefore, we use trading volume as a proxy for the market trading time and show that the fir"
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},
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{
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"title": "Bitcoin intraday time series momentum - Wiley Online Library",
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"url": "https://onlinelibrary.wiley.com/doi/10.1111/fire.12290",
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"desc": "The intraday time series momentum of Gao et al. (2018) suggests that the first half-hour of the equity trading day predicts the final half-hour."
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}
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],
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"opening range breakout crypto 5 minute backtest": [
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{
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"title": "Backtest Results for the Opening Range Breakout Strategy - Reddit",
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"url": "https://www.reddit.com/r/algotrading/comments/1j9pxsr/backtest_results_for_the_opening_range_breakout/",
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"desc": "Summary: This strategy uses the first 15 minute candle of the New York open to define an opening range and trade breakouts from that range.Trading the Opening Range Breakout(ORB) Strategy - Reddit$BLNK - 5 minute opening range breakout (ORB) strategy reference.More results from www.reddit.com"
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},
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{
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"title": "Opening Range Breakout Strategy up 400% This Year (Strict Rules, Fully ...",
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"url": "https://tradethatswing.com/opening-range-breakout-strategy-up-400-this-year/?srsltid=AU7gw4V3TOcIEwZ5ZMAfwfUTkHMGG4B1do3jxbKqLEx1tRzblGXCDAsf",
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"desc": "ORB Strategy buys or sells when the price has a 5-minute candle close outside the high or low of the first 15 minutes. So 5-min chart, with high ..."
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},
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{
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"title": "I Backtested 1178668 Opening Range Breakout Trades. Here's the Data.",
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"url": "https://www.youtube.com/watch?v=MOG-DbgmzzI",
|
||
"desc": "# I Backtested 1,178,668 Opening Range Breakout Trades. Here's the Data.\nThe 5-minute is \"too noisy.\"\n\n[**5-minute ORB wins (53.78%)**](https://www.youtube.com/watch?v=MOG-DbgmzzI&t=83s)\n\n[**TSLA, META, SPY: symbol breakdown** **TSLA, META, SPY: symbol breakdown** \\\n\\\n5:21](https://www.youtube.com/"
|
||
},
|
||
{
|
||
"title": "5 minute opening range breakout on ES: 108% return in 6 months - edgeful",
|
||
"url": "https://www.edgeful.com/blog/posts/5-minute-opening-range-breakout-es-strategy",
|
||
"desc": "# 5 minute opening range breakout on ES: 108% return in 6 months\n## table of contents\n- what is the 5 minute opening range breakout\n- the backtest results: 5 minute opening range breakout on ES\n\n## how to build your own 5 minute opening range breakout strategy\n5. **run the backtest.** get at least 6"
|
||
},
|
||
{
|
||
"title": "ORB Trading Strategy Collection | TrendSpider Store",
|
||
"url": "https://trendspider.com/trading-tools-store/collection/orb-trading-strategy/",
|
||
"desc": "This ORB Trading Strategy bot is built specifically for backtesting Opening Range Breakout setups using intraday charts. The strategy operates on 1 minute, 5 ..."
|
||
},
|
||
{
|
||
"title": "Opening Range Breakout Strategy (ORB): Backtest",
|
||
"url": "https://www.quantifiedstrategies.com/opening-range-breakout-strategy/",
|
||
"desc": "## What is an opening range breakout?\n#### An example of an opening range breakout\nThe opening range can, for example, be from the open of the daily session until the close of the first five-minute bar (obviously opening range breakout is intraday bars). Then you use the high and low within that 5-m"
|
||
}
|
||
],
|
||
"Markov switching regime detection intraday cryptocurrency trend range": [
|
||
{
|
||
"title": "Markov and Hidden Markov Models for Regime Detection in ...",
|
||
"url": "https://www.preprints.org/manuscript/202603.0831",
|
||
"desc": "This study investigates the application of Markov and Hidden Markov Models (HMMs) for detecting latent market regimes in cryptocurrency markets ..."
|
||
},
|
||
{
|
||
"title": "What Is Market Regime Detection with Hidden Markov Models?",
|
||
"url": "https://www.cube.exchange/what-is/market-regime-detection-with-hidden-markov-models",
|
||
"desc": "A trend-following strategy can work well in persistent moves and then bleed in a choppy range; a mean-reversion strategy can do the opposite."
|
||
},
|
||
{
|
||
"title": "Market Regime Detection using Hidden Markov Models in QSTrader",
|
||
"url": "https://www.quantstart.com/articles/market-regime-detection-using-hidden-markov-models-in-qstrader/",
|
||
"desc": "## Python Implementation\n### Regime Detection Implementation\n#### Short-Term Trend Following Strategy\n```python\nclass MovingAverageCrossStrategy(AbstractStrategy):\n \"\"\"\n Requires:\n tickers - The list of ticker symbols\n events_queue - A handle to the system events queue\n short_window -"
|
||
},
|
||
{
|
||
"title": "Hidden Markov Model Market Regimes: How HMM Detects ...",
|
||
"url": "https://www.quantifiedstrategies.com/hidden-markov-model-market-regimes-how-hmm-detects-market-regimes-in-trading-strategies/",
|
||
"desc": "The HMM essentially acts as a regime switch, allocating capital to range-trading models instead of trending models during these periods. Volatility-Based ..."
|
||
},
|
||
{
|
||
"title": "Regime switches and commonalities of the cryptocurrencies asset class",
|
||
"url": "https://www.sciencedirect.com/science/article/abs/pii/S1062940821000577",
|
||
"desc": "In this paper we test for regime changes and possible regime commonalities in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, ..."
|
||
},
|
||
{
|
||
"title": "A Markov Regime Switching Approach to Characterizing Financial Time ...",
|
||
"url": "https://medium.com/@cemalozturk/a-markov-regime-switching-approach-to-characterizing-financial-time-series-a5226298f8e1",
|
||
"desc": "# A Markov Regime Switching Approach to Characterizing Financial Time Series\n## Get Cemal Öztürk, Ph.D.’s stories in your inbox\n- **p[0->0] (Probability of staying in Regime 0)**: 0.9775"
|
||
}
|
||
],
|
||
"ATR percentile filter breakout strategy low volatility": [
|
||
{
|
||
"title": "ATR Filter (MT5): Volatility Gate for Better Trade Quality - AlfaTactix",
|
||
"url": "https://alfatactix.com/academy/market-filters/atr-average-true-range",
|
||
"desc": "# ATR Filter (MT5): Volatility Gate for Better Trade Quality | AlfaTactix\n## Filter Explanation\n### ATR (Average True Range) Filter Explanation\n- Helps avoid trading during low-volatility periods when false signals are more common and profit potential is limited. Research indicates that trading duri"
|
||
},
|
||
{
|
||
"title": "Volatility Filter Indicator: ATR Percentile ... - Desire To Trade",
|
||
"url": "https://www.desiretotrade.com/docs/volatility-filter-indicator-atr-percentile-volatility/",
|
||
"desc": "The indicator built into the advanced trade filters DesireToTrade made algos takes any value below 50 as Low Volatility, anything between 50 and ..."
|
||
},
|
||
{
|
||
"title": "Volatility ATR Bands Strategy With a 33-Year Backtest (Rules, Results ...",
|
||
"url": "https://www.quantifiedstrategies.com/volatility-atr-bands-strategy/",
|
||
"desc": "ATR Bands strategy is a rules-based trading approach that uses volatility measures to time breakouts and reversals. It overlays dynamic price bands. These ..."
|
||
},
|
||
{
|
||
"title": "Rango Verdadero Medio (ATR) — Indicadores y estrategias",
|
||
"url": "https://es.tradingview.com/scripts/averagetruerange/page-11/",
|
||
"desc": "El Rango verdadero medio (ATR) es una herramienta que se utiliza en el análisis técnico para medir la volatilidad. — Indicadores y estrategias."
|
||
},
|
||
{
|
||
"title": "10 Volatility Filters Every Serious Trader Needs",
|
||
"url": "https://algomatictrading.substack.com/p/10-volatility-filters-every-serious",
|
||
"desc": "Description: ATR, introduced by J. Welles Wilder Jr., measures market volatility by calculating the average range between high and low prices ..."
|
||
},
|
||
{
|
||
"title": "ATR Trading Strategies Guide - Blog - TradersPost",
|
||
"url": "https://blog.traderspost.io/article/atr-trading-strategies-guide",
|
||
"desc": "Effective ATR breakout filters require price moves to exceed previous resistance or support by at least one ATR value. This filter eliminates ..."
|
||
}
|
||
],
|
||
"realized volatility long memory HAR vs EWMA out-of-sample forecast comparison horizon": [
|
||
{
|
||
"title": "Advances in forecasting realized volatility: a review of methodologies",
|
||
"url": "https://link.springer.com/article/10.1186/s40854-025-00809-5",
|
||
"desc": "# Advances in forecasting realized volatility: a review of methodologies\n## Analysis of applied forecasting models\\\n### Linear models\\\n#### HAR model\\\nAs noted, financial time series often exhibit features such as long memory (C4) and leptokurtosis (C3) (Cheng and Hung [2011](https://link.springer.c"
|
||
},
|
||
{
|
||
"title": "Forecasting Realized Volatility with Time Series Foundation Models",
|
||
"url": "https://arxiv.org/html/2607.05291v1",
|
||
"desc": "# Forecasting Realized Volatility with Time Series Foundation Models: A Comparison with Econometric Benchmarks\n## 1 Introduction\nForecasting realized volatility is central to risk management, derivative pricing, and portfolio allocation. Since the work of [Andersen and Bollerslev (1998)](https://a"
|
||
},
|
||
{
|
||
"title": "Volatility Forecasting Methods - Risk Hub",
|
||
"url": "https://riskhub.org/blogs/volatility-forecasting-methods",
|
||
"desc": "Decay factor ( λ ): the EWMA knob controlling how fast old data is forgotten; higher means longer memory and a smoother, slower forecast."
|
||
},
|
||
{
|
||
"title": "Volatility Forecasting: HAR Model - Portfolio Optimizer",
|
||
"url": "https://portfoliooptimizer.io/blog/volatility-forecasting-har-model/",
|
||
"desc": "## The HAR volatility forecasting model [Permalink](https://portfoliooptimizer.io/blog/volatility-forecasting-har-model#the-har-volatility-forecasting-model)\n### The original HAR model [Permalink](https://portfoliooptimizer.io/blog/volatility-forecasting-har-model#the-original-har-model)\nThe main id"
|
||
},
|
||
{
|
||
"title": "Prediction of realized volatility and implied volatility indices using AI ...",
|
||
"url": "https://www.sciencedirect.com/science/article/pii/S1057521924001534",
|
||
"desc": "Report improved out-of-sample forecasts on horizons from one day to one month. ANN and LSTM compared to AR and EGARCH across loss functions."
|
||
},
|
||
{
|
||
"title": "[PDF] Forecasting Realised Volatility Using ARFIMA and HAR Models",
|
||
"url": "https://eprints.lancs.ac.uk/id/eprint/133284/1/Volatility_Forecasting_Paper_QF.pdf",
|
||
"desc": "ARFIMA models are superior for the crisis and pre-crisis sub-samples. HAR forecasts are less sensitive to regime change and to longer forecasting horizons."
|
||
}
|
||
]
|
||
} |