Key Concepts:
Autoregressive Coefficients (𝛽𝑖): These coefficients represent the influence of past values of Bitcoin returns on current values.
Intercept (𝛼): This represents the baseline level of Bitcoin returns.
Standard Deviation (𝜎): Measures the variability or volatility of Bitcoin returns.
Regime: Refers to different states or conditions that Bitcoin returns and MPU can exist in, such as high or low volatility regimes.
Transition Probabilities: The likelihood of moving from one regime to another over time.
Model Selection: The research discusses different variations of the Markov-switching VAR model, such as Markov-switching mean (MSM) and Markov-switching intercept (MSI), and determines the most suitable model based on criteria like the Akaike Information Criterion (AIC).
Empirical Results and Discussion:
Stationarity Tests: These tests check whether the data series (Bitcoin returns and MPU) are stable over time.
Cointegration Test: Determines whether there's a long-term relationship between Bitcoin returns and MPU.
Model Estimation: Estimates the parameters of the chosen model based on the data.
Transition Probabilities: Examines the likelihood of transitioning between different regimes.
https://w3.do/W_m57wHg
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libretech@cashu-address.com (EmPOWnet-Texts) / LibreTech-Freedom
npub1dg…ghmdm
2024-02-17 02:19:20
Author Public Key
npub1dgu6rp53h53l2xvcn95r7ju4vxkl7hmj2pmshvzkudzqrz84rsvszghmdmPublished at
2024-02-17 02:19:20Event JSON
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"content": "Key Concepts:\nAutoregressive Coefficients (𝛽𝑖): These coefficients represent the influence of past values of Bitcoin returns on current values.\nIntercept (𝛼): This represents the baseline level of Bitcoin returns.\nStandard Deviation (𝜎): Measures the variability or volatility of Bitcoin returns.\nRegime: Refers to different states or conditions that Bitcoin returns and MPU can exist in, such as high or low volatility regimes.\nTransition Probabilities: The likelihood of moving from one regime to another over time.\nModel Selection: The research discusses different variations of the Markov-switching VAR model, such as Markov-switching mean (MSM) and Markov-switching intercept (MSI), and determines the most suitable model based on criteria like the Akaike Information Criterion (AIC).\n\nEmpirical Results and Discussion:\nStationarity Tests: These tests check whether the data series (Bitcoin returns and MPU) are stable over time.\nCointegration Test: Determines whether there's a long-term relationship between Bitcoin returns and MPU.\nModel Estimation: Estimates the parameters of the chosen model based on the data.\nTransition Probabilities: Examines the likelihood of transitioning between different regimes.\n\nhttps://w3.do/W_m57wHg\n\nPosted via 🛰️ #Satcom (https://satcom.app)",
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