This package helps to handle, read-in and analyze data from the lobster high-frequency data universe

applyNS | applyNS Counts number of trades during last k seconds |

autocovariance | Autocovariane computes the Autocovariance matrices inputs are... |

aux_hac_weight | HAC Weight kernel |

base_mapply | base_mapply Base mapply function Use this function to compute... |

brk_estimate | Compute Blocked-Realized Kernels |

brk_smoothing | Smoothing Smoothes (BRK) matrices as in Hautsch et al (2012)... |

cond | Conditioning number Returns the conditioning number of a... |

driftburst_tstat | Drift-Burst t-statistic Function computes drift-burst... |

exponential.kernel | Exponential kernel |

extract_lobster | Extract lobster files This function extracts 7z files... |

IVhat_f | IV estimate based on 20 minute RV estimates |

lobster | Lobster: Limit order book sample |

parzen.kernel | Parzen Kernel Computes the Parzen-Kernel |

readin_lobster | Read-in lobster files This function reads-in extracted... |

realized_kernel | Realized Kernel estimator (univariate) |

remove_lobster | Remove lobster files This function removes extracted lobster... |

riwish | Inverse wishart Samples from an inverse wishart distribution... |

rwish | Wishart Samples from a Wishart distribution |

two_scale_realized_volatility | Two-scaled realized volatility estimator |

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