code/analysis/spy_return_regs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "attempted-suspension",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow.dataset as ds\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "from tqdm.auto import tqdm\n",
    "from multiprocessing import Pool\n",
    "from stargazer.stargazer import Stargazer\n",
    "from stargazer_mod import Stargazer as S_alt\n",
    "from adjustText import adjust_text\n",
    "import ipywidgets as widgets\n",
    "import itertools\n",
    "import copy\n",
    "\n",
    "sns.set_context(\"paper\", font_scale=1.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "celtic-information",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "soviet-perry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='datetime'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data for just SPY\n",
    "daily_df = pd.read_feather('../../data/proc/SPY_daily.feather')\n",
    "\n",
    "# Clean?\n",
    "daily_df = daily_df.sort_values(by = ['datetime'])\n",
    "\n",
    "# Check\n",
    "daily_df.assign(cumret = daily_df['log_return'].cumsum()).plot('datetime', 'cumret')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legal-detail",
   "metadata": {},
   "source": [
    "# Analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "competent-participant",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## Add regression variables\n",
    "\n",
    "# Rename\n",
    "daily_df['SJ_vol'] =  daily_df['real_vol_pos'] - daily_df['real_vol_neg']\n",
    "\n",
    "# Differences\n",
    "daily_df = daily_df.sort_values(by = 'datetime') \n",
    "for col in ['real_var', 'real_var_pos', 'real_var_neg', 'SJ', 'real_vol', 'real_vol_pos', 'real_vol_neg', 'SJ_vol']:\n",
    "    daily_df[f'{col}_diff'] = daily_df[f'{col}'].diff(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animated-account",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Daily Regressions (Main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "specified-seafood",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Regression functions\n",
    "\n",
    "def add_smooth_return(daily_df, window):\n",
    "    \n",
    "    daily_df['log_return_rolling'] = daily_df.groupby(['permno'])['log_return'].transform(\n",
    "        lambda x: x.rolling(window, min_periods = window).mean())\n",
    "    daily_df['log_return_rolling_lead'] = daily_df.groupby(['permno'])['log_return_rolling'].transform(\n",
    "        lambda x: x.shift(-window))\n",
    "    \n",
    "    return daily_df\n",
    "\n",
    "def add_smooth_regressor(daily_df, window, regressor):\n",
    "    \n",
    "    daily_df[f'{regressor}_rolling_mean'] = daily_df.groupby(['permno'])[regressor].transform(\n",
    "        lambda x: x.rolling(window, min_periods = window).mean())\n",
    "    \n",
    "    return daily_df\n",
    "    \n",
    "\n",
    "def run_regression(daily_df, regressors, window, sample_period):\n",
    "\n",
    "    # Define sample for regression and add necessary RHS vars\n",
    "    reg_df = add_smooth_return(daily_df.copy(), window)\n",
    "    for regressor in regressors:\n",
    "        reg_df = add_smooth_regressor(reg_df, window, regressor)\n",
    "        \n",
    "    # Filter by sample period\n",
    "    reg_df = reg_df.loc[reg_df['datetime'].between(*sample_period)]\n",
    "\n",
    "    # Fit\n",
    "    T = len(reg_df['log_return_rolling_lead'].dropna())\n",
    "    fit = smf.ols('log_return_rolling_lead ~ ' + ' + '.join(regressors), reg_df).fit(\n",
    "        cov_type=\"HAC\", cov_kwds={\"maxlags\": int(0.75 * T ** (1 / 3))}\n",
    "    )\n",
    "    \n",
    "    # Fit - alt for t-stats\n",
    "    T = len(reg_df[f'{regressors[0]}_rolling_mean'].dropna())\n",
    "    fit_alt = smf.ols('log_return_lead ~ ' + ' + '.join([x + '_rolling_mean' for x in regressors]), reg_df).fit(\n",
    "        cov_type=\"HAC\", cov_kwds={\"maxlags\": int(0.75 * T ** (1 / 3))}\n",
    "    )\n",
    "\n",
    "    # Save results\n",
    "    result = [sample_period, regressors, window, fit.params, fit.tvalues, fit_alt.tvalues, fit]\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "atmospheric-positive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2002-2020)\n",
      "\\begin{table}[!htbp] \\centering\n",
      "\\begin{tabular}{@{\\extracolsep{5pt}}lcccccc}\n",
      "\\\\[-1.8ex]\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "& \\multicolumn{6}{c}{\\textit{Dependent variable:}} \\\n",
      "\\cr \\cline{2-7}\n",
      "\\\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Intercept & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ \\\\\n",
      "& (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) \\\\\n",
      "& $[0.098]$ & $[0.045]$ & $[0.702]$ & $[0.571]$ & $[0.647]$ & $[0.316]$ \\\\\n",
      " $\\sqrt{RV}$ & 0.038$^{}$ & & 0.003$^{}$ & & 0.003$^{}$ & \\\\\n",
      "& (0.056) & & (0.049) & & (0.030) & \\\\\n",
      "& $[0.675]$ & & $[0.042]$ & & $[0.067]$ & \\\\\n",
      " $\\sqrt{RV^-}$ & & 0.303$^{}$ & & 0.106$^{}$ & & 0.057$^{}$ \\\\\n",
      "& & (0.248) & & (0.085) & & (0.027) \\\\\n",
      "& & $[1.223]$ & & $[1.275]$ & & $[2.001]$ \\\\\n",
      " $\\sqrt{RV^+}$ & & -0.246$^{}$ & & -0.100$^{}$ & & -0.051$^{}$ \\\\\n",
      "& & (0.236) & & (0.081) & & (0.036) \\\\\n",
      "& & $[-1.041]$ & & $[-1.286]$ & & $[-1.918]$ \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Observations & 4,783 & 4,783 & 4,779 & 4,779 & 4,763 & 4,763 \\\\\n",
      " $R^2$ & 0.000 & 0.002 & 0.000 & 0.001 & 0.000 & 0.002 \\\\\n",
      "\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "\\textit{Note:}\\end{tabular}\n",
      "\\end{table}\n",
      "                                                                                                    \n",
      "(2002-2019)\n",
      "\\begin{table}[!htbp] \\centering\n",
      "\\begin{tabular}{@{\\extracolsep{5pt}}lcccccc}\n",
      "\\\\[-1.8ex]\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "& \\multicolumn{6}{c}{\\textit{Dependent variable:}} \\\n",
      "\\cr \\cline{2-7}\n",
      "\\\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Intercept & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ \\\\\n",
      "& (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) \\\\\n",
      "& $[0.431]$ & $[0.423]$ & $[0.611]$ & $[0.582]$ & $[1.110]$ & $[0.979]$ \\\\\n",
      " $\\sqrt{RV}$ & 0.018$^{}$ & & 0.006$^{}$ & & -0.018$^{}$ & \\\\\n",
      "& (0.060) & & (0.047) & & (0.029) & \\\\\n",
      "& $[0.301]$ & & $[0.111]$ & & $[-0.369]$ & \\\\\n",
      " $\\sqrt{RV^-}$ & & 0.259$^{}$ & & 0.060$^{}$ & & 0.040$^{}$ \\\\\n",
      "& & (0.262) & & (0.085) & & (0.026) \\\\\n",
      "& & $[0.991]$ & & $[0.740]$ & & $[2.502]$ \\\\\n",
      " $\\sqrt{RV^+}$ & & -0.233$^{}$ & & -0.050$^{}$ & & -0.066$^{}$ \\\\\n",
      "& & (0.256) & & (0.068) & & (0.036) \\\\\n",
      "& & $[-0.912]$ & & $[-0.758]$ & & $[-2.554]$ \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Observations & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 \\\\\n",
      " $R^2$ & 0.000 & 0.001 & 0.000 & 0.000 & 0.002 & 0.004 \\\\\n",
      "\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "\\textit{Note:}\\end{tabular}\n",
      "\\end{table}\n",
      "                                                                                                    \n",
      "(2020-2020)\n",
      "\\begin{table}[!htbp] \\centering\n",
      "\\begin{tabular}{@{\\extracolsep{5pt}}lcccccc}\n",
      "\\\\[-1.8ex]\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "& \\multicolumn{6}{c}{\\textit{Dependent variable:}} \\\n",
      "\\cr \\cline{2-7}\n",
      "\\\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Intercept & -0.001$^{}$ & -0.001$^{}$ & 0.001$^{}$ & 0.001$^{}$ & -0.001$^{}$ & -0.001$^{}$ \\\\\n",
      "& (0.002) & (0.002) & (0.001) & (0.001) & (0.001) & (0.001) \\\\\n",
      "& $[-0.427]$ & $[-0.597]$ & $[0.486]$ & $[-0.537]$ & $[-0.583]$ & $[-1.594]$ \\\\\n",
      " $\\sqrt{RV}$ & 0.134$^{}$ & & -0.030$^{}$ & & 0.116$^{}$ & \\\\\n",
      "& (0.208) & & (0.166) & & (0.060) & \\\\\n",
      "& $[0.645]$ & & $[-0.152]$ & & $[0.779]$ & \\\\\n",
      " $\\sqrt{RV^-}$ & & 0.808$^{}$ & & 0.600$^{}$ & & 0.272$^{}$ \\\\\n",
      "& & (0.828) & & (0.309) & & (0.121) \\\\\n",
      "& & $[0.976]$ & & $[1.622]$ & & $[1.782]$ \\\\\n",
      " $\\sqrt{RV^+}$ & & -0.562$^{}$ & & -0.597$^{}$ & & -0.092$^{}$ \\\\\n",
      "& & (0.678) & & (0.301) & & (0.144) \\\\\n",
      "& & $[-0.829]$ & & $[-1.684]$ & & $[-1.623]$ \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Observations & 253 & 253 & 249 & 249 & 233 & 233 \\\\\n",
      " $R^2$ & 0.004 & 0.009 & 0.001 & 0.026 & 0.066 & 0.075 \\\\\n",
      "\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "\\textit{Note:}\\end{tabular}\n",
      "\\end{table}\n",
      "                                                                                                    \n"
     ]
    }
   ],
   "source": [
    "# Get regression results\n",
    "date_param_list = [('2002', '2021'), ('2002', '2020'), ('2020', '2021')]\n",
    "\n",
    "for date_param in date_param_list:\n",
    "    print(r'(' + date_param[0] + '-' + str(int(date_param[1])-1) + ')')\n",
    "    results_list = []\n",
    "    for window in (1,5,21):\n",
    "        for regressors in [['real_vol'], ['real_vol_pos', 'real_vol_neg']]:\n",
    "            result = run_regression(daily_df, regressors, window, date_param)\n",
    "            results_list.append(result)\n",
    "\n",
    "    ## Format finished results\n",
    "    results_df = pd.DataFrame(results_list, columns = ['sample_period', 'regressors', 'window', 'beta', 't_stats', 't_stats_alt', 'fit'])\n",
    "\n",
    "    # Get fits and replace t-stats\n",
    "    fit_list_alt = []\n",
    "    for i in range(len(results_df)):\n",
    "        fit_alt = copy.deepcopy(results_df.iloc[i,:]['fit'])\n",
    "        t_stats = results_df.iloc[i]['t_stats_alt'].copy()\n",
    "        t_stats.index = fit_alt.tvalues.index\n",
    "        fit_alt.tvalues = t_stats\n",
    "        fit_list_alt.append(fit_alt)\n",
    "\n",
    "    sg = S_alt(fit_list_alt)\n",
    "    sg.cov_map = {'real_vol': r'$\\sqrt{RV}$', 'real_vol_pos': r'$\\sqrt{RV^+}$', 'real_vol_neg': r'$\\sqrt{RV^-}$'}\n",
    "    sg.show_stars = False\n",
    "    sg.show_tstats = True\n",
    "    sg.show_residual_std_err = False\n",
    "    sg.show_f_statistic = False\n",
    "    sg.show_adj_r2 = False\n",
    "    print(sg.render_latex())\n",
    "    print(' '*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "light-evening",
   "metadata": {},
   "source": [
    "### With SJ vol (scratch tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "familiar-method",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{landscape}\n",
      "\\subsubsection{2002-2020}\n",
      "\\begin{table}[!htbp] \\centering\n",
      "\\begin{tabular}{@{\\extracolsep{5pt}}lccccccccc}\n",
      "\\\\[-1.8ex]\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "& \\multicolumn{9}{c}{\\textit{Dependent variable:}} \\\n",
      "\\cr \\cline{2-10}\n",
      "\\\\[-1.8ex] & \\multicolumn{3}{c}{Daily} & \\multicolumn{3}{c}{Weekly} & \\multicolumn{3}{c}{Monthly}  \\\\\n",
      "\\\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Intercept & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ \\\\\n",
      "& (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) \\\\\n",
      "& $[0.098]$ & $[0.045]$ & $[2.079]$ & $[0.702]$ & $[0.571]$ & $[2.059]$ & $[0.647]$ & $[0.316]$ & $[2.114]$ \\\\\n",
      " $\\sqrt{RV^+} - \\sqrt{RV^-}$ & & & -0.268$^{}$ & & & -0.102$^{}$ & & & -0.054$^{}$ \\\\\n",
      "& & & (0.237) & & & (0.075) & & & (0.025) \\\\\n",
      "& & & $[-1.132]$ & & & $[-1.296]$ & & & $[-1.738]$ \\\\\n",
      " $\\sqrt{RV}$ & 0.038$^{}$ & & & 0.003$^{}$ & & & 0.003$^{}$ & & \\\\\n",
      "& (0.056) & & & (0.049) & & & (0.030) & & \\\\\n",
      "& $[0.675]$ & & & $[0.042]$ & & & $[0.067]$ & & \\\\\n",
      " $\\sqrt{RV^-}$ & & 0.303$^{}$ & & & 0.106$^{}$ & & & 0.057$^{}$ & \\\\\n",
      "& & (0.248) & & & (0.085) & & & (0.027) & \\\\\n",
      "& & $[1.223]$ & & & $[1.275]$ & & & $[2.001]$ & \\\\\n",
      " $\\sqrt{RV^+}$ & & -0.246$^{}$ & & & -0.100$^{}$ & & & -0.051$^{}$ & \\\\\n",
      "& & (0.236) & & & (0.081) & & & (0.036) & \\\\\n",
      "& & $[-1.041]$ & & & $[-1.286]$ & & & $[-1.918]$ & \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Observations & 4,783 & 4,783 & 4,783 & 4,779 & 4,779 & 4,779 & 4,763 & 4,763 & 4,763 \\\\\n",
      " $R^2$ & 0.000 & 0.002 & 0.001 & 0.000 & 0.001 & 0.001 & 0.000 & 0.002 & 0.002 \\\\\n",
      "\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "\\textit{Note:}\\end{tabular}\n",
      "\\end{table}\n",
      "                                                                                                    \n",
      "\\end{landscape}\n",
      "\\begin{landscape}\n",
      "\\subsubsection{2002-2019}\n",
      "\\begin{table}[!htbp] \\centering\n",
      "\\begin{tabular}{@{\\extracolsep{5pt}}lccccccccc}\n",
      "\\\\[-1.8ex]\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "& \\multicolumn{9}{c}{\\textit{Dependent variable:}} \\\n",
      "\\cr \\cline{2-10}\n",
      "\\\\[-1.8ex] & \\multicolumn{3}{c}{Daily} & \\multicolumn{3}{c}{Weekly} & \\multicolumn{3}{c}{Monthly}  \\\\\n",
      "\\\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Intercept & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ & 0.000$^{}$ \\\\\n",
      "& (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) & (0.000) \\\\\n",
      "& $[0.431]$ & $[0.423]$ & $[2.053]$ & $[0.611]$ & $[0.582]$ & $[2.046]$ & $[1.110]$ & $[0.979]$ & $[2.077]$ \\\\\n",
      " $\\sqrt{RV^+} - \\sqrt{RV^-}$ & & & -0.245$^{}$ & & & -0.055$^{}$ & & & -0.054$^{}$ \\\\\n",
      "& & & (0.255) & & & (0.068) & & & (0.025) \\\\\n",
      "& & & $[-0.962]$ & & & $[-0.757]$ & & & $[-2.560]$ \\\\\n",
      " $\\sqrt{RV}$ & 0.018$^{}$ & & & 0.006$^{}$ & & & -0.018$^{}$ & & \\\\\n",
      "& (0.060) & & & (0.047) & & & (0.029) & & \\\\\n",
      "& $[0.301]$ & & & $[0.111]$ & & & $[-0.369]$ & & \\\\\n",
      " $\\sqrt{RV^-}$ & & 0.259$^{}$ & & & 0.060$^{}$ & & & 0.040$^{}$ & \\\\\n",
      "& & (0.262) & & & (0.085) & & & (0.026) & \\\\\n",
      "& & $[0.991]$ & & & $[0.740]$ & & & $[2.502]$ & \\\\\n",
      " $\\sqrt{RV^+}$ & & -0.233$^{}$ & & & -0.050$^{}$ & & & -0.066$^{}$ & \\\\\n",
      "& & (0.256) & & & (0.068) & & & (0.036) & \\\\\n",
      "& & $[-0.912]$ & & & $[-0.758]$ & & & $[-2.554]$ & \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Observations & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 & 4,531 \\\\\n",
      " $R^2$ & 0.000 & 0.001 & 0.001 & 0.000 & 0.000 & 0.000 & 0.002 & 0.004 & 0.002 \\\\\n",
      "\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "\\textit{Note:}\\end{tabular}\n",
      "\\end{table}\n",
      "                                                                                                    \n",
      "\\end{landscape}\n",
      "\\begin{landscape}\n",
      "\\subsubsection{2020-2020}\n",
      "\\begin{table}[!htbp] \\centering\n",
      "\\begin{tabular}{@{\\extracolsep{5pt}}lccccccccc}\n",
      "\\\\[-1.8ex]\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "& \\multicolumn{9}{c}{\\textit{Dependent variable:}} \\\n",
      "\\cr \\cline{2-10}\n",
      "\\\\[-1.8ex] & \\multicolumn{3}{c}{Daily} & \\multicolumn{3}{c}{Weekly} & \\multicolumn{3}{c}{Monthly}  \\\\\n",
      "\\\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6) & (7) & (8) & (9) \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Intercept & -0.001$^{}$ & -0.001$^{}$ & 0.001$^{}$ & 0.001$^{}$ & 0.001$^{}$ & 0.001$^{}$ & -0.001$^{}$ & -0.001$^{}$ & 0.001$^{}$ \\\\\n",
      "& (0.002) & (0.002) & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) & (0.001) \\\\\n",
      "& $[-0.427]$ & $[-0.597]$ & $[0.564]$ & $[0.486]$ & $[-0.537]$ & $[0.514]$ & $[-0.583]$ & $[-1.594]$ & $[0.558]$ \\\\\n",
      " $\\sqrt{RV^+} - \\sqrt{RV^-}$ & & & -0.504$^{}$ & & & -0.596$^{}$ & & & -0.049$^{}$ \\\\\n",
      "& & & (0.645) & & & (0.320) & & & (0.151) \\\\\n",
      "& & & $[-0.782]$ & & & $[-1.738]$ & & & $[-0.188]$ \\\\\n",
      " $\\sqrt{RV}$ & 0.134$^{}$ & & & -0.030$^{}$ & & & 0.116$^{}$ & & \\\\\n",
      "& (0.208) & & & (0.166) & & & (0.060) & & \\\\\n",
      "& $[0.645]$ & & & $[-0.152]$ & & & $[0.779]$ & & \\\\\n",
      " $\\sqrt{RV^-}$ & & 0.808$^{}$ & & & 0.600$^{}$ & & & 0.272$^{}$ & \\\\\n",
      "& & (0.828) & & & (0.309) & & & (0.121) & \\\\\n",
      "& & $[0.976]$ & & & $[1.622]$ & & & $[1.782]$ & \\\\\n",
      " $\\sqrt{RV^+}$ & & -0.562$^{}$ & & & -0.597$^{}$ & & & -0.092$^{}$ & \\\\\n",
      "& & (0.678) & & & (0.301) & & & (0.144) & \\\\\n",
      "& & $[-0.829]$ & & & $[-1.684]$ & & & $[-1.623]$ & \\\\\n",
      "\\hline \\\\[-1.8ex]\n",
      " Observations & 253 & 253 & 253 & 249 & 249 & 249 & 233 & 233 & 233 \\\\\n",
      " $R^2$ & 0.004 & 0.009 & 0.003 & 0.001 & 0.026 & 0.026 & 0.066 & 0.075 & 0.001 \\\\\n",
      "\\hline\n",
      "\\hline \\\\[-1.8ex]\n",
      "\\textit{Note:}\\end{tabular}\n",
      "\\end{table}\n",
      "                                                                                                    \n",
      "\\end{landscape}\n"
     ]
    }
   ],
   "source": [
    "# Get regression results\n",
    "date_param_list = [('2002', '2021'), ('2002', '2020'), ('2020', '2021')]\n",
    "\n",
    "for date_param in date_param_list:\n",
    "    print(r'\\begin{landscape}')\n",
    "    print(r'\\subsubsection{' + date_param[0] + '-' + str(int(date_param[1])-1) + '}')\n",
    "    results_list = []\n",
    "    for window in (1,5,21):\n",
    "        for regressors in [['real_vol'], ['real_vol_pos', 'real_vol_neg'], ['SJ_vol']]:\n",
    "            result = run_regression(daily_df, regressors, window, date_param)\n",
    "            results_list.append(result)\n",
    "\n",
    "    ## Format finished results\n",
    "    results_df = pd.DataFrame(results_list, columns = ['sample_period', 'regressors', 'window', 'beta', 't_stats', 't_stats_alt', 'fit'])\n",
    "\n",
    "    # Get fits and replace t-stats\n",
    "    fit_list_alt = []\n",
    "    for i in range(len(results_df)):\n",
    "        fit_alt = copy.deepcopy(results_df.iloc[i,:]['fit'])\n",
    "        fit_alt.tvalues = results_df.iloc[i]['t_stats_alt']\n",
    "        fit_alt.tvalues.index = results_df.iloc[i]['t_stats'].index\n",
    "        fit_list_alt.append(fit_alt)\n",
    "\n",
    "    sg = S_alt(fit_list_alt)\n",
    "    sg.cov_map = {'real_vol': r'$\\sqrt{RV}$', 'real_vol_pos': r'$\\sqrt{RV^+}$', \n",
    "                  'real_vol_neg': r'$\\sqrt{RV^-}$', 'SJ_vol': r'$\\sqrt{RV^+} - \\sqrt{RV^-}$'}\n",
    "    sg.show_stars = False\n",
    "    sg.show_tstats = True\n",
    "    sg.show_residual_std_err = False\n",
    "    sg.show_f_statistic = False\n",
    "    sg.show_adj_r2 = False\n",
    "    sg.column_labels = ['Daily'] + ['Weekly']  + ['Monthly']\n",
    "    sg.column_separators = [3,3,3]\n",
    "    print(sg.render_latex())\n",
    "    print(' '*100)\n",
    "    print(r'\\end{landscape}')"
   ]
  }
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