{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## Introduction - Measuring Market Risk in Python \n",
"### [(Go to Quant Lab)](https://israeldi.github.io/quantlab/)\n",
"\n",
"#### Source: https://github.com/playgrdstar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"VAR or ES are common terms that one would usually come across in finance when it comes to the measurement of market risks.\n",
"\n",
"VAR, or Value At Risk, is basically a measure of the potential losses that one could face, at a specific level of confidence - e.g. 99%. But before we get into VAR, we first need to discuss what value we are assessing risk against. What we want to measure would be the change in market prices over a time period (e.g. day to day). So what VAR would then tell us then would be how much we could lose (or gain) due to the change in prices.\n",
"\n",
"It's quite common to use lognormal instead of normal returns when computing the change in prices. Useful links which provide more information on the difference between the two -\n",
"\n",
"- https://quantivity.wordpress.com/2011/02/21/why-log-returns/\n",
"- http://www.insight-things.com/log-normal-distribution-mistaken\n",
"\n",
"We will compute relative returns and lognormal returns for FX and equity prices. As daily returns are not large, the difference for FX is close to indiscernible, and just slightly for equity returns.\n",
"\n",
"We will use FX and equity data freely available from Quandl. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create free Quandl account\n",
"\n",
"1. Go to [Quandl](https://www.quandl.com) and create a free account.\n",
"2. Retrive your API key from your profile\n",
"3. Open **Terminal** and install quandl: `pip install quandl` "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from scipy import stats\n",
"import numpy as np\n",
"import pandas as pd\n",
"import quandl"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Get your own key from Quandl and add here\n",
"quandl.ApiConfig.api_key = \"nN5mnpi_zxaiJp7u3Yii\""
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"fx_list = ['CUR/JPY', 'CUR/GBP', \n",
" 'CUR/EUR', 'CUR/CHF']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"start = pd.datetime(2011,1,1)\n",
"end_ = pd.datetime.today()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"dates = pd.date_range(start, end_)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"FX_DF = pd.DataFrame(index=dates)\n",
"for code in fx_list:\n",
" FX_DF[code] = quandl.get(code, start_date=start, end_date=end_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's just plot the distribution of actual price levels."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"FX_DF.hist(bins=20, figsize=(10,10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Working with Equities"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"eq_list = ['EOD/MSFT', 'EOD/AAPL', 'EOD/MMM', 'EOD/MCD']\n",
"EQ_DF = pd.DataFrame(index=dates)\n",
"for code in eq_list:\n",
" EQ_DF[code] = quandl.get(code, start_date=start, end_date=end_).Close"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"EQ_DF.hist(bins=20, figsize=(10,10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"EQ_Returns = (EQ_DF / EQ_DF.shift(10)) - 1\n",
"EQ_Returns.hist(bins=20, range=(-0.05, 0.05), figsize=(10,10))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"EQ_DF_LogReturns = np.log(EQ_DF / EQ_DF.shift(10))\n",
"EQ_DF_LogReturns.hist(bins=20, range=(-0.05, 0.05), figsize=(10,10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Value at Risk ##"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Single Asset\n",
"\n",
"There's nothing very complicated about Value at Risk (VAR). To put it simply, it's simply a single metric that shows the potential losses of a portfolio etc (at different confidence levels). There are three main methods to compute VAR -\n",
"* Parametric\n",
"* Historical\n",
"* Monte Carlo\n",
"\n",
"**Parametric VAR**\n",
"\n",
"Very often, the parametric VAR is based on a normal distribution. Plotting a normal distribution and the VAR on a chart will give us a good overview of how this works."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# We use z to define how many standard deviations away from the mean\n",
"# Here we use z = -2.33 to get to a 99% confidence interval. Why 99% will be obvious once we plot out the distribution\n",
"z = -2.33\n",
"\n",
"plt.figure(figsize=(12,8))\n",
"\n",
"# plotting the normal distribution, using the scipy stats norm function\n",
"plt.ylim(0, 0.5)\n",
"x = np.arange(-5,5,0.1)\n",
"y1 = stats.norm.pdf(x)\n",
"plt.plot(x, y1)\n",
"\n",
"x2 = np.arange(-5, z, 0.01) # we use this range from the -ve end to -2.33 to compute the area\n",
"sum_ = 0\n",
"# s = np.arange(-10,z,0.01)\n",
"for i in x2:\n",
" sum_ += stats.norm.pdf(i) * 0.01 # computing area under graph from -5 to -2.33 in steps of 0.01\n",
"\n",
"plt.annotate('Area is ' + str(round(sum_,3)), xy = (z-1.3, 0.05), fontsize=12)\n",
"plt.annotate('z=' + str(z), xy=(z, 0.01), fontsize=12)\n",
"plt.fill_between(x2, stats.norm.pdf(x2))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you understand what VAR and confidence levels mean (from the chart above), getting the $Z$ for different confidence levels is simple, and is the basis for computing parametric VAR."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"95%, 99%, 99.9% Z = -1.6448536269514722 -2.3263478740408408 -3.090232306167813\n"
]
}
],
"source": [
"z_95 = stats.norm.ppf(1 - 0.95)\n",
"z_99 = stats.norm.ppf(1 - 0.99)\n",
"z_999 = stats.norm.ppf(1 - 0.999)\n",
"\n",
"print('95%, 99%, 99.9% Z =', z_95, z_99, z_999)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let $\\alpha$ be the confidence level, then the $N$-day VaR is given by:\n",
"> $VAR_N = \\varPhi^{-1}(1-\\alpha) * \\sigma_V * \\sqrt{N}$\n",
"\n",
"If we are working with returns, then the formula becomes:\n",
"> $VAR_N = position * (-\\mu_V + \\varPhi^{-1}(1-\\alpha)*\\sigma_V) * \\sqrt{N}$\n",
"\n",
"If mean return is close 0, then we can drop this term from the formula above."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"\n",
"start = pd.datetime(2011,1,1)\n",
"end_ = pd.datetime.today()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"# Get Price data from AAPL\n",
"AAPL = quandl.get('EOD/AAPL', start_date=start, end_date=end_).Close\n",
"\n",
"rets_1 = (AAPL / AAPL.shift(1)) - 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let’s get market prices of AAPL from Quandl again, and compute the returns.\n",
"We shall compute the mean and standard deviation of the AAPL returns first as we will use this later to perform Monte Carlo simulation."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.00012614513280729436 0.02959836345294892 -2.3263478740408408 171.08\n"
]
}
],
"source": [
"mean = np.mean(rets_1)\n",
"std = np.std(rets_1)\n",
"Z_99 = stats.norm.ppf(1 - 0.99)\n",
"price = AAPL.iloc[-1]\n",
"\n",
"print(mean, std, Z_99, price)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let’s compute the parametric and historical VAR numbers so we have a basis for comparison."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"ParamVAR = price * Z_99 * std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Historical VaR**\n",
"\n",
"Historical VAR is even simpler. We simply get the return at the right percentile and apply the same formula to the latest prices."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parametric VAR is -11.780 and Historical VAR is -6.704\n"
]
}
],
"source": [
"HistVAR = price * np.percentile(rets_1.dropna(), 1)\n",
"\n",
"print('Parametric VAR is {0:.3f} and Historical VAR is {1:.3f}'.format(ParamVAR, HistVAR))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Monte Carlo**\n",
"\n",
"For Monte Carlo simulation, we simply apply a simulation using the assumptions of normality, and the mean and std computed above."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Simulated VAR is -11.725602014919927\n"
]
}
],
"source": [
"np.random.seed(100)\n",
"n_sims = 1000000\n",
"sim_returns = np.random.normal(mean, std, n_sims)\n",
"SimVAR = price * np.percentile(sim_returns, 1)\n",
"print('Simulated VAR is ', SimVAR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple Assets\n",
"\n",
"**Parametric VAR**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's compute the VAR for the equity prices we obtained from Quandl earlier"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"confidence = 0.99\n",
"Z = stats.norm.ppf(1 - confidence)\n",
"mean = np.mean(EQ_Returns)\n",
"stddev = np.std(EQ_Returns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The VAR for the latest prices is then ..."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"EOD/MSFT -6.856038\n",
"EOD/AAPL -31.625073\n",
"EOD/MMM -14.430516\n",
"EOD/MCD -9.489010\n",
"dtype: float64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"EQ_DF.dropna().iloc[-1,:] * Z *stddev"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Historical VAR**"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EOD/MSFT -6.990010\n",
"EOD/AAPL -13.950664\n",
"EOD/MMM -19.221713\n",
"EOD/MCD -14.115385\n",
"Name: 2017-12-28 00:00:00, dtype: float64\n"
]
}
],
"source": [
"print(EQ_DF.dropna().iloc[-1,:] * np.percentile(EQ_Returns.dropna(), 1))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
![](\"https://raw.githubusercontent.com/israeldi/quantlab/master/assets/images/Program-Logo.png\"){kind=logo}
\n",
"\n",
"## Introduction - Measuring Market Risk in Python \n",
"### [(Go to Quant Lab)](https://israeldi.github.io/quantlab/)\n",
"\n",
"#### Source: https://github.com/playgrdstar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"VAR or ES are common terms that one would usually come across in finance when it comes to the measurement of market risks.\n",
"\n",
"VAR, or Value At Risk, is basically a measure of the potential losses that one could face, at a specific level of confidence - e.g. 99%. But before we get into VAR, we first need to discuss what value we are assessing risk against. What we want to measure would be the change in market prices over a time period (e.g. day to day). So what VAR would then tell us then would be how much we could lose (or gain) due to the change in prices.\n",
"\n",
"It's quite common to use lognormal instead of normal returns when computing the change in prices. Useful links which provide more information on the difference between the two -\n",
"\n",
"- https://quantivity.wordpress.com/2011/02/21/why-log-returns/\n",
"- http://www.insight-things.com/log-normal-distribution-mistaken\n",
"\n",
"We will compute relative returns and lognormal returns for FX and equity prices. As daily returns are not large, the difference for FX is close to indiscernible, and just slightly for equity returns.\n",
"\n",
"We will use FX and equity data freely available from Quandl. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create free Quandl account\n",
"\n",
"1. Go to [Quandl](https://www.quandl.com) and create a free account.\n",
"2. Retrive your API key from your profile\n",
"3. Open **Terminal** and install quandl: `pip install quandl` "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from scipy import stats\n",
"import numpy as np\n",
"import pandas as pd\n",
"import quandl"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# Get your own key from Quandl and add here\n",
"quandl.ApiConfig.api_key = \"nN5mnpi_zxaiJp7u3Yii\""
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"fx_list = ['CUR/JPY', 'CUR/GBP', \n",
" 'CUR/EUR', 'CUR/CHF']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"start = pd.datetime(2011,1,1)\n",
"end_ = pd.datetime.today()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"dates = pd.date_range(start, end_)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"FX_DF = pd.DataFrame(index=dates)\n",
"for code in fx_list:\n",
" FX_DF[code] = quandl.get(code, start_date=start, end_date=end_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's just plot the distribution of actual price levels."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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