Risk Appetite Statement by various companies

 JP Morgan Chase (2020 annual report)

Risk appetite The Firm’s overall appetite for risk is governed by a “Risk Appetite” framework. The framework and the Firm’s risk appetite are set and approved by the Firm’s CEO, Chief Financial Officer (“CFO”) and CRO. Quantitative parameters and qualitative factors are used to monitor and measure the Firm’s capacity to take risk consistent with its stated risk appetite. Qualitative factors have been established to assess select operational risks, and impact to the Firm’s reputation. Risk Appetite results are reported to the Board Risk Committee.

How does this type of scam earn money

 

This type of scam is really annoying and the google filter could not get rid of it. I am wondering anybody know how those scammers earn money. Anybody actually call them to get scammed? 

Monte Carlo simulation in Python

This is a good article to talk about Monte Carlo simulation. 

 https://www.mikulskibartosz.name/monte-carlo-simulation-in-python/

statsmodels (a good stats package)

 In [1]: import numpy as np

In [2]: import statsmodels.api as sm

In [3]: import statsmodels.formula.api as smf

# Load data
In [4]: dat = sm.datasets.get_rdataset("Guerry", "HistData").data

# Fit regression model (using the natural log of one of the regressors)
In [5]: results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()

# Inspect the results
In [6]: print(results.summary())

matplotlib plot settings

method 1 

import matplotlib

matplotlib.rc('xtick', labelsize=20)

matplotlib.rc('ytick', labelsize=20)

matplotlib.rc('font',family='sans-serif', size=12)

 

method 2

import matplotlib.pyplot as plt

 

SMALL_SIZE = 8

MEDIUM_SIZE = 10

BIGGER_SIZE = 12

 

plt.rc('font', size=SMALL_SIZE)          # controls default text sizes

plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title

plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels

plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels

plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels

plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize

plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title

 

method 3

matplotlib.rc('xtick', labelsize=20)

matplotlib.rc('ytick', labelsize=20)

matplotlib.rc('font', size=20)

matplotlib.rc('axes', titlesize=20)

matplotlib.rc('axes', labelsize=20)

matplotlib.rc('legend', fontsize=20)

matplotlib.rc('figure', titlesize=20)

 

method 4

#size=25

size=15

params = {'legend.fontsize': 'large',

          'figure.figsize': (20,8),

          'axes.labelsize': size,

          'axes.titlesize': size,

          'xtick.labelsize': size*0.75,

          'ytick.labelsize': size*0.75,

          'axes.titlepad': 25}

plt.rcParams.update(params)

 

beta-PERT Monte Carlo Simulation

 Here, we are running Beta-PERT Monte Carlo simulation.

from scipy import stats as stats

from scipy.stats import beta as beta

from scipy.stats import rv_continuous

import matplotlib.pylab as plt

class Beta_PERT(rv_continuous):

    def _shape(self, minimum, mode, maximum, lamb):

        alpha = 1+lamb*(mode-minimum)/(maximum-minimum)

        beta = 1+lamb*(maximum-mode)/(maximum-minimum)

        return [alpha,beta]

    

    def _cdf(self,x, minimum, mode, maximum, lamb):

        s_alpha, s_beta = self._shape(minimum, mode, maximum, lamb)

        z = (x-minimum)/(maximum-minimum)

        cdf = beta.cdf(z,s_alpha,s_beta)

        return cdf

    

pert = Beta_PERT(name="pert")

rv_1 = pert(0.02,0.05,0.2,4)

rv_2 = pert(1,5,20,4)

N = 5000

freq = rv_1.rvs(N)

loss = rv_2.rvs(N)

ALE = freq*loss

Common hedging strategies

1. Long/Short Equity Strategy 
2. Market Neutral Strategy 
3. Merger Arbitrage Strategy 
4. Convertible Arbitrage Strategy 
5. Capital Structure Arbitrage Strategy 
6. Fixed-Income Arbitrage Strategy 
7. Event-Driven Strategy 
8. Global Macro Strategy 
9. Short Only Strategy

https://www.wallstreetmojo.com/hedge-fund-strategies/