This post is about replicating the Turbulence Index, Correlation Surprise, and Absorption Ratio that was publisehd in Journal of Portfolio Management by Mark Kritzman of Wyndham Capital. Stay tuned!

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
import systemicRiskMeasures1 as srm   

import os
from pprint import pprint

pd.core.common.is_list_like = pd.api.types.is_list_like # Quick-fix for version compatibility correct version compatability.
import pandas_datareader.data as web  # module for reading datasets directly from the web
from pandas_datareader.famafrench import get_available_datasets

-----------------------------------------------------------
ModuleNotFoundError       Traceback (most recent call last)
<ipython-input-1-91eb8522a543> in <module>
      3 import matplotlib.pyplot as plt
      4 import scipy as sp
----> 5 import systemicRiskMeasures1 as srm
      6 
      7 import os

ModuleNotFoundError: No module named 'systemicRiskMeasures1'

In [7]:

datasets = get_available_datasets()
print('No. of datasets:{0}'.format(len(datasets)))
#datasets #comment out if you want to see all the datasets

No. of datasets:286

In [9]:

Start='19940131'
Start_Recession_Values='19940101'
End='20140131' #20140630 latest St Louis Recession data date
window_range= 60  #months

FmmaFrench49_from1926=pd.read_pickle('FF49_1926').resample('M').sum().loc[Start:End]
Recession_Values= pd.read_pickle('USARECM')
VIX_returns= pd.read_pickle('^VIX').resample('M').mean().loc[Start:End]

Balanced_port= srm.logreturns(Returns=pd.read_pickle('Probit_portfolio')).resample('M').sum().loc[Start:End]
Recession_Values= Recession_Values[Start_Recession_Values:] 

t1 = pd.read_pickle('Probit_portfolio')
SP500_TB= pd.read_pickle('Probit_portfolio').resample('M').mean().loc[Start:End]

In [10]:

Input=FmmaFrench49_from1926
Inputs=Input[window_range:]

In [12]:

MD_input=Inputs         #Change this value for data required
        #Run
SRM_mahalanobis= srm.MahalanobisDist(Returns=MD_input)[17:]

In [13]:

SRM_mahalanobis_turbulent_nonturbulent_days= srm.MahalanobisDist_Turbulent_Returns(MD_returns= SRM_mahalanobis, Returns=MD_input)
                    #drop inputs

In [11]:

Inputs=Inputs.drop('MD',1)
MD_input= MD_input.drop('MD',1)

/home/randlow/github/blog/content/articles/Econometrics/systemicRiskMeasures1.py:69: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  returns['MD']=MD_returns

In [ ]:

Corr_Input= Inputs
        #Run
SRM_Correlation_Surprise=srm.Correlation_Surprise(Returns=Corr_Input)  #need to reshift by 41 months
        #Graph
srm.Corr_plot(Corr_sur=SRM_Correlation_Surprise[0][17:], Mag_sur=SRM_Correlation_Surprise[1][17:],  width=25, figsize=(10,4.5), datesize='M')

In [ ]:

AR_input= Inputs
SRM_absorptionratio= srm.Absorption_Ratio(Returns= AR_input, halflife=int(8.5))                        #define Absorption Ratio
    #Graphs
srm.plot_AR(AR=SRM_absorptionratio, figsize=(10,2.5),yaxis=[0.84,0.92])

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