PnL data - PerformanceAnalytics /

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PnL data - PerformanceAnalytics /

Johan Palleschitz
Good evening all,

I have PnL data in EUR that i want use for ES, VaR calculationw/reports, as described in the documentation.

I have read the PerformanceAnalytics guideline, searched on SO but i do not find a solution that works for me.

Any suggestion or indication where i should look would be very appreciated!!

Have a nice evening!!
Regards
Johan

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Re: PnL data - PerformanceAnalytics /

braverock
VaR/ES should work with a distribution of cash P&L as well as a
distribution of returns.

Can you present an example that isn't behaving as you expect it to?

Regards,

Brian 

On Wed, 2021-02-03 at 17:58 +0000, Johan Palleschitz wrote:

> Good evening all,
>
> I have PnL data in EUR that i want use for ES, VaR
> calculationw/reports, as described in the documentation.
>
> I have read the PerformanceAnalytics guideline, searched on SO but i
> do not find a solution that works for me.
>
> Any suggestion or indication where i should look would be very
> appreciated!!
>
> Have a nice evening!!
> Regards
> Johan
>
> _______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-finance
> -- Subscriber-posting only. If you want to post, subscribe first.
> -- Also note that this is not the r-help list where general R
> questions should go.
>

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Re: PnL data - PerformanceAnalytics /

Johan Palleschitz
Good afternoon Brian and thank you for getting back to me, much appreciated!

I have attached the sample data below.

data as:
"xts" "zoo"

Weights calculated as
df_w <- df[,1:3]/rowSums(df[,1:3])

> df
Date
FX
 Shares
 Commodities
2020-01-02
           892
         8,609
       17,864
2020-01-03
       9,971
       10,454
         3,478
2020-01-04
       9,446
       19,453
       40,373
2020-01-05
-      7,362
       15,760
       35,030
2020-01-06
-      5,006
       13,780
-        1,786
2020-01-07
-      3,760
            757
-      34,151
2020-01-08
-      3,739
         6,265
-      41,606
2020-01-09
-      5,589
       22,874
-      23,932
2020-01-10
-      8,482
       18,005
       10,922
2020-01-11
-      4,884
         2,465
       25,374
2020-01-12
-      1,140
       16,825
       36,941
2020-01-13
-      4,341
         1,413
       17,187
2020-01-14
-         746
       18,655
         7,193
2020-01-15
-         497
-      20,688
-      30,418
2020-01-16
-      9,872
-        9,579
         6,703
2020-01-17
       6,011
-      11,616
       16,286
2020-01-18
       4,023
-        8,166
       45,782
2020-01-19
       5,446
-      23,821
       26,343
2020-01-20
       1,173
-        3,731
         1,126
2020-01-21
       3,858
-      17,266
       27,975
2020-01-22
       8,586
-        9,468
       38,842
2020-01-23
           467
       11,088
       19,451
2020-01-24
-      9,049
       14,817
         2,098
2020-01-25
-      2,324
       23,952
       35,510
2020-01-26
-      6,534
         2,902
-      20,131
2020-01-27
-      1,641
         7,147
-        6,921
2020-01-28
       3,974
       13,986
-      24,161

I have tested some functions from PerformanceAnalytics

1.     table.Arbitrary(df,
                metrics=c("VaR","mean"),
                metricsNames=c("modVaR","mean"),
                p=.95)

                          FX    Shares Commodities
Average Return     -782.1784  4624.844    8569.309
Standard Deviation 5705.4913 13602.512   24924.682

Ok

2.     table.Arbitrary(df,
                metrics=c("VaR", "ES"),
                metricsNames=c("Modified VaR","Modified Expected Shortfall"))

VaR calculation produces unreliable result (risk over 100%) for column: 1 : 9603.44809651169
VaR calculation produces unreliable result (risk over 100%) for column: 1 : 19496.0283854767
VaR calculation produces unreliable result (risk over 100%) for column: 1 : 34970.4906301062,
etc.
              FX   Shares Commodities
modVaR   -1.0000   -1.000      -1.000
mean   -782.1784 4624.844    8569.309

modVaR does not make sense. (well, the values are there, after "VaR calculation produces unreliable result (risk over 100%) for column: 1 : "
Mean works fine.

3.     table.Distributions, works fine.

4.     table.DownsideRisk, give similar error as table.Arbitrary does above for VaR and ES.

VaR calculation produces unreliable result (risk over 100%) for column: 1 : 8879.0654029
ES calculation produces unreliable result (risk over 100%) for column: 1 : 9460.7561265, .... etc

                                    FX    Shares   Commodities
Historical VaR (95%)            -1.000    -1.000 -1.000000e+00
Historical ES (95%)             -1.000    -1.000 -1.000000e+00
Modified VaR (95%)              -1.000    -1.000 -1.000000e+00
Modified ES (95%)               -1.000    -1.000 -1.000000e+00

5. table.RollingPeriods and table.Stats, works fine.

6. chart.Histogram does not work, same error as above.

7. Expected Shortfall

> # first do normal ES calc
> ES(df, p=.95, method="historical")
ES calculation produces unreliable result (risk over 100%) for column: 1 : 9460.7561265
ES calculation produces unreliable result (risk over 100%) for column: 2 : 22254.650985
ES calculation produces unreliable result (risk over 100%) for column: 3 : 37878.379905
   FX Shares Commodities
ES -1     -1          -1

> # now use modified Cornish Fisher calc to take non-normal distribution into account
> ES(df, p=.95, method="modified")
ES calculation produces unreliable result (risk over 100%) for column: 1 : 10844.7485269339
ES calculation produces unreliable result (risk over 100%) for column: 2 : 22667.3644584819
ES calculation produces unreliable result (risk over 100%) for column: 3 : 40970.5338686356
   FX Shares Commodities
ES -1     -1          -1

When i use weights = df_w (df_w <- df[,1:3]/rowSums(df[,1:3])) i get the following error message.

> ES(df, portfolio_method="component", weights = df_w)
Error in ES(df, portfolio_method = "component", weights = df_w) :
  number of items in weights not equal to number of columns in R

So my queation is as follows:

1. Can i use the following chart.* table.* with my data? - chart.Histogram, chart.Boxplot(df), chart.QQPlot(df), chart.VaRSensitivity(df), chart.BarVaR, ...
2. Do i calculate the weights (df_w) correctly? it does not seem to be the case. SHould i even include it?

I would like to be able to visualise boxplots, Histograms with methods, VaRSensitivity and tables/stats per Assetclass and total.
Without being able to understand how to solve this, there seems to be some tweek i miss at an initial state that might solve the "ES / VaR calculation produces unreliable result (risk over 100%) for column: 1(n) "

Any guidance / hints would be greaT
Wish you all a nice start to the week.

Best
Johan

From: Brian G. Peterson<mailto:[hidden email]>
Sent: den 5 februari 2021 02:58
To: Johan Palleschitz<mailto:[hidden email]>; [hidden email]<mailto:[hidden email]>
Subject: Re: [R-SIG-Finance] PnL data - PerformanceAnalytics /

VaR/ES should work with a distribution of cash P&L as well as a distribution of returns.

Can you present an example that isn't behaving as you expect it to?

Regards,

Brian

On Wed, 2021-02-03 at 17:58 +0000, Johan Palleschitz wrote:
Good evening all,

I have PnL data in EUR that i want use for ES, VaR calculationw/reports, as described in the documentation.

I have read the PerformanceAnalytics guideline, searched on SO but i do not find a solution that works for me.

Any suggestion or indication where i should look would be very appreciated!!

Have a nice evening!!
Regards
Johan

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