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Financial Turbulence Example

01 Dec 2012

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Today, I want to highlight the Financial Turbulence Index idea introduced by Mark Kritzman and Yuanzhen Li in the Skulls, Financial Turbulence, and Risk Management paper. Timely Portfolio did a great series of posts about Financial Turbulence:

As example, I will compute Financial Turbulence for the equal weight index of G10 Currencies.

I created a helper function get.G10() function in data.r at github to download historical data for G10 Currencies from FRED.

Let’s compute Financial Turbulence Index for G10 Currencies.

	#*****************************************************************
	# Load historical data
	#*****************************************************************
	library(SIT)
	load.packages('quantmod')
	
	fx = get.G10()
		nperiods = nrow(fx)

	# Check data, plot FX vols
	ret = diff(log(fx))
	hist.vol = sqrt(252) * bt.apply.matrix(ret, runSD, n = 20)		
	plota.matplot(hist.vol,main='FX Vol')

plot of chunk plot-2

Swiss currency, DEXSZUS - Switzerland/U.S., dropeed 13% in a single day due to removal of trading restrictions.

	#*****************************************************************
	# Rolling estimate of the Financial Turbulence for G10 Currencies
	#****************************************************************** 
	turbulence = fx[,1] * NA
		colnames(turbulence) = 'turbulence'
	ret = coredata(fx / mlag(fx) - 1)

	look.back = 252

	for( i in (look.back+1) : nperiods ) {
		temp = ret[(i - look.back + 1):(i-1), ]

		# measures turbulence for the current observation
		turbulence[i] = mahalanobis(ret[i,], colMeans(temp), cov(temp))
	}	

	# DEXSZUS - Switzerland/U.S.
	print(to.nice(cbind(fx,turbulence)['2015-01-14::2015-01-16',]))
  DEXCAUS DEXJPUS DEXNOUS DEXSDUS DEXSZUS DEXUSAL DEXUSEU DEXUSNZ DEXUSUK turbulence
2015-01-14 1.20 116.78 7.64 8.05 1.02 1.23 0.85 1.29 0.66 23.03
2015-01-15 1.19 116.95 7.65 8.14 0.89 1.22 0.86 1.28 0.66 17,225.61
2015-01-16 1.20 117.45 7.60 8.14 0.85 1.22 0.87 1.29 0.66 47.83 
	#*****************************************************************
	# Plot 30 day average of the Financial Turbulence for G10 Currencies
	#****************************************************************** 	
	plota(EMA( turbulence, 30), type='l', 
		main='30 day average of the Financial Turbulence for G10 Currencies')

plot of chunk plot-3

	#*****************************************************************
	# Same plot with 2015-01-15 removed
	#****************************************************************** 	
	turbulence['2015-01-15']=NA
	plota(EMA( ifna.prev(turbulence), 30), type='l', 
		main='30 day average of the Financial Turbulence for G10 Currencies, 2015-01-15 removed')

plot of chunk plot-3

There is a big spike in the index during 2008-2009 period. If you had monitored the Financial Turbulence Index and reduced or hedged your positions during these times, you would be able to reduce your draw-downs and sleep better at night.

To view the complete source code for this example, please have a look at the bt.financial.turbulence.test() function in bt.test.r at github.

(this report was produced on: 2015-03-14)

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