Cash Might be Your Tail Risk
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Just like James Montier Ode to the Joy of Cash and David Merkel Got Cash?, I think cash is an extremely powerful tool. Of the 3 ingredients (land, labor, and capital) of the economy, capital (cash) is most scarce at the end of a crisis or recession while land and labor are most plentiful. Its scarcity in disaster rewards patient holders of cash when opportunity is most plentiful and rewards most certain. Deflation, or alternately stable to inflating currency, also rewards the most risk averse with high cash allocations.
However, if your cash is the US $, owning US $ throughout and at the end of the next collapse will not be rewarded. Please expand your definition of cash to include other currencies besides the US $, and do not let home bias determine your cash denomination. The tail risk most ignored in the average US investors’ portfolios is the US $. If nothing else, at least monitor the currency markets. You very easily could hold the most overowned and least scarce asset in the world (the US $). Argentine Peso was not good in 2001. Russian Ruble was not good in 1998. Thai Baht, Malaysian Ringgit, and Korean Won were not good in 1997. Mexican Peso was not good in 1994. Cash is fine as long as cash is not the source of the tail risk.
 |
| From TimelyPortfolio |
Fortunately, the Euro has been a distraction, but at some point the focus will shift to the US. Don’t fight the Fed generally works in stocks, and it also definitely applies to the US $ here.
#monitor currencies require(quantmod) #get currency data from the FED FRED data series
Korea <- getSymbols("DEXKOUS",src="FRED",auto.assign=FALSE) #load Korea
Malaysia <- getSymbols("DEXMAUS",src="FRED",auto.assign=FALSE) #load Malaysia
Singapore <- getSymbols("DEXSIUS",src="FRED",auto.assign=FALSE) #load Singapore
Taiwan <- getSymbols("DEXTAUS",src="FRED",auto.assign=FALSE) #load Taiwan
China <- getSymbols("DEXCHUS",src="FRED",auto.assign=FALSE) #load China
Japan <- getSymbols("DEXJPUS",src="FRED",auto.assign=FALSE) #load Japan
Thailand <- getSymbols("DEXTHUS",src="FRED",auto.assign=FALSE) #load Thailand
Brazil <- getSymbols("DEXBZUS",src="FRED",auto.assign=FALSE) #load Brazil
Mexico <- getSymbols("DEXMXUS",src="FRED",auto.assign=FALSE) #load Mexico
India <- getSymbols("DEXINUS",src="FRED",auto.assign=FALSE) #load India
USDOther <- getSymbols("DTWEXO",src="FRED",auto.assign=FALSE) #load US Dollar Other Trading Partners
USDBroad <- getSymbols("DTWEXB",src="FRED",auto.assign=FALSE) #load US Dollar Broad #combine all the currencies into one big currency xts
currencies <- merge(1/Korea, 1/Malaysia, 1/Singapore, 1/Taiwan,
1/China, 1/Japan, 1/Thailand, 1/Brazil, 1/Mexico, 1/India,
USDOther, USDBroad)
currencies <- na.omit(currencies)
colnames(currencies) <- c("Korea", "Malaysia", "Singapore", "Taiwan",
"China", "Japan", "Thailand", "Brazil", "Mexico", "India",
"USDOther", "USDBroad") #use sde MODist package as described in the fine presentation
#http://www.rinfinance.com/agenda/2011/StefanoIacus.pdf
require(sde) currenciesROC <- as.zoo(ROC(currencies,1,type="discrete"))
d <- MOdist(ROC(currencies,1,type="discrete"))
cl <- hclust( d )
groups <- cutree(cl, k=4)
#jpeg(filename="currencies.jpg",quality=100,width=6.5, height = 6.5, units="in",res=96)
plot(as.zoo(currencies), col=groups, main="Various Asian and American Currencies
1995-Current")
#dev.off()
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.