nsaconcierge.blogg.se - R weighted standard deviation

Now, on to constructing a portfolio and calculating volatility. Take a quick look at the monthly returns above, to make sure things appear to be in order. Head(portfolio_component_monthly_returns_xts) # AGG DBC EFA SPY VGT # Convert back to an xts, so we can use the cov() and StdDev() functions. Tq_transmute(mutate_fun = periodReturn, period = "monthly") %>% # a different time period like weekly or yearly. # Use the function from tidyquant note how easily we could change to # We need to gather into long format in order to use tq_transmute(). # I like to have the date column as the first column. # Remove the row.names column it's not needed anymore. # Convert to tibble so can stay in the tidyquant/verse. Let’s import prices and save them to an xts object. Those are AGG (a US bond fund), DBC (a commodities fund), EFA (a non-US equities fund), SPY (an S&P500 ETF), VGT (a technology fund). On to step 1, wherein we import prices and calculate returns for the 5 ETFs to be used in our portfolio.

Compare those to the standard deviation of monthly SPY returns.

a built-in function from performanceAnalytics.

Calculate the standard deviation of monthly portfolio returns using three methods:.

Import prices and calculate returns for 5 assets and construct a portfolio. Okay, without further ado, here’s where we are headed today: It’s a good template for other visualization derivations, even if standard deviation is old hat for you. This Notebook makes it exceedingly clear how we derive our final data visualizations on portfolio volatility. Second, as always, we have an eye on making our work reproducible and reusable. Plus, today we’ll look at a new way to convert daily prices to monthly using the tidyquant package, and that might offer enough new substance. I don’t think any investment professional looks back on hours spent pondering volatility as a waste of time. That said, I would humbly offer a couple of benefits to the R code that awaits us.įirst, volatility is important, possibly more important than returns.

R users with experience in the world of volatility may wish to skip this post and wait for the visualizations in the next one. Minimum Variance Portfolio: find minimum variance portfolio weightsĪ quick word of warning: this series begins at the beginning with portfolio standard deviation, builds up to a more compelling data visualization in the next post, and finally a nice Shiny app after that.