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R - one-step-ahead Stochastic Volatility for 5-minute VWAP prices
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R - one-step-ahead Stochastic Volatility for 5-minute VWAP prices

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R - one-step-ahead Stochastic Volatility for 5-minute VWAP prices

I found this interesting so am cross-posting it from Quant Stack Exchange.

Source: r - one-step-ahead Stochastic Volatility for 5-minute VWAP prices - Quantitative Finance Stack Exchange

If anyone can comment on the accuracy of the formula, please do so either here on futures.io (formerly BMT) or on the original question URL.


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I'm trying to run an SV model against prices of Euro/USD. For those not familiar with SV, its a volatility model in which each point gets its own volatility parameter ht with 3 main parameters that are derived using a monte carlo simulation (MCMC),

μ = average of the volatility across the entire sample set
ϕ = the weighting that the volatility of the last point has on thenext (predicted) volatility
ht = the most recent - {time interval}'s volatility.
σ = volatility of the volatility
Once we have μ, ϕ, and ht we can predict ht+1, that is, the predicted volatility of the next point, by ht+1∼Normal(μ+ϕ(ht−μ),σ).

I'm wondering if I'm doing the next part correctly:

To put this into practice, we run a MCMC every 5 minutes, gathering those variables and predicting ht+1. This results in a graph like this, for CME futures contract 6EU4 (September euro) with 5-period BBands also displayed. Ignore the shapes that appear on the graph.

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It feels inaccurate, so I'm not sure if I'm doing something wrong. Did I understand the process correctly? phi is around .55 for the first half of the day, then jumps to .95 and stays there, which seems wrong, but I guess it isn't too surprising given the data...

R's package stochvol is taking care of the parameter estimation, so assume that the numbers themselves are accurate.

Should I paste the 5-minute Volume Weighted Average Prices here? its a pretty long data set. I'll edit it to do that if so.

Mike

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Looks really interesting.

Here is a test script pieced from the manual. I have no idea about the actual question though.

set.seed(123)
library("stochvol")
library("quantmod")

stockData <- new.env()
startDate = as.Date("2004-01-13")
endDate = as.Date("2014-08-25")
tickers <- c("SPY","QQQ")
getSymbols(tickers, env = stockData, src = "yahoo", from = startDate, to = endDate)

ret <- dailyReturn(stockData$QQQ,type='log')

res <- svsample(ret, priormu = c(-10, 1), priorphi = c(20, 1.1),priorsigma = .1)

volplot(res, forecast = 100)

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