Category Archives: Quant finance
Volatility estimation and time-adjusted returns
Do non-trading days explain the mystery of volatility estimation? Previously The post “The volatility mystery continues” showed that volatility estimated with daily data tends to be larger (in recent years) than when estimated with lower frequency returns. Time adjusting One of the comments — from Joseph Wilson — was that there is a problem with … Continue reading
Posted in Quant finance, R language
Tagged S&P 500, time-adjusted returns, variance compression, volatility
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News analytics
Last week was the news analytics workshop at Birkbeck College. The idea There is room in news analytics for a large range of approaches. The leading model runs along the lines of: something happens a journalist (possibly a machine) creates a news item the news item is captured, time-stamped and given an id the news … Continue reading
The volatility mystery continues
How do volatility estimates based on monthly versus daily returns differ? Previously The post “The mystery of volatility estimates from daily versus monthly returns” and its offspring “Another look at autocorrelation in the S&P 500” discussed what appears to be an anomaly in the estimation of volatility from daily versus monthly data. In recent times … Continue reading
Posted in Quant finance, R language
Tagged autocorrelation, S&P 500, variance compression, volatility
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Alpha decay in portfolios
How does the effect of our expected returns change over time? This is not academic curiosity, we want to know in the context of our portfolio if we can. And we can — we visualize the effect of expected returns in situ. First step The idea is to look at the returns of portfolios that … Continue reading
Posted in Quant finance, R language, Random portfolios
Tagged alpha decay, expected returns, MACD, realized alpha
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A quant finance group on LinkedIn
Room with a view. A problem Most groups concerning finance on LinkedIn are full of garbage. Lots of items that don’t pertain to the real subject of the group, including lots that don’t pertain to much of anything. The “Quant Finance” group was an exception — it was almost completely on-target. However, it seems to … Continue reading
Asynchrony in market data
Be careful if you have global daily data. The issue Markets around the world are open at different times. November 21 for the Tokyo stock market is different from November 21 for the London stock market. The New York stock market has yet a different November 21. The effect The major effect is that correlations … Continue reading
Posted in Quant finance, R language
Tagged asynchronous data, asynchrony, multivariate moving average
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Another look at autocorrelation in the S&P 500
Casting doubt on the possibility of mean reversion in the S&P 500 lately. Previously A look at volatility estimates in “The mystery of volatility estimates from daily versus monthly returns” led to considering the possibility of autocorrelation in the returns. I estimated an AR(1) model through time and added a naive confidence interval to the … Continue reading
Posted in Quant finance, R language
Tagged autocorrelation, mean reversion, S&P 500, variance compression
5 Comments
The mystery of volatility estimates from daily versus monthly returns
What drives the estimates apart? Previously A post by Investment Performance Guy prompted “Variability of volatility estimates from daily data”. In my comments to the original post I suggested that using daily data to estimate volatility would be equivalent to using monthly data except with less variability. Dave, the Investment Performance Guy, proposed the exquisitely … Continue reading
Posted in Quant finance, R language
Tagged annualize, autocorrelation, S&P 500, variance compression, volatility
18 Comments
Some new ideas in financial mathematics
Financial mathematicians have built an increasingly elaborate structure around the idea of “the market” … In this article, I intend to challenge some of these foundational concepts with the intention of destabilizing the intellectual structure that has been erected on top of them and to demonstrate how a randomly generated portfolio can beat “the market,” … Continue reading
Variability of volatility estimates from daily returns
Investment Performance Guy has a post “Periodicity of risk statistcs (and other measures)” in which it is wondered how valid volatility estimates are from a month of daily returns. Here is a quick look. Figure 1 shows the variability (and a 95% confidence interval (gold lines) from a bootstrap) of the volatility estimate (black line) … Continue reading
