The paper titled, Efficient Estimation of Volatility using High Frequency Data, is about testing a set of volatility estimators using high frequency data. I will attempt to briefly summarize the paper. For a person working in the financial markets, there is not a day that goes by without hearing the word, “volatility’’. Yet, it is something that is not observed. If you assume that stocks follow some random process like a GBM, then the relevant question to ask is, “How does one estimate the diffusion parameter/process in the model?

The paper titled, Regression analysis with many specifications, uses stationary bootstrap method to evaluate a large set of models. In a typical data mining set up, the problem of choosing the number of covariates can be handled in many ways such as Best subset selection Forward or/ and backward regression Forward stage wise regression Lasso Ridge regression Combination of Lasso and Ridge regression

The paper titled, “Consistent High-Precision Volatility from High Frequency Data”, talks about the trade off that one needs to make while considering sampling frequency. If you increase sampling frequency, the measurement error goes down but microstructure noise increases. If you decrease sampling frequency, the microstructure noise decreases but the measurement error goes up. Researchers in the past have suggested the usage of 10min/20min/xmin intervals based on some visual tools that have fancy names such as volatility signature plots.

Geeta Ma’am & Kathalaya find a new audience : India Inc Here’s a link to an article in the telegraph

Books on derivative pricing come in all shades and colors. Some books give a brief introduction of derivatives at a leisurely pace and then suddenly the content becomes very mathematical. There are some books that have theorems and lemmas all through. There are some books that talk about risk-neutral pricing giving very little intuition about the concept. In the gamut of books available, I think this book stands out for a couple of reasons.