[youtube https://www.youtube.com/watch?v=4OBLAW7oQYo?rel=0]

The paper titled, “A Simple Long Memory Model of Realized Volatility”, is one of the most cited papers in the area of long memory volatility models. One typically assumes that log prices follow an arithmetic random walk. In this kind of set up, it has been shown in the previous research that integrated volatility of Brownian motion can be approximated to any arbitrary precision using the sum of intraday squared returns.

The paper, “Determining the best forecasting models”, is about testing 55 models that belong to the GARCH family. If we have just one model and a straw model, it is easy to show that some statistic on the test sample that the hypothesized model is superior. How do we go about testing a set of competing models? There are many wonderful techniques in the Bayesian world. However this paper is more frequentist in nature.

The paper titled, “Liquidity considerations in estimating implied volatility”, by Susan Thomas and Rohini Grover, is about a new way of constructing volatility index that is based on weighing the implied volatility of the options based on the relative spreads at various strikes. The key idea behind the paper is that there is considerable liquidity asymmetry across various strikes for the near month and mid month contracts on NIFTY options. This leads the authors to hypothesize a measure that is based on weighing implied volatilities.

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?