[youtube https://www.youtube.com/watch?v=XNbaR54Gpj4?list=UUAuUUnT6oDeKwE6v1NGQxug]

The paper titled, “Answering the Skeptics : Yes, Standard Volatility models do provide accurate forecasts” is a classic paper on volatility modeling by Andersen and Bollerslev. What’s this paper about ? If you build a volatility model, How do you go about testing it ? This is the key question answered in the paper. At a daily frequency or intraday frequency, returns do not show serial correlation. However there is a serial dependency amongst them.

Crisis hits financial markets at regular intervals but the market participants keep assuming that they “understand the behavior” of markets and are in “total control” of the situation until the day things crash. There is an army of portfolio managers, equity research analysts, macro analysts, low frequency quants,derivative modeling quants, high frequency quants etc., all trying to understand the markets and trying to make money out of it. Do their gut /intuitive/quant models come close to how the market behaves ?

This paper is just 8 pages long but conveys an important point about random walk tests. The paper analyzes the use of variance and absolute variation as measures of volatility while testing a series for random walk. The paper suggest the following plot : for different values of zeta. For zeta=1 ,one ends up using absolute variation and for zeta=2, one ends up using variance. If the time series has fat tails, it might happen that variance ratio plots do not show anything fishy.

This paper by Lo and MacKinlay analyze the effects of non synchronous trading on stochastic properties. The transaction data of any asset traded in an exchange is irregularly spaced. Homogeneous time series is an artifact. Non Homogeneous time series is the reality. For example, the daily prices of securities quoted in the news papers as “closing prices” are not the prices that are exactly traded at the very last second of the market close.