Phd thesis on stock market volatility

Phd thesis on stock market volatility

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where denotes price on day, phd thesis on stock market volatility. I mentioned this question briefly in this post, when I was explaining how people compute market volatility. I encourage anyone who is interested in this technical question to read that post, it really explains the reasoning well. I wanted to add two remarks to the discussion, however, which actually argue for not using log returns, but instead using percentage returns in some situations.

You can get lots of free market data here and try this out yourself empirically, but it also makes sense. Therefore when you approximate returns as log normal, you should probably stick to daily returns. So instead you try different parametric families and compare. This is usually not what people expect about the market, especially considering that there does not exist an infinite amount of money yet!

Cathy, could part of the reason that log returns are assumed be because there are a hell of a lot less nodes on the binomial tree, due to recombination? Am I misremembering? Like Like. Like Liked by 1 person. If I model a 2D random phd thesis on stock market volatility with percentage steps, rather than constant-move steps, is the resulting distribution of end results approaching a lognormal distribution I think so.

So, e. Cathy, is it unrealistic to bootstrap with real data only because super-bad market days are so far in the past that we only have low-res records? Lastly — did you ever use Cauchy? Quants move quickly to trinomial or jump-diffusion models as a second-order solution to address intra-period volatility spikes.

Again my chops are rusty. Exp maps Brownian motion or phd thesis on stock market volatility walks on -oo,oo to processes on 0,oo. Invariance under additive shifts and statistics for increments turn into scale invariance and statistics for log returns. So it is very natural and convenient to use log returns for analysis or statistics on scale-invariant price series that live on 0,oo. Donsker Take exp, and you see a model built using scaled, finite-var, iid returns will converge to geometric Brownian motion where exp and log are natural.

Agree that the recombining property of binary trees is crucial to make the backward propagation of CRR-type computational methods efficient.

You may have more luck with trinomial trees with varying probabilities positive for stability or grids. Would be interesting to learn what people are doing with these fat tail distributions. You lose a lot with no higher moments, or even an expectation. And continuity is nice too, but the point is it may not be realistic for all things. FWIW, square of log returns looks like phd thesis on stock market volatility market standard phd thesis on stock market volatility measuring realized variance.

Meucci argues that for stocks you should use log returns for estimation since they are invariants and arithmetic returns are not. However, he says optimization should occur on arithmetic returns since that is what you actually will phd thesis on stock market volatility as an investor. As far as I can tell, E X is undefined if the degrees of freedom is less than 1 and Variance X is infinite if degrees of freedom is less than or equal to 2.

Hence, your problem is that you have some extreme outliers that have caused you to estimate the degrees of freedom to be very small. My solution is to clean the data in order to reduce the impact of outliers before estimating any t-distribution. Hmmm… no. Prices themselves are never what we care about, we only care about the change of prices.

There are lots of ways to do this but they never involve prices themselves, and usually they are set to be scale-independent. I do think I was being sloppy though. When I say log-normal distribution of returns, I really should have said normal distribution of log returns.

So good phd thesis on stock market volatility discover somebody with a few genuine thoughts on this subject matter. thank you for starting this up.

This web site is something that is needed on the internet, someone with a little originality! This is a good discussion, as far as it goes. The point to note here is that there have been multiple — multiple! But to take just one example, in Oct the market dropped More to the point, frequency distributions assume independence of daily returns but, again in the tails, we see that there can be bursts of dependence, phd thesis on stock market volatility.

Clearly, there is a big problem with any of the approaches that are most widely used in the markets today in explaining the widely divergent behaviors between the body of the distributions versus the tails.

The intractability inherent in adopting the more extreme valued distributional assumptions seems to be the barrier to their wider use, e. These are delicate problems that remain hotly contested areas of research. Email Address:. mathbabe Exploring and venting about quantitative issues. Home About HCSSiM Cool math books Contact. Why log returns? August 30, Cathy O'Neil, mathbabe, phd thesis on stock market volatility.

Share this: Twitter Facebook Email Print Reddit. Like this: Like Loading Categories: data sciencefinancehedge funds. Comments FoW Like Like. Questions: 1. How practically hard is it to run a recombinant tree without lognormal returns? The Quantivity link is very nice. Random thoughts— Exp maps Brownian motion or random walks on -oo,oo to processes on 0,oo. Cathy O'Neil, mathbabe. toric contact lenses wiki, phd thesis on stock market volatility. Links 30 Mar « Pink Iguana.

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How Are Market Liquidity and Volatility Related? ☝

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Jun 02,  · Stock Market News. Cryptocurrency News a follow-up on our December letter where we wrote extensively about our broad thesis about the Artificial Intelligence opportunity. a PhD May 25,  · Innovative Technology Fund I, a Qualified Opportunity Zone Fund launches and is seeking accredited investors for Technology and Real Estate investment. The Fund is seeking a $10,, Series A Feb 08,  · PHD Thesis Writing Assistance. Write My Thesis For Me. Pay For Thesis Paper; Tiller () explains that if large institutions dump their stake in a company due to market volatility, it will experience a significant drop in the value or performance of its stock