What to make of historical stock market volatility?
"The first thing to realize is that the type of chart you saw is not appropriate for long-term comparisons.  The vertical axis uses a linear scale, where each unit occupies the same amount of space.  This is visually misleading because the relevant information at any point in the chart is ""how much is the value going up or down?"" and ""how much"" change depends on how much the value of the investment is at that moment.  For example, if you buy something at $10 and the price changes $1, that is significant, 10%.  If you buy something at $1000 and the price changes $1, that is not so significant, only 0.1%.  The problem in that chart is that 100 Dow points occupy the same space whether the Dow is at 870 or 10800. To get a better feel for the volatility, you should use a log (logarithmic) scale.  Google has an option for this.  Using it shows:   In this chart you can see that the volatility appears much less extreme in recent years.  True, the 2006-2009 change is the largest drop, and there might be slightly higher volatility generally, but it is not nearly as extreme-looking.  The drops in 1974 and 1987 can be seen to be significant."
"Since these indices only try to follow VIX and don't have the underlying constituents (as the constituents don't really exist in most meaningful senses) they will always deviate from the exact numbers but should follow the general pattern. You're right, however, in stating that the graphs that you have presented are substantially different and look like the indices other than VIX are always decreasing. The problem with this analysis is that the basis of your graphs is different; they all start at different dates... We can fix this by putting them all on the same graph:  this shows that the funds did broadly follow VIX over the period (5 years) and this also encompasses a time when some of the funds started. The funds do decline faster than VIX from the beginning of 2012 onward and I had a theory for why so I grabbed a graph for that period. My theory was that, since volatility had fallen massively after the throes of the financial crisis there was less money to be made from betting on (investing in?) volatility and so the assets invested in the funds had fallen making them smaller in comparison to their 2011-2012 basis.  Here we see that the funds are again closely following VIX until the beginning of 2016 where they again diverged lower as volatility fell, probably again as a result of withdrawals of capital as VIX returns fell. A tighter graph may show this again as the gap seems to be narrowing as people look to bet on volatility due to recent events. So... if the funds are basically following VIX, why has VIX been falling consistently over this time? Increased certainty in the markets and a return to growth (or at least lower negative growth) in most economies, particularly western economies where the majority of market investment occurs, and a reduction in the risk of European countries defaulting, particularly Portugal, Ireland, Greece, and Spain; the ""PIGS"" countries has resulted in lower volatility and a return to normal(ish) market conditions. In summary the funds are basically following VIX but their values are based on their underlying capital. This underlying capital has been falling as returns on volatility have been falling resulting in their diverging from VIX whilst broadly following it on the new basis." Check the answers to this Stackoverflow question https://stackoverflow.com/questions/754593/source-of-historical-stock-data a number of potential sources are listed ATR really looks at the volatility within the day -- So you would be able to see if the stock is becoming more or less volatile in daily trading.  This is often useful for charting and finding entry and exit locations.   Traditional historic volatility (as you cited) will give you a look at the long term volatility of the security.  The example of this is that there could be trends up or down but the same daily volatility (same ATR) There are methods that try to incorporate both intraday information along with historic volatility.  As for which is a better measure of volatility-- it depends on what you are using the measure for. My level of analysis is not quite that advanced. Can you share what that would show and why that particular measure is the one to use?  I've run regression on prices between the two. VIX prices have no correlation to the s&amp;p500 prices. Shouldn't true volatility result in the prices (more people putting options on the VIX during the bad times and driving that price up) correlate to the selloff that occurs within the S&amp;P500 during recessions and other events that would cause significant or minor volatility? My r2 showed no significance within a measurement of regression within Excel.  But, *gasp* I could be wrong, but would love to learn more about better ways of measurement :) Dow Jones: http://en.wikipedia.org/wiki/Historical_components_of_the_Dow_Jones_Industrial_Average  NASDAQ: http://en.wikipedia.org/wiki/NASDAQ-100 (scroll down) S&P Tricky. From what I can find, you need to be in Harvard Business School, a member of CRSP, or have access to Bloomberg's databases. S&P did have the info available years ago, but no longer that I can find. "When people (even people in the media) say: ""The stock market is up because of X"" or ""The stock market is down because of Y"", they are often engaging in what Nicolas Taleb calls the narrative falacy. They see the market has moved in one direction or another, they open their newspaper, pick a headline that provides a plausible reason for the market to move, and say: ""Oh, that is why the stock market is down"". Very rarely do statements like this actually come from research, asking people why they bought or sold that day. Sometimes they may be right, but it is usually just story telling. In terms of old fashioned logic this is called the ""post hoc, ergo proper hoc"" fallacy. Now all the points people have raised about the US deficit may be valid, and there are plenty of reasons for worrying about the future of the world economy, but they were all known before the S&P report, which didn't really provide the markets with much new information. Note also that the actual bond market didn't move much after hearing the same report, in fact the price of 10 year US Treasury bonds actually rose a tiny bit. Take these simple statements about what makes the market go up or down on any given day with several fistfuls of salt." I think you're missing Simon Moore's point. His point is that, due to low inflation, the returns on almost all asset classes should be less than they have been historically, so we shouldn't rebalance our portfolio or withdraw from the market and hold cash based on the assumption that stocks (or any other asset) seem to be underperforming relative to historical trends.  His last paragraph is written in case someone might misunderstand him, he is not advocating to hold cash, just that investors should not expect as good returns as has happened historically, since those happened in higher inflation environments. To explain: If the inflation rate historically has been 5% and now it's 2%, and the risk-free-market return should be about 2%, then historically the return on a risk-free asset would be 7% (2%+5%), and now it should be expected to be 4% (2%+2%). So, if you have had a portfolio over some time you might be concerned that the rate of return is worsening, but Simon's point is that before you sell off your stocks / switch investment brokers, you should try to figure out if inflation is the cause of the performance loss. On the subject  of cash: cash always loses value over time from inflation, since inflation is a measure of the increase in prices over time-- it's a part of the definition of what inflation is.  That said, cash holdings lose value more slowly when inflation is lower, so they are relatively less worse than before. The future value of cash doesn't go up in low inflation (you'd need deflation for that), it just decreases at a lower rate, that is, it becomes less expensive to hold- but there still is a price. As an addendum, unless a completely new economic paradigm is adopted by world leaders, we will always see cash holdings decrease in value over time, since modern economics holds that deflation is one of the worst things that can happen to an economy. If you're worried about volatility, and you're in mostly long positions, you should be looking to diversify your portfolio (meaning, buying some stocks that will do better in a bear market) if it's not already diverse, but you shouldn't be looking to abandon your positions, unless you anticipate a short-term need for cash.  Other than that, you may want to hold off on the short-term positions for a while if you're concerned about volatility, though many traders see volatility as a great time to make money (as there is more movement, there's more opportunity to make money from mispriced stocks in both directions).  Unless you think the market will be permanently down due to these reasons, anyway, but I don't see any reason to believe that yet.  Even World War Two wasn't enough to permanently hurt the market, after all! Remember that everyone in the market knows what you do.  If there were a sure thing that the market was going to crash, it already would have. Conservative positions tend to involve holding onto a well diversified portfolio rather than simply holding onto cash, unless the investor is very conservative (in which case the portfolio should be cash anyway). The fact that you say this is your rainy day fund does make me a little curious, though; typically rainy day funds are better in cash (and not invested) since you might hit that rainy day and need cash quickly (in which case you could take significant losses if the time isn't right). "Stock support and resistance levels mean that historically, there was ""heavy"" buying/selling at those levels. This suggests, but does not guarantee, that ""someone"" will buy at ""support"" levels, and ""someone"" will sell at ""resistance levels. Any ""history"" is meaningful, but most analysts will say that after six months to a year, the impact of events declines the further back in time you go. They can be meaningful for periods as short as days." "Their algorithm may be different (and proprietary), but how I would to it is to assume that daily changes in the stock are distributed normally (meaning the probability distribution is a ""bell curve"" - the green area in your chart).  I would then calculate the average and standard deviation (volatility) of historical returns to determine the center and width of the bell curve (calibrating it to expected returns and implied volaility based on option prices), then use standard formulas for lognormal distributions to calculate the probability of the price exceeding the strike price. So there are many assumptions involved, and in the end it's just a probability, so there's no way to know if it's right or wrong - either the stock will cross the strike or it won't." Historical volatility of a stock is going to be based on past performance, basically its current trend.  That can be useful, but really is no indication of how it will perform in the future.  Especially with a big swing in the market. Now if you're talking about implied volatility (IV) of an options contract, that's a little different.  IV is derived from an option’s price and shows what the market “implies” about the stock’s volatility in the future. Thus it is based on the actions of active traders and market makers.  So, it gives you a bit more insight into what's going on, but at times has less to do with fundamentals. I guess a good way to think of IV based on options contracts is as an educated opinion, of the market as a whole, with regards to how much that stock could likely move over a period of time (options expiration). Also note that IV represents the potential for a stock to move, but it does not forecast direction. I don't know of any studies off the top of my head, but I'm sure there have been plenty. So basically they are trying to see two things.  One is whether prices are correlated to each other for long periods of time as a preliminary study suggested (which would go against efficient markets hypothesis, since you could use that info to game the market) or if that result is illusory and the long term returns are close to a standard normal distribution which would follow the effiecient markets hypo.  The second thing I don't follow as well, but they're trying to solve the first thing so that they can then look at why, when they look at returns at different time scales, (1minute, one hour, one week), the model which had been proposed for these returns is not supported by the data  (the first thing). They say that the old model (Levy) says that the variability should not be the same at the different time scales, but the data suggests that it is.  So they then propose a modification of the old Levy model, and say that it would also explain the strange first result they looked at. (that prices are correlated for longer periods).  That probably doesn't make any sense, but you might have more luck by posting in /r/statistics. There are lots of good answers on here already.  There are actually lots of answers for this question.  Lots.  I have years of experience on the exchange feed side and there are hundreds and thousands of variables.   All of these variables are funneled into systems owned by large financial institutions (I used to manage these - and only a few companies in the world do this so not hard to guess who I work for).  Their computers then make trades based on all of these variables and equations.  There are variables as whacky as how many times was a company mentioned in an aggregate news feed down to your basic company financials.  But if there is a way to measure a company (or to just guess) there is an equation for it plugged into a super computer at a big bank. Now there are two important factors on why you see this mad dash in the morning: Now most of the rest of the day is also automated trades but by the time you are an hour into market open the computers for the most part have fulfilled their calendar buys.  Everyone else's answer is right too.  There is futures contracts that change, global exchange info changes, options expiring, basic news, whatever but all of these are amplified by the calendar day changing. This link does it ok:  http://investexcel.net/1979/calculate-historical-volatility-excel/ Basically, you calculate percentage return by doing stock price now / stock price before.  You're not calculating the rate of return hence no subtraction of 100%.  The standard is to do this on a daily basis: stock price today / stock price yesterday. The most important and most misunderstood part is that you now have to analyze the data geometrically not arithmetically.  To easily do this, convert all percentage returns with the natural log, ln(). Next, you take the standard deviation of all of those results, and apply exp().  This answers the title of your question. For convenience's sake, it's best to annualize since volatility (implied or statistical) is now almost always quoted annualized.  There are ~240 trading days each year.  You multiply your stdev() result by (240 / # of trading days per return) ^ 0.5, so if you're doing this for daily returns, multiply the stdev() result by 240^0.5; if you were doing it weekly, you'd want to multiply by (240 / ~5)^0.5; etc.  This is your number for sigma.  This answers the intent of your question. For black-scholes, you do not convert anything back with exp(); BS is already set up for geometric analysis, so you need to stay there. The reason why analysis is done geometrically is because the distribution of stock returns is assumed to be lognormal (even though it's really more like logLaplace). "Unfortunately, there is very little data supporting fundamental analysis or technical analysis as appropriate tools to ""time"" the market. I will be so bold to say that technical analysis is meaningless. On the other hand, fundamental analysis has some merits. For example, the realization that CDOs were filled with toxic mortgages can be considered a product of fundamental analysis and hence provided traders with a directional assumption to buy CDSs. However, there is no way to tell when there is a good or bad time to buy or sell. The market behaves like a random 50/50 motion. There are many reasons for this and interestingly, there are many fundamentally sound companies that take large dips for no reason at all. Depending on your goal, you can either believe that this volatility will smooth over long periods and that the market has generally positive drift. On the other hand, I feel that the appropriate approach is to remain active. You will be able to mitigate the large downswings by simply staying small and diversifying  - not in the sense of traditional finance but rather looking for uncorrelated products. Remember, volatility brings higher levels of correlation. My second suggestion is to look towards products like options to provide a method of shaping your P/L - giving up upside by selling calls against a long equity position is a great example. Ground your trades with fundamental beliefs if need be, but use your tools and knowledge to combat risks that may create long periods of drawdown."