Why is volatility in a positive direction clubbed in the same risk category as volatility in a negative direction?
Mostly, when an equity's price rises, its statistical and implied volatilities fall and vice versa. The reason why is a mathematical phenomenon mixed with the reality that a unceasingly falling asset price will soon not exist, skewing the results with survivorship bias.   Since volatility is standard deviation of price indexes, a security that changes in price by the same amount every day will have lower volatility, so a rising price will have lower implied volatility because its mostly experiencing positive daily price change while a recently falling price will have higher volatility because factored together with the positive price changes, the negative price changes will widen the standard deviation of the securities price index. Quantitatively, any change, in or out of one's favor, is a risk because change is uncertain, and any uncertainty is a risk. This quantitative interpretation while valid runs almost totally counter to the value opinion, that a lower price relative to value is a lower risk than a higher price relative to value, but both have their place in time. Over long time periods, it's best to use the value interpretation, quantitative for shorter.  Using the opposite has hastily destroyed many a fund manager.
Risk and return always go hand by hand.*  Risk is a measure of expected return volatility. The best investment at this stage is a good, easy to understand but thorough book on finance. *Applies to efficient markets only. Equal weighted indexes are not theoretically meant to be less volatile or less risky; they're just a different way to weigh stocks in an index. If you had a problem that hurt small caps more than large caps, an equal weighted index will be hurt more than a market-cap weighted one.  On the other hand, if you consider that second rung companies have come up to replace the top layer, it makes sense to weigh them on par.  History changes on a per-country basis - in India, for instance, the market's so small at the lower-cap end that big money chases only the large caps, which go up more in a liquidity driven move. But in a more secular period (like the last 18 months) we see that smaller caps have outperformed. "In theory, the idea is that diversified assets will perform differently in different circumstances, spreading your risk around. Whether that still functions in practice is a decent question, as the ""truth"" of most probability based arguments for diversification rely on the different assets being at least somewhat uncorrelated. This article suggests that might not be true. Specifically: The correlations we note among industry sectors are profoundly and dysfunctionally high. and  Gold and silver traders have gotten too used to the negative correlation trade with stocks. This is, in fact, an unusual relationship for precious metals tostocks. The correlation should actually be zero." "This is because volatility is cumulative and with less time there is less cumulative volatility. The time value and option value are tied to the value of the underlying.  The value of the underlying (stock) is quite influenced by volatility, the possible price movement in a given span of time.  Thirty days of volatility has a much broader spread of values than two days, since each day benefits from the possible price change of the prior days.  So if a stock could move up to +/- 1% in a day, then compounded after 5 days it could be +5%, +0%, or -5%.  In other words, this is compounded volatility. Less time means far less volatility, which is geometric and not linear.  Less volatility lowers the value of the underlying.  See Black-Scholes for more technical discussion of this concept. A shorter timeframe until option expiration means there are fewer days of compounded volatility.  So the expected change in the underlying will decrease geometrically.  The odds are good that the price at T-5 days will be close to the price at T-0, much more so than the prices at T-30 or T-90. Additionally, the time value of an American option is the implicit put value (or implicit call).  While an ""American"" option lets you exercise prior to expiry (unlike a ""European"" option, exercised only at expiry), there's an implicit put option in a call (or an implicit call in a put option).  If you have an American call option of 60 days and it goes into the money at 30 days, you could exercise early.  By contract, that stock is yours if you pay for it (or, in a put, you can sell whenever you decide). In some cases, this may make sense (if you want an immediate payoff or you expect this is the best price situation), but you may prefer to watch the price.  If the price moves further, your gain when you use the call may be even better.  If the price goes back out of the money, then you benefited from an implicit put.  It's as though you exercised the option when it went in the money, then sold the stock and got back your cash when the stock went out of the money, even though no actual transaction took place and this is all just implicit. So the time value of an American option includes the implicit option to not use it early.  The value of the implicit option also decreases in a nonlinear fashion, since the value of the implicit option is subject to the same valuation principles. But the larger principle for both is the compounded volatility, which drops geometrically." I think I may have gotten my reasoning backwards, since beta can be thought of as just the quantification of the relationship in prices but in itself isn't the actual reason behind them.  Risk free are things such as Treasury bonds/bills. The point of derivatives is to get rid of the risk you don't want so you can acquire exposure only to the risk you want. Who wants weather/temperature risk -- speculators. Who doesn't want that risk? Anyone who's core business is adversely affected by bad weather. It's the same reason multinational firms will hedge FX and interest rates. All a speculator is typically doing is taking the other side of the trade based on what they feel is the true price of the risk they are assuming From a mathematical expected-value standpoint, there is no difference between gambling (e.g. buying a lottery ticket) and investing (e.g. buying a share of stock).  The former probably has negative expected value while the latter probably has positive expected value, but that is not a distinction to include in a definition (else every company that gives a bad quarterly earnings report suddenly changes categories). However, investment professionals have a vested interest in claiming there is a difference; that justifies them charging fees to steer you into the right investment.  Consequently, hair-splitting ideas like the motive behind a purchase are introduced.  The classification of an item to be purchased should not depend on the mental state of its purchaser. Depending on the situation, it may be right to engage in negative EV behavior.  For example, if you have $1000 and need $2000 by next week or else you can't have an operation and you will die (and you can't find anyone to give you a loan).  Your optimal strategy is to gamble your $1000, at the best odds you can get, with a possible outcome of $2000.  So even if you only have a 1/3 chance of winning and getting that operation, it's still the right bet if you can't find a better one. "people are willing to pay higher premiums for options when stocks go down. Obviously the time value and intrinsic value and interests rates of the option doesn't change because of this so the miscalculation remainder is priced into the implied volatility part of the formula.  Basically, anything that suggests the stock price will get volatile (sharp moves in either direction) will increase the implied volatility of the option. For instance, around earnings reports, the IV in both calls and puts in the nearest expiration dates are very high. When stocks go down sharply, the volatility is high because some people are buying puts for protection and others are buying calls because they think there will be a rebound move in the other direction. People (the ""sleep-at-night"" investors, not the derivatives traders ;) )  tend to be calm when stocks are going up, and fearful when they are going down. The psychology is important to understand and observe and profit from, not to quantitatively prove. The first paragraph should be your qualitative answer" These statements aren't necessarily contradictory. In the first case, investors are bearish because they anticipate selling in the future (because all the interested buyers have bought, so all that remains in the short run are people willing to sell and therefore drive down the price).  In the second case, the trend is strengthened because the increase in volume indicates that the price movement interested a lot of traders. The trend could be bullish or bearish.  The statements aren't contradictory because the second case could very well lead to the first case. For example, if an increase in price is coupled with an increase in volume, this could indicate that the positive trend is strengthening (second case). Traders are becoming more interested in the price move, so they buy.  However, once all of the traders who are willing to enter the market long do so, we're in the first case. Investors realize that all of the traders who were interested in buying have bought, so they become bearish because they expect selling to start soon. well well, good question, worth a discussion, the more filters you add to your strategy (volatility in this case), the lower its predictive power. i mean, one could further filter it by day of the week, whether prices are above or below a 200 day moving average...etc. The risk of the particular share moving up or down is same for both.   however in terms of mitigating the risk, Investor A is conservative on upside, ie will exit if he gets 10%, while is ready to take unlimited downside ... his belief is that things will not go worse .. While Investor B is wants to make at least 10% less than peak value and in general is less risk averse as he will sell his position the moment the price hits 10% less than max [peak value] So it more like how do you mitigate a risk, as to which one is wise depends on your belief and the loss appetite A change in implied volatility tells us something about what investors are thinking (or fearing) about the volatility going forward for the life of the associated option contracts (which may be short or long-lived).  IV does a good job of summarizing the information available to investors, which includes information about the past and the present. However, whether these investor views actually translate into what happens in the future is a topic of debate in the finance literature--investors do not generally know the future--there are conflicting results available.  There have been papers that show that implied volatility has predictive power in some situations, time periods, and horizons (though it is also biased) and other papers that show that it does not have statistically significant predictive power at all. The consensus last time I checked was that implied volatility is no worse than historical volatility (including methods that use trends in historical volatility to forecast where it is going) at predicting future volatility.  Whether it is significantly better and whether either reliably predicts the future is something that is not agreed on.  I take this lack of consensus as evidence that if it does predict future volatility, it does so poorly. Somewhat dated FAJ survey on the subject "First off, the jargon you are looking for is a hedge. A hedge is ""an investment position intended to offset potential losses/gains that may be incurred by a companion investment"" (http://en.wikipedia.org/wiki/Hedge_(finance)) The other answers which point out that put options are frequently used as a hedge are correct. However there are other hedging instruments used by financial professionals to mitigate risk.  For example, suppose you would really prefer that Foo Corporation not go bankrupt -- perhaps because they own you money (because you're a bondholder) or perhaps because you own them (because you're a stockholder), or maybe you have some other reason for wanting Foo Corp to do well. To mitigate the risk of loss due to bankruptcy of Foo Corp you can buy a Credit Default Swap (http://en.wikipedia.org/wiki/Credit_default_swap). A CDS is essentially a bet that pays off if Foo Corp goes bankrupt, just as insurance on your house is a bet that pays off if your house burns down.   Finally, don't ever forget that all insurance is not just a bet that the bad thing you're insuring against is going to happen, it is also a bet that the insurer is going to pay you if that happens.  If the insurer goes bankrupt at the same time as the thing you are insuring goes bad, you're potentially in big trouble." Volatility is a shitty metric and is sample dependent, What is more interesting is point recurrence, i.e. how many times has a certain point been touched in x time, you can make good day trading strategies off point recurrence (relative that is). I use two measures to define investment risk: What's the longest period of time over which this investment has had negative returns? What's the worst-case fall in the value of this investment (peak to trough)? I find that the former works best for long-term investments, like retirement. As a concrete example, I have most of my retirement money in equity, since the Sensex has had zero returns over as long as a decade. Since my investment time-frame is longer, equity is risk-free, by this measure. For short-term investments, like money put aside to buy a car next year, the second measure works better. For this purpose, I might choose a debt fund that isn't the safest, and has had a worst-case 8% loss over the past decade. I can afford that loss, putting in more money from my pocket to buy the car, if needed. So, I might choose this fund for this purpose, taking a slight risk to earn higher return. In any case, how much money I need for a car can only be a rough guess, so having 8% less than originally planned may turn out to be enough. Or it may turn out that the entire amount originally planned for is insufficient, in which case a further 8% shortfall may not be a big deal. These two measures I've defined are simple to explain and understand, unlike academic stuff like beta, standard deviation, information ratio or other mumbo-jumbo. And they are simple to apply to a practical problem, as I've illustrated with the two examples above. On the other hand, if someone tells me that the standard deviation of a mutual fund is 15%, I'll have no idea what that means, or how to apply that to my financial situation. All this suffers from the problem of being limited to historical data, and the future may not be like the past. But that affects any risk statistic, and you can't do better unless you have a time machine.