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35

I would offer the distinctions are i) pure statistical approach, ii) equilibrium based approach, and iii) empirical approach. The statistical approach includes data mining. Its techniques originate in statistics and machine learning. In its extreme there is no a priori theoretical structure imposed on asset returns. Factor structure might be identified thru ...


16

I think you have the correct dichotomy here. Things started in the late 1980s and through the 1990s with analytical approaches particularly to derivative pricing (as in "hey, let's create yet another exotic option we can sell to the buy side"). The risk modelling "fashion" of the 1990s (when regulated entities such as banks needed to beef up reporting) ...


11

Equity returns have persistent negative skewness and excess kurtosis[1] over longer periods. So yes you're right: a majority of the daily returns is positive and small and a minority of the returns is negative and larger. This can be quite extreme, for example Black Monday. I don't have the data right now but you can get returns on major indices freely. [1]...


8

Since the stock is listed on NASDAQ, you have access to fairly standard 10Q and 10K financial statements. So you can apply the analysis pioneered by Ed Altman in his Z-score paper - compare this company's fundamental ratios with those of other companies, and see how many of them went bankrupt historically. For example, Moody's KMV uses this approach to ...


7

I would recommend Marc Wildi's work on signal extraction.


7

Richardh is spot on. The price of the VIX option is a weighted sum of put (strikes < forward) and call (strikes > forward) options on the S&P 500. The weights are proportional to 1/strike^2. As the S&P goes down the out of the money puts become more valuable and those have the highest weights. I will leave arguments about the market as a whole to ...


7

By definition, the average investor holds the market portfolio. Risk aversion can be measured as the slope (i.e. ratio of expected returns to volatility) on the efficient frontier. Therefore, the risk aversion of the average investor assuming the S&P500 is the proxy for the market portfolio is the expected returns of the S&P 500 divided by the ...


7

Is your question more about approaches taken on the buy side vs. sell side? If so, you may want to read Attilio Meucci's paper, P vs. Q, on this topic. He breaks down the dichotomy as derivatives pricing (the "Q" world), which uses a lot of very sophisticated modeling involving Ito calculus and PDEs, and portfolio management (the "P" world), which makes ...


6

the Commodity Traders report is the most useful for this, it lets you deduce large and small players on the stock index futures. This is only released weekly by the CFTC Otherwise you can use volume:price divergence and average volume moving average to further deduce whats happening. Finally you can use level 2's to get a feel for the speed of orders and ...


6

SMM stands for single-month mortality and CPR stands for constant (or conditional) prepayment rate. They're both units of voluntary prepayment rates ($CPR = 1-(1-SMM)^{12}$). They could be based on either estimated or actual prepayments. Where to get actual MBS prepayment data will depend on what type(s) of MBS pools you're modeling (e.g. agency, ...


6

The concept of a tradable asset is closely related to the principle of (no-)arbitrage. Much of quant finance is about the connection between the price of a derivative and the price of its underlying. The fundamental reason that there is a connection at all, is the possibility to set up self-financing trading strategies in the underlying(s) which replicate ...


6

There are more ways to approach this but the method I propose should work reasonably well in practice, especially if you increase the number of assets you hold. Calculate the beta of the stocks you're holding with respect to an index Buy $N_f$ (sell when $N_f$ is negative) future contracts on that index $N_f$ can be calculated as $$N_f = \frac{\beta_T - \...


6

The top chart is called a 'candle stick chart' or 'OHLC candlestick' or 'OHLC bar chart' http://multicharts.com/trading-charts When the price goes down during a time interval (from O to C) the box is filled in orange, when the price goes up it is green bordered with black inside. The exact colors are a matter of taste, as long as they are clearly different ...


6

I have been told: Bankruptcy is very controversial Google Scholar Researchers. You might track companies ratios (e.g., debt to equity ratio, EPS, net income, cash per share (cash/sh), etc.). For instance, GE looks almost bankrupt. But, it is not and there is a very low probability that GE would file for any bankruptcy chapter, I'm just guessing. There ...


5

Your question will be very difficult to answer, at least for equities. The best you can probably do in terms of accurate information are research reports from organizations like Tabb. You can look at positioning of players from 13F reports, meaning you can see which players have large positions in a certain equity. You may not be able to discern why, ...


5

In the academic literature it is extremely widely applied in the last 20 years. I would estimate maybe 200 empirical papers, or more. For example a common finding is that higher frequency (daily) wavelet correlations have been high since 2007, attributable either to increasing financial interation or the financial crisis. It is also popular to estimate the ...


5

It's really quite simple. It's just a matter of the fact that we can change measure on the stochastic volatility while not changing the fact that the stock is a martingale. Once we can do this, we have payoffs that have different values under different measures, so the market can't be complete. For clarity, just consider a stock S, a money market account ...


5

The original Vasicek paper is "An equilibrium model of the term structure". If you google for it, you'll find it and you can read in his own words his motivation for developing it. In particular, what now is called the Vasicek model basically comes from applying his results to an Ornstein-Uhlenbeck model for the spot process, which he claims was proposed by ...


5

Stock market indices fall faster than they rise, in part, due to leveraged long investors. As individual stocks fall, investors must de-risk due to margin calls, and those margin calls may need to be met by selling other stocks. This causes correlations to increase as markets fall. This also causes indices to fall more quickly than they rise, since the ...


5

You can try using different approaches. Starting from something not that "heavy" like the NN. 0) Pre study - you need to prepare your data (how you will treat a negative spread (i.e. ASK - BID <0), what will you do if you will have 0 spread and then you will divide some value by it?), - plan your research ahead - how will you divide your limited data ...


5

What happened was the BoJ announcement. Such large scale news are well covered in mainstream media (ft, bloomberg, etc) and also mainstream anti-media (eg zerohedge).


5

There was a methodology change in how volume is reported. Now it is using the consolidated tape including trades on all exchanges. See: http://stockcharts.com/articles/dont_ignore_this_chart/2016/12/whats-the-deal-with-that-intraday-volume-on-the-dow-indu.html


4

I think there is a result that some generalizations of the Vickrey auction to two sided trading do not have balanced budgets: i.e. require additional incentives from the market maker. It occurs as a consequence of avoiding any participant's price being dependent on their own input. The "Vickrey" approach would be to make someone's price equal to the ...


4

One idea - borrowing from Google's 2nd Price auction model, which uses Vickery, for prioritizing rank of ads on their search page would be to determine a strictly monotonic increasing function $f(*)$, which applied to $u1 = (o1 - r1)$ and $u2 = (o2-r2)$ results in $o1*f(u1) \geq o2*f(u2)$ iff $o1 \geq o2$. The winner in this case would pay: $o2*f(u2) / f(u1)...


4

An Axioma research paper from August 2011, Using Multiple Risk Models for Superior Portfolio Management… A Practice Not Just For Quants, answers exactly your question, I believe. Note the graphs at the top of page 8. They compare their medium-horizon fundamental and statistical factor models from January 2008 to January 2009. At the start of the period, ...


4

I don't know if these are the most commonly traded or most popular (for your definition of popular) but here are a few exotic products that I recall being supported by the flagship product at my former employers. Exotic Options: Asian - Strike price is dependant on average price throughout the deal, not just at expiry Bermudan - So called because it's ...


4

To avoid confusion, this only applies to most equity/index option. In a return distribution, there's a measurement called skewness which measures the asymmetry of upside and downside. Let's define that as 30d Put Premium/ 30d Call Premium. It's already priced in because most of the time, skew > 1. However, if you trade commodities or companies that might ...


4

Yes. Check out Time-Series Analysis by Shumway and Stoffer. Spectral Analysis and Filtering is covered in Chapter 4.


4

There are two excellent choices for implementing prediction markets: (1) Use book orders that stand until filled, just as intrade.com does. (2) Use an automated market maker (like Robin Hanson's) that stands ready to make trades. The book orders model is very simple to implement, but can suffer from very wide Bid/Ask spreads. And, it can be tough to bet ...


4

This is known as a 'crossed' book, the exchange will attempt to uncross the book at the price at which the maximum amount of volume can trade. In your example at the price of 42 there's only 3533 amount of buying quantity, and there are more than enough sellers to cover this. At a price of 40, there's now 3533+425 buying quantity willing to trade, and still ...


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