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Yes, you can say they are traded on listed options, but only for a few limited markets, and not that liquid relative to options on a single asset. For instance, the commodity futures space, there are options on commodity spreads listed, and a strike of 0 would be the same as an exchange option. These options have some liquidity in energy and grain markets,...


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(This is my opinion; someone is likely to disagee). I like to think of the carry as the predictable part (e.g. the coupon that accrues daily) and the rolldown as the stochastic part (the curves moved - maybe the forwards realized, maybe not. A good estimate of what it might turn out to be as to reprice for the next day assuming all forwards are realized. I ...


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I'm assuming you're talking about a European option. I did a similar problem for my homework recently, I used the in-out parity for pricing the up and in barrier option. Basically European Option = Knock up and in Option + Knock up and out option You can price the up and out easily using Binomial and use BS formula for pricing the European Option, then ...


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That is the concept of Cointegration Regressing two non-stationary variables results in spurious regression. However, if these two variables are cointegrated, spurious regression no longer arises. As stated on p. 120, We call these resulting betas a cointegrating vector. Cointegration is a statistical property of time series, mainly proposed by Engle/...


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Under Black-Scholes assumption for the 2 assets $S_1$ and $S_2$ with volatilities $\sigma_{1,2}$ and correlation $\rho$ the value of this option has an explicit expression which is the Margrabe formula To quote the result explicitly Introducing $\sigma = \sqrt{\sigma_1^2 + \sigma_2^2 - 2 \sigma_1\sigma_2\rho}$, Margrabe's formula states that the fair price ...


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Another application in commodity trading is power transmission capacity. A power transmission line will be used to transport electricity from zone A to zone B if the price $p_A$ in zone A is smaller than the price $p_B$ in zone B, else it will remain idle. So the "payoff" of owning power transmission capacity $A\rightarrow B$ is $(p_B-p_A)^+$. This is a zero ...


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Broadly you're asking about directional versus relative value strategies. There are lots of directional approaches, but I've yet to see many discussed publicly in non-generic ways (I mean, if they work, why would anyone talk about them?). As others have noted, trend following is a notable example. I'd consider a lot of equity factor approaches as a ...


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This is pretty standard fare for a Stats 101 course, so as to rationale, etc. you might benefit from picking up a textbook or otherwise do some reading on this. In brief though, hypothesis testing allows us to assess the likelihood sample estimates are different than theorized values in the absence of actual population values. In the cases above, with a ...


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If you exercise the option (assuming that is an american option) you would receive the intrinsic value, which is for a Call option $\max(S-K, 0) $, and for a Put option $\max(K-S, 0)$. Hence, 11300.00 - 11100.00 = 200. If you are talking about selling the option instead of exercising it, I recommend to have a look at the Black & Scholes model, John C ...


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A portfolio is simply the collection of all assets you own. So in all your three cases, you still have a portfolio. In a Sense, a trading strategy is a synonym for portfolio in maths finance since you only need to know how much you invest at a certain time in a certain asset. For instance, your first scenario may be described by $(0,100,0,0....)$, i.e. you ...


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Lots of different ways to do it that typically involve the following: (1) Identify starting universe. (2) Source and process underlying attribute data for each holding (for instance, for a low vol factor, possibly ST and LT vol for each security). (3) At this point, there is tons of variability. Once you have your factor data, some simply use it for ...


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First you need to start with an investible universe of securities that can be used to retrieve data on. From there, you'll need to compile a list of each individual factor that you'd like to screen for (Momentum, Value, Growth, Div Payers, High Multiple, Asset Quality etc...) and create a metric of measurement for each factor. Enter into some sort of ...


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Take a look at Factor Investing, Systematic trend following/ CTA and Systematic Global Macro. See also this article on Bloomberg: https://www.bloomberg.com/news/articles/2018-10-02/your-guide-to-the-many-flavors-of-quant-investing-quicktake


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As Alex C says in the comments, Longstaff and Schwarz did consider multiple factors and mention it as one of the advantages (page 114 in the journal): By its nature, simulation is a promising alternative to traditional finite difference and binomial techniques and has many advantages as a framework for valuing, risk managing, and optimally exercising ...


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R makes a fine environment for quantitative research. Same case with Python. For further information on R versus Python for quant finance, see Is R being replaced by Python at quant desks? As far as entire production-level algorithmic trading systems go, no. For execution R is generally not used but rather the alpha model in R is integrated into an ...


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As an example of an instrument priced using Margrabe, Mexico government sold exchange warrants a few times in the past. The program's goal was to reduce hard-currency debt and replace it by local currency debt. An investor would pay some premium for the warrrants and have the right later to tender some face amount of hard-currency (and external-law) ...


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You can fetch Indian stocks data from NSEpy.The data available on NESpy are: 1. Daily stock data 2. Stock futures data 3. Stock options data 4. Index futures data 5. Index options data Example to get daily stock data from NSEpy in Python: from nsepy import get_history from datetime import datetime start = datetime(2019, 1, 1) end = datetime(2019, 30, 7) ...


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