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6

Few points from my experience: 1 Another filters that you that you should consider is for price = 999 or 999.99 that appears in some data providers. 2 Another set of checks is to look at cross-section of e.g. range = (high-low)/close over all names. Check for the smallest range and largest range to see if the values make sense. You can also check daily % ...


3

It's all about the notation - so i try to be very precise now. The Fama-MacBeth approach is a cross-sectional regression at each period of time: $$R_{t}^{ei}= \beta_{i}^{'}\lambda_t+a_{it}$$ where $R_{t}^{ei}$ is the excess-return of asset $i$ at time $t$ and $\beta_{i}^{'}$ denotes the estimated beta-factor of the stock. As stated in Cochrane (Asset ...


3

If your payoff is linear, then it is a little tough to see what's going on, so let's consider the quadratic case. Here's a generic quadratic to sample, centered at zero Antithetic sampling introduces samples with a mean perfectly equal to zero, which effectively introduces perfect bilateral symmetry to the whole problem In contrast, delta hedging will ...


2

The paper is reliable and the formula is correct. However as you mention yourself there is an error. $$ \frac{\log \left(\frac{e^{0.01}-1}{0.01}\right)}{0.01} = 0.500417 \neq 0.498 $$


2

You can also read through the answer to this related question: How are Brownian Bridges used in derivatives pricing in practice? Please also note that the timings mentioned are terribly slow. I know speed is not Python's strong point, but still. 3m50s for 20000 simulations with 2000 time steps (dt=1/2000) gives one the wrong idea of how efficient MC can be ...


2

The path dependency of barrier options requires a sufficient number of steps to accurately model price evolution. For example, the stock price simulation, for dt=1/10, for dt=1/500, for dt=1/1000, It can be seen that, if you use fewer number of steps, a barrier might not be triggered which would otherwise have been triggered if more number of steps were ...


2

The adjusted close will change after dividends and stock splits. So the old data will have to be replaced by the new. So it is usually a good idea to check for adj close of the downloaded values against current values. I also like to check for downloaded data against some other source (like Google). I do this by writing a unit test that will randomly pick a ...


1

That is because the innovation $inn$ is a standard normally distributed random variable and therefore it can actually take positive or negative values. Without the innovation it would be a deterministic model (i.e. no risk). So, de facto it is +/- but mathematically you have to write $+\sqrt{GARCH} \cdot inn$ because $inn$ itself contributes the sign and can ...


1

A standard approach here is to build a hedge implied by your model and evaluate its hedging performance when it comes to daily rebalancing of your CDS portfolio... I assume daily data is the highest frequency you've got. You are doing finance, right? So in addition to regular statistical goodness-of-fit measures, you should always try PNL-based goodness-of-...


1

I finally found my error,thanks dm63 for the explanation. I had a hard time imagining the negative position value and that it implies that I also get the interest from getting cash for the short..I used a slightly different approach but got the same result. FV when stock goes up = FV when stock goes down 1 -1Δ+100Δ(r)=1Δ+100Δ(r) 1 -1Δ=1Δ Δ=.5 PV= FV/(...


1

Could you show the exact call that you use when you "plug them in manually"? Anyway, can you override the bracketing interval in RQuantLib with a tighter range, say 1% to 100%? library("NMOF") vanillaOptionImpliedVol("american", price = 3.7, S = 37.39, X = 35, tau = .1698, q = 0.0654, r = 0.17, ...


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