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| bio | website | researchgate.net/profile/… |
|---|---|---|
| location | Vienna | |
| age | 31 | |
| visits | member for | 11 months |
| seen | May 14 at 7:53 | |
| stats | profile views | 128 |
Risk Manager at Raiffeisen Capital Management
External Lecturer at Vienna University of Technology
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Apr 4 |
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Stochastic modelling of derivatives on dividends thank you for your detailed answer about options on dividend-paying assets. But my question is about derivatives (e.g. futures) on dividends. This is related but a totally different story ... it is about instruments like this: globalderivatives.nyx.com/stock-indices/nyse-liffe/… |
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Apr 3 |
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Obtaining a consistent covariance matrix for stochastic volatility processes If you are interested you can look at this question for requirements for a positive-definite covariance matrix. |
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Apr 3 |
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Data Synchronization The approach in the paper that you reference is indeed very interesting and close to our paper. However, they use a $VAR(1)$ mode, in which auto-correlations do not vanish but just decay. We chose a $VMA(1)$ model as there the autocorrelations vanish after one time step. If autocorrelation comes from non-contemporaneous trading then it should vanish after one day. In practice a $VMA(1)$ and a $VAR(1)$ model will give similar results in most applications. |
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Apr 3 |
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Obtaining a consistent covariance matrix for stochastic volatility processes A consistent resp. valid covariance matrix has to be positive-definite (in fact: non singular). Should I elaborate on that? |
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Apr 3 |
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Stochastic modelling of derivatives on dividends I have a newer version and quickly browsing it again I don't see anything on derivatives on dividends. I only see remarks on things like Black-Scholes for dividend paying assets - there might be connections (due to arbitrage, just guessing) but nothing directly related. |
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Apr 2 |
answered | Data Synchronization |
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Apr 2 |
asked | Risk factors for derivatives on dividends |
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Apr 2 |
revised |
Stochastic modelling of derivatives on dividends Question was simplified in order to attract answers. |
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Apr 2 |
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Stochastic modelling of derivatives on dividends @JoshuaUlrich You are right - I will split the first 2 points and the second up. Thanks for this suggestion. |
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Mar 28 |
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Stochastic modelling of derivatives on dividends edited body |
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Mar 27 |
asked | Stochastic modelling of derivatives on dividends |
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Mar 22 |
answered | How to justify a model that could not predict external factors? |
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Mar 21 |
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Central Limit Theorem and Lévy processes I like the question very much. The reason must but that it has jumps (because of time jumps due to the Gamma subordinator). But I can not tell you a rigorous reasoning. If there are jumps then it is not Gaussian. I will try to find something. |
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Mar 21 |
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Correlation decay in lognormal distribution Those to get a feeling for this thing: we have stationary returns (with some correlations) but prices are not stationary at all. In any case it is interesting to see that correlation decreases that fast. |
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Mar 20 |
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Correlation decay in lognormal distribution Doesn't the variance of $\sum_{i=t}^T r_t$ grow with $T$ and so does the variance of $P_T = \exp(\sigma \sum_{i=t}^T r_t + x)$. So the variance of the process $P_T$ is not stationary. |
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Mar 20 |
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Correlation Sensitivity nkhuyu prices the option in the framework of correlated GBM and constant parameters. This is idealized and there such phenomenons don't exist. Reality is probably different but first we should understand the ideal world. In the idealized world only vol and correlations exist. Of course when trading one should not forget about reality. |
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Mar 20 |
answered | Central Limit Theorem and Lévy processes |
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Mar 18 |
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Calculate the expectation of a shift CDF Great @Andreas, thanks for sharing this proof! |
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Mar 15 |
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Calculate the expectation of a shift CDF added a mines in the formula in line 2 $F(-s)$ |
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Mar 15 |
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Correlation decay in lognormal distribution Isn't it true that the mean and the variance of the price process is not stationary. Therefore although returns are correlated the prices don't share this property in general. The mean (of prices) wanders around and the variance grows. |
