user25064
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This is a very serious problem. In general these results should not be used as they usually suffer from very low robustness and display butterfly effect. In other words, the parameters can change very ...

Some approaches Use only common points - Exclude all holidays in any index. Reduced sample size Loss of information No 'made up' data (consistency) Fill forward - use previous day as you ...

It can, in my opinion be stated that your understanding of the situation is exactly backward. The CAPM model should be stated as $$\tag{CAPM model} r_s = r_f + \beta_s (r_m - r_f) + \epsilon$$ ...

Let's look at the formula for an ARMA(p, q) model $$X_t = c + \sigma z_t + \sum_{i=1}^p \varphi_i X_{t-i} + \sigma\sum_{i=1}^{q}\theta_i z_{t-i}$$ where $z_t \in \mathcal{N}(0,1)$ for all $t$. ...

As @Nicholas said in a comment KX/KDB+ is popular in finance for this purpose. Direct message passing and local aggregation on the machine may be the best method in this case IMO.

I would consider Amihud (2002) as a good first approximation with that level of data.

The problem is that what some mean when they say "volatility" is BS implied vol from an option price. What some others mean when they say "volatility" is some diffusion parameter from a drift ...

I think that the approach suggested by @Alex is pretty standard and I have seen such charts before but for a less orthodox approach that may clean up the graphics a bit, you might consider the final ...

This is in general simply referred to as compounding. In functional terms, consider the inefficient python code below def total_discount(rates): return (1.0 - discount(1.0, rates))*100.0 def ...

Write out the simple equations \begin{align} Y_j &= a_0 Z_j + a_1 Z_{j-1} + a_2 Z_{j-2}\\ Y_{j-1} &= a_0 Z_{j-1} + a_1 Z_{j-2} + a_2 Z_{j-3} \end{align} There are some very simple cases ...

R package TTR has rolling window algorithms and understands day counting etc. It stands on the shoulders of xts (which extends zoo) and quantmod

while it is true that $$\lim_{T\to\infty} Z(t, T) = \lim_{T\to\infty} e^{-r(T-t)} = 0$$ this is when $r$ is independent of time to maturity, a flat and constant yield curve. In practice, we use yield ...

It seems to me that JavaScript charting is becoming relatively poplar see google trends. The main example is d3js and things that run on top of it like c3js and nvd3. I recently wrote a simple python ...

Kenetic Component Analysis If I am to summarize the work of the authors from a broader view than that which is taken in the abstract, essentially the price process is decomposed into position, ...

I wanted to add this answer for anyone still searching for a convenient way to find this data, go to http://www.quandl.com/ search in this format <ticker> outstanding shares Edit: The ...

Martingales + Markovian Here is the motivation. Conditional expectations are martingales by the tower property of conditional expectations (an easy exercise to show). Suppose $r=0$, by the risk ...

Do $N$ MC simulations of $M$ samples, calculating your estimate of VaR for each one $\{\widehat{VaR}_i\}_{i=1}^N$ and you now have an IID sample! Take the sample (or unbiased) standard deviation for ...

You can start to understand Brigo and Mercurio from the standard Shreve material but it does not look at things from the perspective of semimartingales which will possibly be confusing at some point. ...

See for reference Merton 1971 Optimum consumption and portfolio rules in a continuous-time model is an excellent application of the topic. As @phi mentioned Arbitrage theory in Continuous Time by ...

Two parts Real world vs risk neutral: Can we even estimate risk neutral volatility using historical data? There is a difference in distribution of the underlying stock price under the real world and ...

I would suggest writing the joint density as the product of the conditional densities then estimate parameters using an optimization package. The joint density is given by f(r_0, \ldots, r_T) = ...