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You are working with a time series $x(t)$ which has been re-denominated at times $t_1$ and $t_2$. You want to rescale the time series for all times $t < t_2$. First, do you know what the rescaling factors ($k$) should be (e.g. did 1000 units turn into one unit)? If not, I would set $k_2:= t_2^+ / t_2^-$, where $t_2^-$ is the last currency rate before the ...


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How is this different than a reverse stock split? If you just want the same scale for all the data, you'd just have to update the historic data using the reverse split ratio.


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why not run the same time series 3 times, once for each data set?


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It's also necessary to look into technical indicators and filters. Technically analysis are often widely employed by finance practitioners and can apply to any sort of timeframe whether that's intra-day or EODs. Since ANNs are able to fit complex non-linear inputs, it would not hurt to add many of those indicators into the mix


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I would suggest you to add spreads to the implied hazard rates, spreads that you regress on the macroeconomic factors. Then you stress by calculating the spreads corresponding to the stressed factors.


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Depends how you calculating correlation, but probably you have rolling window from what you get high and low for calculation, when you adding samples to window then some samples must exit the window, when sample that exiting are not equal to high and low then it's don't matter, but when high or low is exiting then suddenly everything changes in your ...


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Two cointegrated series contain a single unit root. Each series can be formulated as the sum of a common unit root plus a stationary component. Most textbooks covering cointegration will cover such formulations - see Hamilton's (1994) discussion of Phillips' "triangular representation" of a cointegrated vector, for example. Simulating is likely to be easy ...


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Non-Stationary process can be analyzed and there are various models available that can be used . For example, Autoregressive Integrated Moving Average model (ARIMA) models are used to explain homogeneous non-stationary models as well as random walk with drift can be used for explaining several such series. have a look at this link : ...


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I would suggest Time Series Analysis by James Douglas Hamilton


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I would say that you can use Johansens methods to test for rank of co-integration matrix. There are tests for that. If there is no co-integration vector present and both series are I(0) then there is no co-integration. Series still might have some short-run dynamics. If series are I(1) and no con-integration vector is present then modeling these series by ...



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