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Apr
1
comment Mean-variance portfolio & quadratic programming
The minimize variance or maximize utility approach can easily be cast in terms of a quadratic programming problem, which have been well-studied. As you note, you can't apply quadratic programming to maximizing return given variance because it involves a non-linear constraint. There are a lot of techniques that can do it. Second-order cone programming comes to mind. Most commercial optimizers I've used can do it. There are some open source non-linear optimizers that can as well.
Mar
31
answered Mean-variance portfolio & quadratic programming
Mar
25
comment Backtesting with fundamentals
Python is a full programming language so it has a lot of potential, but you'll probably have to roll up your sleeves and program some of the stuff you want. There is dedicated backtesting software out there, but you'd have to pay for it.
Mar
24
comment Backtesting with fundamentals
What analysis do you need to do that you can't do with numpy and pandas? Why don't you try going through the documentation and playing around with it a bit more?
Mar
23
reviewed Reject What are some research articles on using principle components to generate alpha?
Mar
23
revised How is the MA (moving average model) useful?
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Mar
21
comment Log returns vs Relativizing to Portfolio size of $1
It makes it difficult to recommend something without understanding the context. For instance, if I am performing mean-variance optimization including transaction costs I might think about them differently than if I were trying to evaluate how much transaction costs have impacted my portfolio in the past. In one case, I might think about transaction costs as a function of a change in weights or holdings, while in the other I might think in terms of dollars as a % of AUM. I'm not sure why you would analyze them in terms of log returns.
Mar
21
answered How is the MA (moving average model) useful?
Mar
17
comment Log returns vs Relativizing to Portfolio size of $1
It might be useful if you add more details about what you're trying to do and what the various calculations are.
Mar
14
comment Use of geometric mean for average return of several indices
@ChrisDegnen What you're trying to do doesn't make much sense to me. You don't need a geometric average to get an average of returns cross-sectionally. Arithmetic average is fine. Weighted averages (based on market-cap or something) are also common.
Mar
13
comment Critique against consumption-based asset pricing theory?
Also, this theory isn't all that popular among practitioners. You'd get more information talking to some finance professors or something.
Mar
13
comment Critique against consumption-based asset pricing theory?
@Investor To your first point, advocates of this approach would likely say that it is an abstraction that wouldn't substantially impact the analysis. Others (like yourself) may disagree with that assessment, but oh well. To your second point, this is not agent based modelling. Representative agents are be more-or-less alike. Same utility functions, same preferences, same expectations. Maybe different ages in overlappying generation models. For the most part, all the agents are exposed to the same consumption goods in the same way.
Mar
12
revised Critique against consumption-based asset pricing theory?
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Mar
12
answered Critique against consumption-based asset pricing theory?
Mar
12
comment Optimal lag length selection criterion in GARCH(p,q) model using MATLAB
Matlab's GARCH outputs the log-likelihood, which is the primary input to AIC/BIC. Just write a function that loops over the relevant parameters, calculate AIC/BIC, then selects the one with the best.
Mar
11
comment Econometrics - Granger Causality
davegiles.blogspot.com/2011/04/…
Mar
11
answered Econometrics - Testing
Mar
11
comment Optimal lag length selection criterion in GARCH(p,q) model using MATLAB
mathworks.com/help/econ/conduct-a-likelihood-ratio-test.html
Mar
5
comment Copula Value At Risk
I have no idea what those charts are supposed to be saying.
Mar
4
comment Copula Value At Risk
What do you mean you don't want a single value? You mean you want a distribution for the portfolio Value at Risk? Or, do you mean you want the quantile for each security? Also, I think Copula Value at Risk might be a misleading term. Sort of implies that the Value at Risk calculation is different, when it's really just the modelling that's different. Before setting what you want up with Copulas, I might first set it up with multivariate normal (as that is equivalent to what you're doing).