# Tag Info

31

I can only talk about quantitative trading. As a rule of thumb, the lower frequency you work in, the more econometrics is important, whereas for a higher frequency, the more econometrics becomes useless. (I would still recommend a top econometrician for HFT since they have what it takes to succeed, it's just the models aren't out-of-the-box applicable.) But ...

11

Upon close reading, this appears to be 3 (interesting) questions, not one. I'm not sure if the mods have the tools needed to split it up, so I'm just going to write down the three questions as I see them and then deal with them one by one. Note, it is simpler for me to talk about variance instead of volatility. This has no material impact on the answer. ...

7

Step 1: Get your data from SQL into R -> http://www.r-bloggers.com/?s=SQL Step 2: Run your analysis/optimizations like -> http://www.r-bloggers.com/portfolio-optimization-in-r-part-1/ or http://blog.streeteye.com/blog/2012/01/portfolio-optimization-and-efficient-frontiers-in-r/ or via RMetrics: http://www.statistik.wiso.uni-erlangen.de/lehre/bachelor/...

7

@user2763361 has a very thorough list of useful econometric topics for quantitative finance. I would add missing, mixed frequency, and irregular data as major issues that I'm either constantly dealing with or begrudgingly ignoring. Seasonal adjustment is important too for some data (like electricity futures), though the subject is also related to his ...

6

Risk-free rate is that you get for letting someone else use your money in a riskless manner. Suppose we live in a world where there is no risk whatsoever. In particular, if you lend someone \$100 there is 100% certainty that he will pay you back in a year. Before the pay date, he can do whatever he wants with your$100, while you have no access to it. Even ...

5

There are a lot of code in Eric Zivots recent class in computational finance. http://spark-public.s3.amazonaws.com/compfinance/R%20code/portfolio.r http://spark-public.s3.amazonaws.com/compfinance/R%20code/testport.r http://spark-public.s3.amazonaws.com/compfinance/R%20code/rollingPortfolios.r Also, you can google some slides in his class where he ...

5

Autocorrelation of returns can be used as a proxy measure for liquidity of the asset. The degree of serial correlation in an asset’s returns can be viewed as a proxy for the magnitude of the frictions, and illiquidity is one of the most common forms of such frictions. A strongly liquid asset should reveal no serial autocorrelation. You can perhaps build ...

5

I think what you are missing is simply the Vega-Gamma relation in the Black-Scholes model. Namely: $$Vega = \frac{\partial v}{\partial \sigma} = \sigma(T-t)S^2 \frac{\partial^2 v}{\partial S^2} = \sigma \tau S^2 \Gamma$$ Plugging this into your coverage error, you get its expression in terms of the Vega which is the most natural measurement of your ...

5

Firstly, the use of the logit models to estimate the PDs is particularly appreciated in some credit industries, as, for instance, the credit retail one. The logit model predicts pretty well the PD on loans, consumer credit, credit cards, ... and all concerns the retail consumer world. Mainly, those listed are the principal sub-industries in the credit ...

4

Most of the credit risk models are some derivative of survival models. Cox Proportional Hazard is one of the early and more popular models, Kaplan-Meier and Logrank tests are others you may have heard of. There are a few ways to go from here. The simplest is to model the sample as binomial with one population as current and the other as in default. A ...

4

No specific history. I'm not aware who introduced this measure initially. Most probably it came up as an example in the research papers on coherent risk measure. All names make sense to some extent: Expected shortfall - as it's an expectation of losses Conditional Value at Risk - as it can be written as $E[X |X >VaR_α(X)]$, i.e. conditional expectation ...

4

First, I am quite sure that this is a typo and it should be $$0 < VaR_1 < VaR_0$$ then $$-VaR_0 < -VaR_1$$ and the plot is correct. Second, the put strategy does not change only the expected profit but the whole distribution of the P&L. If you buy a put with strike $K_1 = -VaR_1$ then you get compensated for losses below $K_1$. But you ...

4

EDITED You are right. We have to look town to the "leaves" in each iteration. I would do it the following way: If $L_i^{(j)}$ is the set of indices in the $j$ branch ($j \in \{1,2\}$), then we define $s_i^{(j)}=\sum_{n \in L_i^{(j)}w_n}$, the weight of the branch before scaling and $n_i^{(j)}=\left|L_i^{(j)}\right|$ the number of leaves in the branch. ...

4

Let the $n-$dimensional vector of returns $\mathbf{r}$ have a multivariate t distribution with $\nu$ degrees of freedom. The marginal distribution of any component $r_i$ has a univariate t distribution also with $\nu$ degrees of freedom. To see this, assuming mean returns have been subtracted, the multivariate t distribution decomposes as the distribution ...

4

The book "Managing Energy Risk An Integrated View on Power and Other Energy Markets" by Burger et al. (2014) may be very helpful as it not only introduces the relevant notions, but does so directly from an energy perspective.

3

I have also seen (in rough decreasing importance order): Mean excess loss, Tail conditional expectation and the variant C.T.E., Tail mean, Mean shortfall... AVaR doesn't seem as common as the other three you mentioned. Acerbi and Tasche 2002 discuss the difference between CVaR and ES. In practice there's little mention on reasons for each choice and ...

3

First of all, usually these models are heavily adapted to a specific country (even for Europe), real estate class (housing, commercial) and market (secondary, primary). In general I would say it's very hard to directly apply standard quantitative tools (like MC) from finance for real estate market. The models I've seen were not heavily quantitative. The ...

3

Couple points for your consideration: At the time of order execution: You are most likely a liquidity taker and thus are rendered a service by those that provide liquidity and you compete for taking liquidity with other takers in the market. As such you need to have a firm grasp at the market impact of your order. Liquidity can be extremely dynamic even ...

3

If Y is the excess returns of your asset and X is that of the market, then CAPM tells you $Y = \beta X + \epsilon$ Taking the variance of both sides yields $$\\ \sigma^2_{Y} = \beta^2 \sigma^2_{X} + \sigma^2_{\epsilon} \\$$ We know that $$\beta = \frac{\sigma_{X,Y}}{\sigma^2_{X}} = \rho_{X,Y}\frac{\sigma_{Y}}{\sigma_{X}}$$ Where $\sigma_{X,Y}$ is the ...

3

do a regression where stock returns is dependent and market return is independent variable. Value of R^2 is Systematic risk and value of 1-R^2 is unsystematic risk...

3

Reuters uses a proprietary model defined StarMine structural/SmartRatios Credit Risk model that has been developed by themselves and provided with the Reuters data service. It does not exist a formal definition or paper about the model, in which it is explained how to get that score; Reuters simply explains roughly what is in its website without going into ...

3

As an overview, Expected Returns, by Antti Ilmanen, was recommended to me. He has a preference for data over theory, so it will appeal to quants. The book is longish, and got a bit heavy at times, but he covers all the investment products and all styles of investing. The biggest problem might be that it is now 3 years old, and was heavily influenced by ...

3

The risk free rate is important and the reason for the inclusion and consideration of the risk free rate is that investors do not get compensated for not taking on risk. Now, we can argue whether the risk free rate truly provides risk free returns (we all should know that it does not, but ...) but it is important in the context of pricing risky assets that ...

3

@Noob2’s comment above is “spot” on. Across the natural resource and energy value chains there are significant price risks that: A. Market prices will fall below price takers’ unit costs; and, B. Market prices will exceed price setters’ unit prices. In either case, if you assume that log price changes are a martingale, and that expected profit is the ...

3

I would give GitHub a try. Searching for option pricing or machine learning or anything like that yield a ton of repositories with implementations that you can look through and learn from. Here are 2 search links: GitHub Search for C++ machine learning GitHub Search for C++ option pricing After gaining some level of comfort, I would look to improve the ...

2

I have studied unsystematic risk [USR] for more than two decades. In fact, I wrote a book (which is here) whose central focus is how to deal with USR in the valuation of non-public companies. It is a multifaceted, complex, and difficult issue. Modern Portfolio Theory did professionals in my line of work no favors when it assumed away the existence of USR ...

2

It is not as simple as changing a value. You need to replace the current factor loadings by feasible values. Furthermore, factor loadings have dependencies between them, that means that when you change one of them, the other factors are affected by this change. In the CCruncher Technical Document there is a proposal to do so. It propose to estimate the ...

2

This is a not a theoritcal/academic answer relating the two by an equation. But from a practicioners stand point. The relationship between vol and gamma depends on the strategy your putting on. For example. In a Short Straddle/Strangle/Butterfly/Iron Condor. Your short theta and the risks your taking are gamma risk, even though your delta neutral, and ...

2

as vanguard2k points out the prolem is dealt with e.g. in Scaling portfolio volatility and calculating risk contributions in the presence of serial cross-correlations and references therein. It turns out that correlations are lowered while lag one cross-correlations increase. E.g. you can probably see a correlation of Japan today to US yesterday due to the ...

2

This can for example be seen in modern portfolio theory (Harry Markowitz, William Sharpe) As an example consider a two asset portfolio with a full investment constraint ($w_1+w_2=1$) so we can write the proportion in asset 1 as $w_1=w$ and in asset 2 as $1-w$ The expected portfolio return $E[R_p]=wE[R_1]+(1-w)E[R_2]$ And variance \$\sigma_p^2 = w^2\sigma_1^...

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