# Tag Info

10

A better, clearer, answer is to compute Lambda (leverage) of the option (link) and see if it is bigger or smaller than 1. Lambda is $\Delta \frac{S}{V}$ so we test $$\Delta \frac{S}{V} \lessgtr 1$$ which is what Joshi is saying in words: compare $\frac{1}{S}$ (delta to price for the stock, the delta of the stock is 1) to $\frac{\Delta}{V}$ (delta to price ...

9

A market is said to be complete if any contingent claim can be replicated by an admissible (i.e. with value process bounded from below) self-financing (i.e. all gains and losses exactly offset each other) trading strategy, a so-called replicating strategy. This strategy being constructed from primary securities - the market prices of which are unique - it ...

9

Indeed, algorithmic trading is a very hidden subject. All I can help you with are some industry-specific terms which might speed up your search for relevant papers and information: Risk of ruin tables (Peak-to-valley) drawdown (maximum drawdown, duration of drawdown etc.) Number of consecutive losses Confidence intervals Empirical distributions (for risk ...

9

If you measure risk by the standard deviation of the portfolio return $$\sigma = \sqrt{w^T \Sigma w},$$ then it is usual to define risk contributions for each asset by $$\sigma_i = w_i (\Sigma w)_i/\sigma,$$ then diversified could mean that these $\sigma_i$ are evenly spread over the assets in the portfolio. You find this approach and more in this paper ...

8

The Strata project is the new pure Java market risk quant library from OpenGamma. For more information, see the documentation and GitHub. It is Apache v2 licensed. Strata takes the experience of the OG-Platform codebase referenced in the question and turns it into a library - no need for databases, servers or similar. Ease of use is a big focus and there ...

7

I am very happy with the following equivalent formulation for the risk budgeting problem (as presented in Bruder, Roncalli, 2012, Managing Risk Exposures using the Risk Budgeting Apporach): Let $b_i$, $\Sigma_{i=1}^n b_i =1$ be the risk budgets, $y_i$ the unscaled portfolio weights and $S$ the variance covariance matrix and $c$ arbitrary. $$y^* = \text{... 7 Step 1: Get your data from SQL into R -> http://www.r-bloggers.com/?s=SQL Step 2: Run your analysis/optimizations like -> http://www.r-bloggers.com/portfolio-optimization-in-r-part-1/ or http://blog.streeteye.com/blog/2012/01/portfolio-optimization-and-efficient-frontiers-in-r/ or via RMetrics: http://www.statistik.wiso.uni-erlangen.de/lehre/bachelor/... 7 if you take the variance of a single asset it scales as a quadratic,$$ var(\lambda X) = \lambda^2 var(X) $$so it's not surprising that the general case gives a quadratic form. 7 This problem can be expressed as the original Merton's portfolio problem. Consider wealth process defined by SDE$$ d X _ { t } = \frac { X _ { t } \alpha _ { t } } { S _ { t } } d S _ { t } + \frac { X _ { t } \left( 1 - \alpha _ { t } \right) } { S _ { t } ^ { 0 } } d S _ { t } ^ { 0 } $$where \alpha_t is proportion of the investment in the risky ... 6 I would entirely separate the investigation and analysis of volatility and correlation between two asset classes. Think of it this way: If volatility is extremely high then high fluctuation to both, the up and down side will contribute to the stability of high volatility. However, for correlations it makes a big difference whether the large move has been ... 6 I am a risk taker and I can say with confidence that you will never convince those individuals, you cited in your question, that they incur too much risk, because there will always be certain traders who prefer lottery tickets over longevity with the same firm (running high risk books unfortunately in the current environment runs equal to a free option; blow ... 6 No reply has been given so I wanted to at least give a visualisation of the expectiles. Suppose the curvy dashed line in my picture represents a cumulative distribution function of some random variable X. Then blue part corresponds exactly to \mathbb{E}[(X-x)_+], while the orange surface corresponds to \mathbb{E}[(X-x)_-]. In the picture x=1. Now if ... 6 It kind of depends what your objective is. First, momentum 'bias' isn't well-defined. Are you looking to eliminate momentum exposure for some reason? Momentum itself isn't even well-defined really: momentum over the trailing 1 year? Trailing 6m? Looking over 3-5y periods where mean-reversion is more at play? Generally, in the absence of a clearer ... 6 Suppose the covariance matrix is V (which is n by n) and the weights are w (of length n). Then the Portfolio Variance is V_p = w^T V w and the Risk Contribution (in terms of variance) of asset k is RC_k=w_k \sum_j V[k,j]w_j in words this is "the weight of asset k times the inner product of the k-th row of V and the weight vector". (Sometimes ... 6 Just a small addendum to @noob2's answer. The discrete shape of \lambda is:$$\lambda \approx \frac{V_1 - V_0}{S_1 - S_0} \times \frac{S_0}{V_0} $$which can be rewritten as$$ \lambda \approx \frac{\frac{V_1 - V_0}{V_0}}{\frac{S_1 - S_0}{S_0}} $$which is as @noob2 said just the ratio of the relative returns for option and stock. 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 would create categories, and work on risk parity among the categories. Otherwise, variance is not really a good measure of downside risk: Change your risk measure, use a rolling window historical VaR or Expected Shortfall at some horizon that matches your investment style. downside semi-variance could do the trick too if don't want to change your algo ... 5 On the N. N. Taleb's website, you can find all his papers collected in the bibliography he updates on his own site. Hope this will help. 5 Try to give David Spiegelhalter a read/listen to David Spiegelhalter's work and research. He is a statistician and a Professor of the Public Understanding of Risk at Cambridge England. Rather than new ways of calculating risk, he looks at ways of communicating risk to a general public that doesn't have any knowledge of stats. I Linked an interesting video-... 5 I cannot suggest some reference particularly, since the field is going to develop day by day, but, generally, you could take a look to: Engelmann, Bernd, and Robert Rauhmeier, eds. The Basel II risk parameters: estimation, validation, and stress testing. Springer Science & Business Media, 2006. Particularly, look at the chapter 4 and 5; the ... 5 Yes, it is correct. Underestimation: you under-estimate the risk, so you have more VaR violations than what your model predicts. Ex: With 100 observations, and a 99% VaR, you expect 1 violation but you observe 5 violations. Overestimation: you over-estimate the risk, i.e the risk is less important that you expect. You observe less VaR violations that you ... 5 Simple example where sub-additivity fails Let there be four possible outcomes i=1,2,3,4 that occur with equal probability \frac{1}{4}. Payoffs for X, Y, and X + Y are given by:$$ X = \begin{bmatrix}-1\\0\\1\\2 \end{bmatrix} \quad Y = \begin{bmatrix}0\\-1\\1\\2 \end{bmatrix} \quad X + Y = \begin{bmatrix}-1\\-1\\2\\4 \end{bmatrix}$$What's the ... 5 On 1, I suspect that is a typo and that the second formula should sum to r. On 2, that is applying well-known techniques in how to handle piece-wise linear functions in an optimizer. For instance, see page 4 of these lecture notes. It's basically doing the same thing with a few additional complications. In CVaR optimization, there are more things to sum ... 5 Let the n-dimensional vector of returns \mathbf{r} have a multivariate t distribution with \nu degrees of freedom. The marginal distribution of any component r_i has a univariate t distribution also with \nu degrees of freedom. To see this, assuming mean returns have been subtracted, the multivariate t distribution decomposes as the distribution ... 4 Another approach to construct a risk parity portfolio would be to use the formulation proposed by Spinu [1]:$$\begin{array}{ll} \underset{\mathbf{w}}{\textsf{minimize}} & \frac{1}{2}\mathbf{w}^{T}\Sigma\mathbf{w} - \sum_{i=1}^{N}b_i\log(w_i)\\ \textsf{subject to} & \mathbf{1}^T\mathbf{w}=1. \end{array} where $\mathbf{w}$ is the vector of portfolio ...

4

I think the national regulators are more concerned with downturn LGD (sort of TTC LGD) rather than a TTC PD. Therefore most rating systems which I encounter are closer to being PIT and thereby easier to validate using the techniques you mentioned and also to backtest. But in any case, model validation is a very subjective field despite the various ...

4

Well, 2 answers I knew right away :-) d) That depends on what is insured. In classical life insurance (person gets sum insured in case of death only) one risk would be large catastrophes with many insured people involved (like 9/11, for example). A "larger" risk is, as you said, change in interest rate, though. In Germany (I don't know about other countries)...

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