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Many long term investors use historical events and the market moves associated with such events to stress test their portfolios. For example, they use the dot-com bust, the latest "great recession", LTCM, Asian Crises, Black Monday, etc and any other dramatic events in history and see how their portfolios would have performed under those conditions. Of ...


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Your process of calculating the impact of market stress scenarios sounds more manual / less automated than best industry practices. The disadvantages of having manual processes incclude: it's expensive, so you're reluctant to add new scenarios to your portfolio of scenarios. (You can't just click a button and add "March 2020", like some lucky ...


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U.S. centric answer. Banks/financial institutions are given standard stress scenarios by regulators for CCAR and DFAST (Dodd-Frank Act Stress Testing). It's a good bet that many institutions in various jurisdictions will also be given stress scenarios for climate risk in the next few years. These are the same scenarios for many institutions. Also many ...


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"1) Whether the shocks to Vol points are in % or bps. For example, for Australia bump to 1M vol is 16.2, so is this 16.2% of original Vol or it is a bump of 16.2 basis points to the original vol?" The shocks are in percentage points: for example if Australian 1M vol is 21.0%, the CCAR shocked 1M vol will be 21.0% + 16.2% = 37.2%. "2) This is to just ...


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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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One of the easiest ways is described in Duffie, Pan (1997) "Bootstrapped Simulation from Historical Data" p.55. $R$ is the set of all risk factors (a time series) $C_{norm}$ is the Covariance Matrix during normal times. $C_{stressed}$ is the Covariance Matrix from a period of stress. You can update $R$ in the following way. $R_{i,stressed}=C_{stressed}^{1/...


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Your link refers to a paper that compares the Standard Formula (prescribed approach to SII calculations) and Internal Models (where companies apply to use their own approach for deriving capital requirements). It is an old paper (2009). My suggestion would be to start by taking a look at the latest Technical Specs (30th April 2014) and navigate any ...


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To say a curve is arbitrage-free, you need to pick an arbitrage path; a series of trades which, when followed, yield a net profit without creating exposure. We neglect counterparty exposure here, since you are presumably using market-neutral rates. One arbitrage is to buy a swap from your curve, and sell at the market price. This is a test of your curve ...


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It may help you to notice that, for a bump in implied volatility $\delta \sigma$, the impact on the price of the derivative $V$ is given by: $$ \delta V = \underbrace{\frac{\partial V}{\partial \sigma}}_{\text{Vega}} \delta \sigma + \frac{1}{2} \underbrace{\frac{\partial^2 V}{\partial \sigma^2}}_{\text{Volga, Vomma}} (\delta \sigma)^2 + o((\delta \sigma)^2) $...


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I would not be surprised that you can perform some regulatory arbitrage by mean of little financial engineering as you suggest, see for example: https://www.risk.net/our-take/7046041/in-stress-test-window-dressing-timing-is-everything. In principle, why wouldn't you be allowed to take such a market position? It is a default of the regulatory approach. In ...


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My question now is what further adjustments do these stressed curves need? One subject that comes to mind is the "no arbitrage-ness" of the curve. Do I have to make sure that the curve does not present arbitrage opportunity? If so, how? No there is no such thing as arbitrage arising from a risk free zero curve. Any zero rate you get is by definition the ...


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Two applications of machine learing (related) techniques are in valuation using neural nets of complex products, e.g. "Deep xVA solver -- A neural network based counterparty credit risk management framework" or using Adjoint Algorithmic Differentiation, e.g. "Fast Greeks by algorithmic differentiation". Both of these techniques aim to ...


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I will attempt to elaborate on this from risk management perspective. scenario analysis approach: An example of this is stress testing that Fed mandates for investment banks. Fed gives stress variables to various fundamental macro variables. For example, a certain market stress scenario will be rates down 100bps, volatility up 30%, curve flatter by 30bps, ...


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As the correlation matrix will most probably become non-positive-semi-definite with such an ad hoc manipulation, you may try one of the following: Still run that algorithm and check that the resulting matrix is still positive (semi) definite. Bootstrapp the correlation matrix, or the volatilities, or both, from your input data. Manipulate the eigenvalues ...


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Have a look at Basel document. The section 98.56 and on describe derivation of the interest rate shocks. 16 years may be too long depending on your portfolio, but I think you can shorten the period and start from there. Caveat: I did not try it myself yet, but will revisit this topic soon and might be able to share my findings. I asked a question related to ...


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First assume you have been given/you know the shocks scenarios. Ideally you would have these scenarios in term of shifts/movements- e.g., curve shifts by $a+bT$. So what I would do is to price the products using the current market interest rate data. Then apply the shifts to the curve and then re-price the products. The change in price is the main object of ...


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Seems like a complicated issue perhaps not well suited for general discussion (too specific to the situation). What I would do is (0) Understand in detail what the limits are currently (may be trivial or may involve a lot of questioning and writing things up on your part) (1) Check which product desks are frequently at or near their upper limits (2) ...


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I would check performance return from each sectors and test if it will improve with different risk limit while total risk still tolerable.


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The number of scenario's I could come up with is infinite, these 3 seem interesting and give a backstory which can make discussions with trustees easier: If you're invested in Europe, I would definitely consider a hard Brexit or Eurozone breakup scenario where European assets are harder hit than other assets. Depending on who you ask these scenario's are ...


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All the big factor risk model providers do macro scenario analysis as part of their suite of products. From the point of view of portfolio management, these sorts of products are ideal for putting your own portfolios through what-if scenarios. For example, MSCI Barra does a macroeconomic factor model described here. Part of their sales pitch includes a "...


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Well, I am not an expert in this field but I set up quite some simulation studies in Matlab and I never had to use Simulink before.


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Conventional wisdom would have it that the system would be arbitrage free if and only if: All the implied spot and forward rates on each curve are non-negative (I.e implied discount factors are monotonic non-increasing wrt maturity) All the implied spot and forward rates on the 3M curve are greater than or equal to the corresponding rates on the OIS curve (...


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There exist a lot of way to choose risk factors and the choice differs according to the kind of underlying assets. In your case, particularly, since the portfolio is composed by currencies, I would choose the risk factors mainly among all the macroeconomic variables available in your dataset or data provider. After that, to choose on which of them basing ...


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