Features to simplify.

How would financial products evolve if they were acquired in the past? LexiFi Apropos’ Backtest feature provides an accurate answer in seconds. Quantify the performance of a financial product as if it had been acquired at one or several date(s) in the past instead of at the real issue date, for any imaginable product. Backtest allows users to perform multiple simulations in one go by specifying a series of acquisition dates in the past, and then view the results of each simulation. Each simulation is done by running the contract from a date in the past along the underlying(s) realised time series of data. This is achieved by translating its fixings by a computed delta time value to obtain corresponding fixings in the past, and normalising the contract using these fixings instead of the real ones. The spot levels of the underlyings in the past can be scaled or shifted so that barriers, expressed in terms of the real initial fixings of the contract, can be used in the simulation even though the simulated initial fixings in the past are different. Unlock value in one click to obtain results for each simulated acquisition date, including the annual yield, the effective lifespan of the product and associated cashflows. Visualize the results using either a tabular or a graphical format. Figure 1: Dates, frequency and reference level shifts can be customised in backtests Figure 2: Metrics such as annual yield and price distribution are displayed graphically Video 1: Running the backtest simulation for a given contract, computing the results and analysing the charts

Calibration associated to a given pricing operation involves essentially finding the parameters that best fit available market data given the pricing model to be used. In the LexiFi Apropos solution, users access a long list of market and proprietary pricing models (the list of supported pricing models can be found here), as well as consistent, high-quality financial market data. Each pricing model has an associated fully documented calibration methodology. LexiFi’s quants provide upon request documentation detailing all model inputs, market data, model choice, calibration assumptions, calendars and more. Models are automatically pre-calibrated, but always allow for user custom calibration when needed. Users have access to model calibration pages where they can edit both model and calibration parameters as shown in Figure 1 or run calibrations in one click and inspect results in depth. An automatic cache allows saving and sharing calibration results across multiple pricing jobs as illustrated in Figure 2 with the saved results tab. Calibration results are displayed both in tables and charts, for easy but in-depth analysis. Dedicated calibration inspectors are available for all models and allow, for instance, to: Inspect and verify resulting parameters and volatility surfaces Compare calibrated model numerical prices with market prices Explore through tracing and charting of surface data slices Figure 1: An example of the calibration page for the Heston model, where parameters are automatically set, but can also be customized by the user. Figure 2: Saved Heston model calibration results: shows saved calibration results under the name “Benchmark” using Heston as the model to calibrate EUROSTOXX 50 Index market data. The local Volatility model calibration example We have selected the Local Volatility model, as it is widely used by our clients, to show the customization and result discovery possibilities that calibration pages offer to users. We use the EUROSTOXX 50 Index. Figure 3 below shows that users can customize data and parameters as follows: Edit the market data transformation methodology Apply pricing adjustments Explicitly override the market data used for the pricing Change Local Volatility calibration parameters: maturity and strike ranges the regular grid definition (maturity step, strike step …) smoothing parameters selecting Multiple curves mode including curves from FX clean volatilities tolerance and bandwidth scale Change Monte Carlo parameters Figure 3: Calibration user interface for the Local Volatility model Once the model is calibrated, results are available in both tabular form and as interactive graphical display (same for every models): The table in Figure 4 below allows inspecting the difference between quoted and calibrated data in basis points and percentages; Monte Carlo results are also displayed and compared to quoted market data. The regularized implied volatility column, which are market volatilities after interpolation, is displayed at last. In Figure 5 the Volatility surface can be inspected graphically in 3D, where users may select a given point in the graph to slice results in two charts: the implied volatility as a function of the strike or as a function of the maturity Regularized implied volatility Local Volatility As shown in Figure 6, for comparison and evaluation purposes, users can inspect a graph of a slice of the implied volatility at a user-defined maturity to trace market implied volatility next to the model implied volatility and the Monte Carlo’s implied volatility results Figure 4 :table of Calibration results of the Local Volatility model in a tabular layout. Quoted market data vs. calibration results with the calculated error in basis points and percentage. Monte Carlo pricing results with associated calculated error in basis points and percentages followed by the Regularized implied volatility results calibration. Figure 5: Volatility surface 3D chart Figure 6: Implied volatility slice Chart

Building the (zero-coupon) yield curve serves as a foundational step in many financial modeling endeavors. Once the curve is built, users can use the Curve Inspector feature to visualize and inspect the curve. How to best build a yield curve from observed market data is a never ending debate among specialists. It is typically a mixture of theoretical considerations, data analysis and cleanup, pragmatic arguments (about stability or smoothness of the curve for instance), implicit assumptions about market structures (that imply different choices of interpolation and extrapolation) and, last but not least, targeted use of the built yield curve. Thanks to the curve inspector tool, users can inspect yield curves based on market data. The primary function of the curve inspector is to provide a graphical representation of the yield curve, allowing users to visualize the relationship between interest rates and their corresponding maturities. Curve inspector is thus a great tool to analyze and visualize yield curves, including curve spreads, shifts, and shapes. Users may be able to plot multiple curves, identify trends or anomalies. The curve inspector offers functionalities such as: Data Analysis: It allows users to analyze the data points that make up the curve, including the yields or interest rates at various points along the maturity spectrum. Scenario Analysis: users are able to input different assumptions into the curve inspector to see how they affect the shape and characteristics of the yield curve. 💡For more details about the Yield Curve building in LexiFi, you can refer to the Monthly Focus Yield Curve building in LexiFi Apropos on the website.

Rest on our quantitative expertise and unlock significant value using our unequaled Monte Carlo Simulation Inspector. Its raison d’être is to provide detailed information on Monte Carlo pricings: It computes risk-neutral probabilities of event occurring (barrier crossing, early redemption, etc…) It provides details on the price, the annual yield, the duration of the product, the number of cash flows, cash flow dates, forwards and zero-coupons for each cash flow It can graphically display distributions of price, annual yield, cash flows, duration and export them to Excel for further investigation Figure 1: Price distribution, forwards and zero-coupon analysis in the Monte Carlo Simulation Inspector The analysis also comprises additional information on underlying forward values and values of zero-coupons between the valuation date and each payment date. Figure 2: Cumulative distribution of the sum of undiscounted cash flows is one of the many available analytics Results of Monte Carlo simulations are thus readily available and essential information such as barrier-crossing probability or early redemption probability can then be accessed from other pages of the software. They can be called from the Template tool and can be displayed in reporting documents. Again, LexiFi Apropos also embeds an export function that transfers the pricing details of a Monte Carlo simulation into an Excel file. It provides full transparency on the results and invites users to have a more thorough look at specific analytics. Figure 3: Pricing details of Monte Carlo simulations can be exported to Excel files

Want to explain or better understand the price LexiFi Apropos just returned? LexiFi gives you an in-depth insight into PDE pricing. Combining access to the contract and to the pricing code, the PDE Inspector provides on demand all relevant information, in an easy to understand form, allowing for a drill down discovery of the pricing process up to the last bit. For products that can be priced using PDE models, users can obtain details of the PDE pricing process and inspect the price matrix on each relevant date instead of the sole price. Parameters can be selected for a multi-dimensional PDE model pricing, namely the size of spatial discretisation and time steps. The ‘Pricing script’ section of the page details the steps of PDE resolution derived from the contract. It is a fundamental tool to observe the introduction of path-dependent variables. LexiFi Apropos provides information and graphs on one-dimension PDE models such as the Garman-Kohlhagen model. Several schemes are also available for two-dimension PDE and a two-dimension representation of price matrix is automatically derived for each value of the underlying and for each variance value (for instance in a Heston model). LexiFi uses efficient numerical methods to solve PDE such as change of variables, interpolation and path-dependant inversion for you to benefit from our know-how. Figures 1, 2, 3 and 4 below illustrate the example of a PDE pricing of an Autocall, maturity 4 years, coupon 4%, barrier 60%, Strike and autocall level 100%. Figure 1: Price of the Autocall in function of its underlyings Greeks for free Moreover, while having to run several Monte Carlo simulations to calculate options sensitivities, one gets the “greeks” for free using PDE methods. The plot area of the PDE Inspector page LexiFi Apropos allows to draw and inspect the Price as a function of initial factor values. One may also chose to draw specified derivatives of the Price function (Greeks) instead of the Price itself. Figure 2: Price sensitivity to the Eurostoxx underlying: first partial derivative of the price in function of the EUROSTOXX Figure 3: Price sensitivity to the S&P500 underlying: first partial derivative of the price in function of the S&P500 Figure 4: Partial derivative of the price in function of the Euro Stoxx 50 and the S&P 500. This figure illustrates the sensitivity of the Autocall’s price to a joint variation of underlyings

Precision and Speed are the two pillars of the calculation of Greeks in LexiFi Apropos. LexiFi Apropos allows to compute on the fly the main Greeks such as Delta, Gamma, Vega, Volga, Vanna, Theta and Rho. These can be computed using different pricing models and methods. A default method is automatically chosen, or the user can choose an explicit one among: Market data finite difference: the derivatives are computed using finite difference, shocking the market data. Model finite difference: the derivatives are computed using finite difference, where the shocks are directly applied to the model in order to gain precision and speed. Malliavin: all Greeks are computed at a linear cost. This method, despite being mathematically complex, returns the desired results quickly and it is the best except when fixings are too close to each other or when the first fixing is too close to the valuation date. Calculate cross-Greeks as well, such as cross Gamma, cross Volga, and cross Vanna, in multi-underlying products. Video 1: Greeks and cross-Greeks for a contract Functionalities are easily accessible from different pages. Greeks for each contract can be computed on the Greek page or on the specific Contract Information page, as well as on other pages such as Reporting. Position Greeks on several contracts can be computed on the Position Greeks page. Video 2: Greeks and cross-Greeks for a position

We know that uncertainty is one of our clients’ major issues, leading us to incorporate an innovative forward-looking feature into our software. Run a simulation of future values at certain dates and study the evolution of the contract according to a set of forward-looking scenarios. Specify a set of scenarios and a set of increasing dates for the valuation that you want. The scenarios will describe how your market data, used for managing and/or pricing the contract, will evolve in the future. Market data that do not appear in the scenario stay constant and are shifted along the simulation timeline. For each scenario and each valuation date, the contract is managed to obtain the residual contract, which is then priced in the market situation (obtained by applying the scenario). Once the simulation has been computed and results have been generated, an elaborated and unequaled post-processing task can be performed on the data. Take advantage of the seven components of this post-processing functionality of Forward Simulation: Summary Cash flows Underlying on horizons and contracts Unused perturbations Residual value by horizon date Annual yield distribution Life in years distribution Figure 1: Details of future cash flows including residual prices, annual yield and remaining life of the contract Figure 2: Chart of the residual value by horizon Figure 3: Chart of the annual yield distribution Figure 4: Chart of the life in years distribution

Assess the consequences of virtually any market situation on your portfolio. As an unparalleled risk simulation software module, the LexiFi Risk Scenario feature allows the assessment of the impact of virtually any quantifiable market shock on the price of an individual structured product or portfolio. The Risk Scenario simulation starts by filling in LexiFi Apropos’s extremely functional trading-quality entry screens and this entire simulation can be automated using LexiFi’s standard tools. The intuitiveness and the power of this feature originate in the combination of LexiFi’s comprehensive list of models eligible for each simulation and a limited amount of uncomplicated operators unlocking an infinite diversity of scenarios. The process of filling in the entry screen can also be simplified by selecting common pre-defined and user-defined scenarios from “Reference”. This contributes to one of LexiFi Apropos’s general objectives: considerably reduce input capture time. How Does it Work? Concretely, users start by defining scenarios, which can be done by browsing the drop-down menu of “Scenario parameters”, then fill in the specifications of the scenario , and, one click on the “Calculate” button later, LexiFi Apropos executes them as if they all occurred instantaneously. For each scenario defined, the software returns the price of the product or of a portfolio of products, and a price difference with the initial price of the product or portfolio. Video 1: General presentation of Risk Scenario Risk Scenario offers immeasurable flexibility for the creation of scenarios. All scenarios are founded upon “bricks”. These bricks help to create and regroup different shocks (intuitively, small scenarios) within one scenario. Tools to Build Scenarios 1- “Explicit”: the first brick can be considered as the most elementary but also as the most specific type of scenario. It applies a shock to explicitly specified market data items, usually from the spot (“initial”) market data environment (a set of market data items). The magnitude of the shock and the specific methodology are set by the user, with specific syntax. “Transformation”: specifies a shock to perform on a given subset of market data items. Users define the shock to apply using simple syntax, identical to the one required for “Explicit”. “Transformation” could be for instance used as part of an “Explicit” scenario. “Explicit” and “Transformation” make the production of entirely new scenarios possible, and these scenarios can be stored in “Reference” for later shared use. Entirely personalized baseline scenarios can thus be obtained. Additional scenarios may be written using these baseline scenarios, useful both for the transformation of market data items and of valuation dates. 2- Partitioners constitute the second brick used to define most scenarios. They correspond to “For all/each”, a feature easily found in the “Scenario parameters” menu. The role of this feature is to separate the market data items of the initial market data environment into different subsets and apply transformations onto the items of the different subsets in different ways. Exactly the same scenarios (as in the general case) can be defined with “For all/each”, except that the scenarios are applied either simultaneously or individually to the appropriate sub-environments. “For all” applies a scenario simultaneously to all market data items of a specified category. “For each” applies a scenario successively to each instance of a specific kind of market data items. 3- “From…to…in…steps transformations” is another essential scenario parameter that could be considered complementary to the partitioner “For all/each”. This third brick is also available in the “Scenario parameters” menu. “From…to…in…steps transformations” defines a list of shocks to apply successively to a given subset of market data items. LexiFi Apropos will shock the quotes of all the items of a given environment from the lower bound to the upper bound of the predefined range in the number of steps chosen. Scenarios can then be combined in different ways. The user is free to choose the scale of her approach, from a micro focus, based on a small number of precise situations to a very broad macro view, characterized by a large amount of shocks. The more macro approach can be illustrated by combining many scenarios. 4- There are precisely three tools to combine scenarios, representing the last main brick, called combinators: “Union”, “Compose” and “Sequence”. The simplest combinator is “Union”: it orders LexiFi Apropos to execute each scenario from the spot following the order of the list of defined scenarios. Video 2: Introducing the “Union” combinator The “Compose” combinator, technically, produces the “cartesian product” of the listed scenarios. This combinator and the “cartesian product” are best explained by the following video: Video 3: The “Compose” combinator The third combinator, “Sequence”, can be seen as a mix of “Union” and “Compose”. “Sequence” is the “Union” of: the first scenario of the list, the “Compose” of the first and the second scenario, and the “Compose” of the first, second and third scenarios, etc. Differently from “Union” and “Compose”, the order of the listed scenarios matters with “Sequence”. Interest Rate-Linked Products Risk Scenario offers the possibility to simulate complex situations of interest to clients using a small amount of very simple operators. The first key situation considered is the yield curve transformation. “Nonparallel yield curve shift” is a dedicated scenario parameter. Rates are shocked according to their maturities. The user can set a maturity in terms of days, months, years, or alternatively set a specific date in the calendar. This scenario parameter enables the creation of customized yield curve transformations, one maturity at a time. A possible transformation would thus be a yield curve inversion, a phenomenon that has preceded every U.S. recession for the past 60 years, according to research from the Federal Reserve Bank of San Francisco (1). The simplicity and the flexbility when simulating such an event is striking: the user just needs to increase the short-term rates of his/her preference by more than longer-term rates (or decrease the long-term rates). The flexibility resides in the fact that both the maturities and the magnitudes of the shocks are selected and set by the user. Video 4: Yield curve transformations with Risk Scenario Another parameter related to the shock of rate maturities is “Rate buckets”: the aim of this parameter is to shock rates from defined maturity intervals, according to one (or several) scenario(s) defined within “Rate buckets”. The second key situation would be a stress test scenario, possibly occuring at times of economic and/or financial crises. The aim is simply to shock quotes according to different magnitudes, depending on the simulated situation. Risk Scenario’s strength here is that it comprises all the bricks to: Shock quotes according to specific magnitudes by using “Explicit” for example Define different magnitudes depending on different market data items. Again, “Explicit” is an adequate brick to do so Partition the market data environment to choose which items to shock and which to leave aside, with, naturally, the partitioner “For all/each” Create a sequence of absolute or relative shocks with “From…to…in…steps transformations” Combine stress tests in three different ways with combinators. LexiFi Apropos merges such stress tests with other scenarios, such as interest rate increases, in a both simple and coherent way. Stress Tests Results LexiFi knows that the quality of the results of data analysis is paramount. Additionally to prices and price differences, other valuable results are unlocked by ticking the “Keep individual pricing results” box, present by default on the Risk Scenario page. For each scenario defined, a pop-up window is opened by double-clicking on the corresponding cell in the “Pricing result” column of the pricing results table. The valuation date, the price, the model kind and information on the “Bid/Ask” is displayed. The user also has access to an audit trail, information regarding different dates relevant to the pricing, properties, market data items, market data transformations and model parameters. What is special here is that this bottomless volume of data and results is stored for every single shock, regardless of its magnitude and the number of market data items it covers. The feature also displays a more concise summary of the shock of market data items, triggered by a double click on any cell of the first column of the pricing results table. Again, a pop-up window appears, containing an exhaustive comparison of the transformations of the main types of market data items affected by the scenarios created. There are two tables: the first compares the “New” (shocked) quotes of the items to the initial (spot) ones, corresponding to the product or the portfolio of interest before the running of the simulation. The second table compares shocked values to the quotes modified by the previous scenario in the list of scenarios defined (this assumes the existence of at least two scenarios). LexiFi’s philosophy centered around perpetual reduction of pricing time is embodied by its “Run parallel scenario pricing” box: when scenarios lead to the same computations, LexiFi Apropos executes these only once, to reduce pricing time, crucial to the industry’s professionals. The incredible connectivity of the functionality is epitomized by its “Excel scenario file” parameter, enabling the import of scenarios from Excel files. Alongside “Explicit”, the other way to define scenarios in a generic way is by using a specific format on an Excel file. Once that the pricing has taken place, it is possible to further choose the results displayed, other than with “Keep individual pricing results”. For any combination of scenarios, with one click on the “Draw price chart” button, or with “Draw diff chart”, the user obtains charts and tables summarizing in a clear and readable way the pricing results. For both prices and price differences, it is also possible to draw charts only for selected rows of the pricing results table. LexiFi Apropos can also save results with “Save as pricing result”, for later use. Video 5: Stress tests and the drawing of charts Document Generation All pricing results obtained with Risk Scenario, from audit trails to individual market data items shocked by the running of one among many scenarios, are automatically transferred and made available to LexiFi Apropos’ document generator. Given the importance of clear communication of stress test results, again in just one click, LexiFi Apropos can offer complete customization of specific documents, exportable in HTML or PDF formats and readily exploitable by clients. The connection with the software’s other preeminent feature opens the door to an unlimited amount of document personalization to present scenario results. Conclusion A major takeaway of this webpage is that LexiFi Risk Scenario offers a functionality that allows the definition of complex scenarios and the evaluation of their impact on prices of derivatives and structured products with a reduced number of simple operators, or “bricks”. There is one brick to create virtually any scenario (“Explicit”), only one to partition environments (“For all/each”), relying on another essential tool (“From…to…in…steps transformations”) and just three bricks to combine scenarios in an almost infinite number of ways (“Union”, “Compose” and “Sequence”). There are also just two operators to define yield curve transformations (“Nonparallel yield curve shift” and “Rate buckets”). The software also “naturally” adapts to the types of market data items treated and/or scenarios defined. Repetitions are always avoided, whether these are calculation repetitions, with “Run parallel scenario pricing” to reduce the time required to return pricing results, or more structural repetitions. This is one among many obstacles automatically detected by the software, pursuing LexiFi’s tradition of clarity and simplicity, reflected even in the structure and the functioning of the Modeling Language for Finance (MLFi). The entire Risk Scenario user experience, from data entry to result analysis, is subsequently made as straightforward as possible. More than 20 years of client feedback suggests that no other software can offer such highly important results at this speed, in such a simple and tailored way. The unmatched power of LexiFi Risk Scenario resides in the software’s capacity to execute any list of combined scenarios, whatever its length and size, which could consist in a set of baseline scenarios, coupled with a quasi-infinite level of user customization. Sources (1): https://www.frbsf.org/economic-research/files/el2018-07.pdf

Evaluate all your different possibilities. Run a solver analysis to calculate the value of one or more contract parameters that corresponds to a user-inputted contract price or other pricing extra results, such as barrier crossing probabilities or average lifetime (in years). LexiFi Apropos’ Solver uses the complete quantitative resources of our technology to calculate matrices of product parameters, thus quickly revealing investment opportunities in the latest market conditions. Also perform the solver analysis for different variations on a single parameter. Use the solver mode to solve for a target formula, or the variation mode to apply some variations to the contract and easily price it for each of these variations. Figure 1: Results of the solver analysis Figure 2: Results of the solver analysis can be plotted on 3D graph

Optimise your products, don’t just manage them! The Underlying Optimiser is designed to compare the prices of a product using different combinations of underlyings. It is possible to choose either single underlying or to combine multiple underlyings in a worst-of or equally weighted performance basket format. The user can either add underlyings to the ones existing in the contract definition or completely substitute them with a new selection. Evaluate the impact of various underlying selections on the price, the barrier crossing probability, the early redemption probability and the average lifetime in years, than choose the most appropriate combination that fully satisfies your needs. Figure 1: The Underlying Optimiser prices a structure with different combinations of underlyings

Sometimes, calculating one or more (ie., Risk Analysis) prices isn’t enough, you may need to explain precisely the difference between two prices to clients. Such investigation can be tricky. Whether you need a punctual analysis or regular reports explaining product P&L, LexiFi Apropos Value Change Analysis will answer your needs. The Value change Analysis Tool Contracts evolve over time due to several factors (time value, coupon detachment, fixings, option exercises or alike) but also different pricing models may be used. The Value Change Analysis quantifies and compares the contribution of such factors to the change in the value of a contract between two dates. Absolute and relative factor contributions are shown for comparison and classification. The tool provides separate charts for both the most important positive and negative effects allow for an immediate intuitive understanding of realized gains or losses. Powerful drill-down capacities allow exploring each factor change up to any individual market data modification, for full transparency. The Analysis function may be applied to previously archived pricing results or pricing operations executed on the fly. Value Change Analysis Reporting Results can be exported into an Excel file in a snap, for further analysis or to simply share the file. An interactive report is available, where users select the contract(s) as well as the analysis’ time period. Reports may be downloaded as PDF documents. Figure 1: Value Change Analysis detects factors affecting the price modification of a product Video 1: Performing a value change analysis

The counterparty risk is known to have an important impact on a portfolio. LexiFi Apropos evaluates this risk and its components precisely and rapidly so that one can concentrate on results and immediately benefit from a complete and intuitive view of them. This feature is helpful to effortlessly fulfill regulatory requirements or to sense the portfolio exposure. Compute pricing to compare: Portfolio risk free price: no default risk Internal portfolio risky price: internal default risk Counterparty’s portfolio risky price: counterparty’s default risk Portfolio risky price: both parties’ default risk The Credit Valuation Adjustment (CVA) and the Debt Valuation Adjustment (DVA) are computed with a strategy based on two strong major points: An algebra analysis: solve American features and create sub-contract for each computation date A pricing engine: use a Monte-Carlo method and a numerical integration LexiFi Apropos includes graphical charts to facilitate the understanding of the results. Both the positive and the negative expected exposures are visually represented, as well as the internal default probability and the counterparty default probability. The video below demonstrates the tool showing its different elements and results. Video: The interface shows that users can keep the default pricing profile or choose a pricing model to price the XVA of a specific contract. The interface has different elements, among which we find: Market data source to specify the market data used to compute the prices and the default/survival probability of the counterparty and that of the company itself. Credit Information to define both entities (the company itself and the counterparty). Is collateralized with a box to check if the contract is collateralized. If the contract is collateralized, we have to specify a Minimum Transfer Amount. The Result section gives the decomposition of the price with the unilateral CVA and DVA and the bilateral CVA. In the Chart section, we show the Positive and Negative expected exposure. We also plot the internal and counterparty default/survival probability. Figure 1: Chart of positive expected exposure at a per-product level

LexiFi Apropos reduces what was once a matter of hours in a few seconds. The Value at Risk (VaR) and conditional VaR (CVaR) are two important risk measures for quantifying and managing both product and portfolio risk. In LexiFi’s Software, we put your at disposal two methodologies to calculate these: the Historical Scenarios and the Monte Carlo scenario generation method. Figure 1: Value at Risk scenario parameters Contracts are priced using a number of different scenarios where each scenario modifies the current market data for the contract underlying spots and volatilities by applying shocks inferred from observed market changes. Volatility shocks are applied to all volatility data (constant volatilities, term-structured curves and smiled surfaces). Value at Risk, Tail-VaR, P&L distribution and details on scenarios are available for inspection. The generated distribution of P&L vs shocks can be viewed both graphically and in charts. Use the Fast-VaR mode to drastically speedup calculations on a portfolio of contracts by iteratively selecting computation scenarios that will be needed. Video 1: Computing VaR for a contract Figure 2: Value at Risk analysis and P&L graph Figure 3: Value at Risk analysis and P&L vs shock factors chart Video 2: Computing VaR on positions Figure 4: Position Value at Risk analysis