Expertise
Asset Management

R-square Asset management activities include traditional fiduciary services, retail brokerage, investment company services, and custody and security-holder services. The distribution channels for asset management products and services vary according to the size, complexity, financial capacity, and geographic characteristics of each institution.

Our clear focus on managing client assets and delivering strong risk-adjusted returns and we have experts and investment professionals who are providing various cutting-edge strategies spanning the full spectrum of asset classes, including equity, fixed income, credit derivative, cash liquidity, currency, real estate, hedge funds and private equity across geographical boundaries.

Our banking and insurance solutions that engage in asset management activities operate within the growing complexity of asset management products have generated client requirements.

We measure potential loss functions, asset portfolio analysis and risk adjusted performance attribution either through direct expense charges or from loss of clients, arises when a bank fails to fulfill its fiduciary and contractual responsibilities to customers, shareholders, and regulatory authorities.

Our services also involve structuring and analysis of alternative investments products such as ILS, Cat Bonds, CDOs, or CLOs, Tail factors et al.

Risk Management

R-square consulting services facilitate the exchange of information and expertise across disciplines. Our goal is to generate ideas and promote good practice for those involved in the business of managing risk. Say for instance our comprehensive resources for trading, financial engineering and financial risk management expertise become a preeminent for practitioners in the banking, capital market, commodity and energy markets. Our consultants are subject matter experts covering entire spectrum of Operational Risk and Credit Analytics. We have as well most sophisticated exposure to measure all risk parameters through the Loss Data Model.

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We examine both the problems and potential solutions on

  • Market risk, Credit risk, Operational risk
  • Corporate governance
  • Liquidity risk
  • Risk adjusted return on capital
  • Risk modeling
  • Risk pool
Enterprise Risk Management & Economic Scenario Generation

In this current global financial recession and possible recovery phase: Risk management and co-operation between national jurisdictions should be improved through the creation, possibly within each country’s central bank, banking and insurance regulators of a “Country Chief Risk Supervisor” role. The functions of this role would include: 1. developing an agreed risk appetite policy for key market-wide risk indicators; 2. monitor and manage risk indicators within that appetite; 3. publicly reporting macro risk indicators, and 4. facilitating risk identification and communication with appropriate decision-makers, at both the national and international levels.

The current financial market crisis demonstrates that a principles-based, comprehensive and risk-sensitive regulatory framework is essential to the stability of the financial services industries. For example, the general absence of appropriate risk-sensitive capital charges for sub-prime related lending and for Collateralized Debt Obligations (CDOs) was a major contributor to the current crisis.

Enterprise Risk Management & Economic Scenario Generation:
We have developed an algorithm for Dynamic Financial Analysis (DFA) that enables the creation of a comprehensive framework to manage Enterprise Risk. DFA is used in the capital budgeting decision process of a company to launch a new invention and predict the impact of the strategic decision on the balance sheet in the horizon. DFA gives strategy for Enterprise Risk Management in order to avoid undesirable outcomes, which could be disastrous.

As the primary risk absorbers in the economy, insurance companies must have a high degree of confidence that they can meet their obligations. ERM helps companies estimate the financial impact of these organization-threatening “tail” results, gain an understanding of which risk factors cause them, and point the way to effective risk containment solutions.

R-square’s proven ERM expertise lies in integrating all types of business risk-including those that are notoriously difficult to quantify-into one coherent view. Ultimately, critical risk analysis allows clients to optimize the overall risk & return trade-off, maximizing an organization’s ability to confidently take on risk-bearing profit opportunities.

We work on the key business processes:

  • Risk appetite & control – ascertain the boundaries for risk taking and ensuring that the business stays within those borders
  • Risk-based performance – appropriately allowing for risk in measuring, monitoring and targeting business performance
  • Capital requirement – understanding basic or minimum capital requirements and efficiently deploying capital resources, consistent with the strategic goals

We develop an ERM strategy model that allowed the company to succinctly express its overall risk positions by identifying key balance sheet synergies and illustrating the sustaining power of varying levels of economic capital. ERM demands highly accurate calculation of economic capital, which is ultimately the balance sheet bulwark against the ravages that catastrophic events can inflict.

Economic capital as the methods or practices that allow insurance companies to attribute capital to cover the economic effects of risk-taking activities. It is a measure of risk rather than capital held, and is distinct from accounting and regulatory capital measures. It provides institution with a “common currency” for measuring, monitoring, and controlling different risk types and the risks of different business units. The risk types typically covered include: credit risk, market risk, operational risk, business risk. They can be measured at various levels, from firm-wide to an individual portfolio. Economic capital has become an increasingly accepted input into decision-making within Financial Institutions. The increased use of economic capital has been driven by rapid advances in risk quantification methodologies, Economic capital measures, issues related to the choice of risk measures, aggregation of risk, and the validation of economic capital models. Nearly all large, internationally active banks set their economic capital solvency standard at a level they perceive to be required to maintain a specific external rating. They tend to look at peers in choosing external ratings and associated solvency standards. The most widely cited reasons for adopting an economic capital framework are to improve strategic planning, define risk appetite, improve capital adequacy, assess risk-adjusted business unit performance and set risk limits.

Along with ERM, FRM we would also like to work on reinsurance in the area of risk transfer to the capital markets, with a focus on the structuring and distribution of ILS and derivatives. Moreover, as per growing market trends impacting the P&C insurance industry we think the requirements also are growing in these following arenas: (ERM) – Risk assessment, Financial risk modeling, Operational risk modeling (Economic capital issues), Capital management, Asset/liability management, Reinsurance and hedging strategy. In future we need to implement the financial modeling solutions and delivery (functional point of view)- Economic scenario generators, DFA, Model implementation assistance. Additionally, the following areas would be also interesting to initiate from the basics like: data mining, predictive modeling and customer scoring, market entry, analysis and positioning, competitive analysis and benchmarking, claim cost management etc. Mergers & acquisitions and restructuring (investment backing)- acquisition strategy, support in sales and capital raising, reinsurance and financing alternatives. From capital market side – Insurance brokerage, portfolio management, equity/ fixed income, fund of fund etc are important as well.

Our ERM model offering: ERM modeling:
a) DFA modeling / tool: Modeling and Management of Nonlinear Dependencies-Copulas in Dynamic Financial Analysis, Dynamic Financial Analysis as the untrodden path for company risk measurement under Solvency-II – Dynamic Financial Analysis (DFA) is the most advance modeling process in today’s property and casualty industry-allowing us to develop financial forecasts that integrate the variability and interrelationships of critical factors affecting our results. Through the modeling of DFA, we see the company’s relevant random variables is based on the categorization of risks which is generated solvency testing where the financial position of the company is evaluated from the perspective of the customers. The central idea is to quantify in probabilistic terms whether the company will be able to meet its commitments in the future. DFA is in the capital budgeting decision process of a company launching a new invention and predicting the impact of the strategic decision on the balance sheet in a horizon of few years.

To recognize the few factors that will affect the asset liability cash flow are demand uncertainty, sales volatility, credit risk, volatility in the price of raw materials cost of capital to name a few. Each of these random variables can be stochastically simulated either based on the distribution of retrospective data or under strategic assumptions. When simulated in a combined way the future cash flows can be predicted which in return would dictate the capital requirements in the future. Depending on the capital structure of the company and simulated interest rate in the capital market the final earnings volatility of the company can be predicted to identify the return and associated risks.

We help our clients implement enterprise risk management and use it to make important strategic decisions. To explain our ideas in depth – and to show how we can help your organization – we are launching a new thought leadership series of articles and videos for insurers interested in embedding ERM.

Being possess a unique characteristic called ‘non-subjective to risk manager’s preferences’, our risk model will possibly be least governed by human bias. Although critics may argue that any search for a single ‘best’ risk measure – one that is best in all conceivable circumstances – would appear to be futile, our modification on VaR will necessarily widen the scope of tail related risk models in institutional and regulatory policymaking.

“The Quants know better than anyone how their models can fail. For banks & insurance companies, the only way to avoid a repetition of the current or past crisis is to measure and control all their risks, including the risk that their models give incorrect results. On the other hand, the surest way to repeat this disaster is to trust the models blindly while taking large-scale advantage of situations where they seem to provide trading strategies that would yield results too good to be true”.

Economic Risk Capital

Economic capital models can be complex, embodying many component parts and it may not be immediately obvious that a complex model works satisfactorily. Moreover, a model may embody assumptions about relationships between variables or about their behaviour that may not hold in all circumstances (e.g under periods of stress). Validation can provide a degree of confidence that the assumptions are appropriate, increasing the confidence of users (internal and external to the bank) in the outputs of the model.

The validation of economic capital models is at a very preliminary stage. There exists a wide range of validation techniques, each of which provides evidence for (or against) only some of the desirable properties of a model. Moreover, validation techniques are powerful in some areas such as risk sensitivity but not in other areas such as overall absolute accuracy or accuracy in the tail of the loss distribution. Used in combination, particularly in combination with good controls and governance, a range of validation techniques can provide more substantial evidence for or against the performance of the model. There appears to be scope for the industry to improve the validation practices that shed light on the overall calibration of models, particularly in cases where assessment of overall capital is an important application of the model. It is advisable that validation processes are designed alongside development of the models rather than chronologically following the model building process. There is a wide range of validation processes and each one provides evidence for only some of the desirable properties of a model. Certain industry validation practices are weak with improvements needed in benchmarking, industry wide exercises, backtesting, profit and loss analysis and stress testing and followed by other advanced simulation model. For validation we adhere to the below mentioned method to calculate.

Calculation of risk measures:
In their internal use of risk measures, banks need to determine an appropriate confidence level for their economic capital models. It generally does not coincide with the 99.9% confidence level used for credit and operational risk under Pillar 1 of Basel II or with the 99% confidence level for general and specific market risk. Frequently, the link between a bank’s target rating and the choice of confidence level is interpreted as the amount of economic capital necessary to prevent the bank from eroding its capital buffer at a given confidence level. According to this view, which can be interpreted as a going concern view, capital planning is seen more as a dynamic exercise than a static one, in which banks want to hold a capital buffer “on top” of their regulatory capital and where it is the probability of eroding such a buffer (rather than all available capital) that is linked to the target rating. This would reflect the expectation (by analysts, rating agencies and the market) that the bank operates with capital that exceeds the regulatory minimum requirement. Apart from considerations about the link to a target rating, the choice of a confidence level might differ based on the question to be addressed. On the one hand, high confidence levels reflect the perspective of creditors, rating agencies and regulators in that they are used to determine the amount of capital required to minimise bankruptcy risk. On the other hand, use of lower confidence levels for management purposes in order to allocate capital to business lines and/or individual exposures and to identify those exposures that are critical for profit objectives in a normal business environment. Consequently, banks typically use different confidence levels for different purposes. Another interesting aspect of the internal use of different risk measures is that the choice of risk measure and confidence level heavily influences relative capital allocations to individual exposures or portfolios. In short, the farther out in the tail of a loss distribution, the more relative capital gets allocated to concentrated exposures. As such, the choice of the risk measure as well as the confidence level can have a strategic impact since some portfolios might look relatively better or worse under risk-adjusted performance measures than they would based on an alternative risk measure.

Stress Testing:
We employ stress testing and scenario analysis to evaluate the impact of sudden stress events on our liquidity position The scenarios are based on historic events Action steps would include selling assets, switching from unsecured to secured funding and adjusting the price we would pay for liabilities (gap closure). This analysis is fully integrated within the existing liquidity risk management framework.

Stress testing analysis provides guidance as to our ability to generate sufficient liquidity under critical conditions and is a valuable input parameter when defining our target liquidity risk position.

“The Quants know better than anyone how their models can fail. For banks & insurance companies, the only way to avoid a repetition of the current or past crisis is to measure and control all their risks, including the risk that their models give incorrect results. On the other hand, the surest way to repeat this disaster is to trust the models blindly while taking large-scale advantage of situations where they seem to provide trading strategies that would yield results too good to be true”.

Objective of Stress Tests:
understand the sensitivity of the portfolio to changes in various risk factors revaluing a portfolio using a different set of assumptions It can be can be used to assess a variety of risks, including that of the market (the possibility of losses from changes in prices or yields), credit risk (potential for losses from borrower defaults or non non-performance on a contract), and liquidity risk (the possibility of depositor runs or losses from assets becoming illiquid).

Stress testing is primarily an exercise in communication between the authorities and the financial sector – both searching for a better fix on potential vulnerabilities A number of factors are involved in the process like the type of risks to analyse, whether single or multiple risk factors are to be shocked, what parameter(s) to shock) (prices, interest rate, volatilities, correlations), by how much (based on historical or hypothetical scenarios) and over what time horizon . Using this model, we assess the interrelationship between the macroeconomic environment and sectoral defaults, and perform a series of stress tests under different scenarios that are thought to be most pertinent to the concerned economy. Stress testing measures the risk arising from abnormal market events whereas VaR analysis focuses on the risk arising from low probability events in normal markets. VaR analysis assigns a single quantitative value to the maximum potential loss that can result for a portfolio within a specific confidence interval and over a specific holding period.

Hedging Strategies

Market Risk & Hedging:
Trading in order to use optimization procedures to generate portfolios, maximizing expected returns, which are perfectly aligned with their stated risk preferences. Similar objectives apply to those who use simulation or DFA techniques. I have practice experience on the issues like: 1) Emerging market, 2) Trend, 3) Economic Factor Model, 4) Short Volatility Model, 5) Long Volatility Model (applied with Tier 1 investment bank in London)

Back Testing : Back Test Measures

  • Loss Exception Deviation
  • Average deviation of loss exceptions from CVaR
  • Average Loss Duration
  • Average time interval between successive loss exceptions
  • Loss Duration Deviation
  • Standard deviation of time interval between successive loss exceptions

Estimation
Volatility Estimation

  • Volatility Forecast and Evaluation
  • Volatility Time series models
  • ARCH
  • Short vs. Long memory models
  • Stochastic Volatility Models
  • Extension to Multivariate and Jumps
  • VaR (Value at risk)

Our approaches towards the asymmetric strategy such as hedging modeling (for equity-index movements) I do work on short – gamma, while leaving gamma un-hedged when the portfolio was net long. The real value proposition for my clients are to gain potentially towards higher returns with lowest possible risk factors, how my involvement should be value added proposition in order to extend the risk management tool for country and economy specific. For an example we could derive the Fourier transform of the security return for option or derivative pricing. In order to handle complex pricing issues we propose an estimation strategy and estimate the model with the data analysis so we could identify areas that the model needs to be improved. We have the competences using high end analytical modeling approaches which will be beneficial for bank, investment bank for credit default spread option which provides the right and not the obligation to buy or sell protection on an underlying reference credit, collateralized debt obligations (CDO), Credit linked notes, total return swaps. A big challenge is that there simply was not enough data to make the models reliable enough. The mathematics was only “correct” when they were fitted to a few data points. We have techniques & can work on small sample size and also have the capacity for missing data problem.

Dynamic Financial Analysis

We have developed an algorithm for DFA that enables the creation of a comprehensive framework to manage Enterprise Risk. DFA is used in the capital budgeting decision process of a company to launch a new invention and predict the impact of the strategic decision on the balance sheet in the horizon. DFA gives strategy for Enterprise Risk Management in order to avoid undesirable outcomes, which could be disastrous.

The approach begins with collection of data and information that can be utilized to perform the analysis. The requested information will include discussion with Bahamas executives as well as reinsurance intermediaries reinsurers regulators and rating agencies, estimate the impact of any changes in reinsurance structure on the level of operating profit and on the variability of profits from two perspectives. One of the perspectives is from “as if” changes to the actual historical results for the last ten years, and the second perspective would be based on dynamic financial analysis of projected future financial results.

Determine required solvency capital for P&C insurers in recent market: CIRC’s Regulatory Strategies: Three pillar framework has been accepted- (1) Solvency (2) Governance (3) Market Conduct; On solvency pillar- 1) Reserving standard has been implemented from 2005, 2) A series of valuation rules for admissible assets and liabilities has been issued for actuarial solvency, 3) But Statutory required/minimum solvency standard has not been updated since 2000. Flexible approach to troubled companies has allowed: a) Some to obtain outside capital or be acquired, b) others to transfer books of business and go into orderly runoff, c) Has avoided some insolvencies, reduced the costs of others. Modeling Catastrophe Risk & impact via DFA: New Options (for optimizing the profitability curve or improving rating et al), 1) New Risk to be evaluated: eg: i. Reinsurance deal with CAT model output; ii. CAT bond with details of payout, risk details (geography, perils covered et al); iii. ILW – details, including Pricing; 2) List of New assets to look into; 3) Goals: Desired level/distribution of Net Profit (estimated), Ratings Score et al.

We will look into portfolio (with restrictions) and we can provide them with combine view.

Outline 1:

  • Capital calculation along the Loss Curve
  • Hierarchy of Quantitative Models

What additional data would be required to do a DFA, what would DFA output show to the customer. We easily create a sample for discussion, what are the limitations of DFA

Outline 2: DFA and Portfolio Management
Our real value propositions are

  • Formation of stable Risk- Asset
  • Revised technique for Profit – Risk evaluation
  • Robust analysis for judging the independence of various sectors to safeguard existence.

Utilization of excess Capital for larger Profitability and Risk Reduction1. Stabilized Risk-Asset allocation:
DFA allows you to assess current health of Risks (liabilities) as well as Assets. Marginally and Jointly. The entire portfolio should be analyzed and have stabilized volatility (with respect to Line of Business, geography, and deal variations). So, ideally, a new entrant should be from a field, which is independent of the current set of insurance basket.

2. Incremental Profit-Risk evaluation:
Lets you assess if new deal is suitable or not in view of incremental expected profits Vs. Incremental Risks. Also it lets user find ‘Gaps’ that can be filled to make overall portfolio better. Even a deal which appears to be lucrative initially gives rise to volatility to the overall portfolio is practically a bad choice as a new contract.

3. Cautious Steps for Risk prone Market:
Given the long-term contract risk and competitive market scenario – where risk assessment has been in question (independence of different entities that gets used to increase diversity and decrese variance / risk). We allow client to modify those assumption as well as allow user to make changes.

Demand Forecasting & Risk Management - Partial List of Engagements
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Supply Chain Risk Management
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OTC Derivatives (Credit Derivatives), Financial Risk Management (FRM) & Brokerage

Lead a team that devised the approach in terms of validating a customer’s pricing model and deliverables thereof to analyze data where by to identify features of the data that we plan to capture with our model. We design a model that captures the features you have documented. For an example we could derive the Fourier transform of the security return for option or derivative pricing. In order to handle complex pricing issues we propose an estimation strategy and estimate the model with the data analysis so we could identify areas that the model needs to be improved. We have the competences using high end analytical modeling approaches which will be beneficial for bank, investment bank for credit default spread option which provides the right and not the obligation to buy or sell protection on an underlying reference credit, collateralized debt obligations (CDO), Credit linked notes, total return swaps. A big challenge is that there simply was not enough data to make the models reliable enough. The mathematics was only “correct” when they were fitted to a few data points. We have techniques & can work on small sample size and also have the capacity for missing data problem.

We have experience in financial/ insurance risks as well as handling of modern financial instruments (options, futures or swaps) with a sound understanding of the models and techniques used in insurance & finance – risk measurement. Portfolio credit risk/ market risk: the latest techniques in cash flow, simulation, time series and stochastic optimization forecasting techniques, pricing, credit derivatives. Development and improvement with product control, CDS, CDO, Bonds. Credit Default swaps, modeling the spread between the forward rates, functional recovery modeling, direct valuation of credit risk derivatives, the multifactor Gaussian models etc. Structure of dependency and the choice of procedures forrare-event simulation on the pricing of multi-name credit derivatives – Collateralized Debt Obligations (CDO). A copula based simulation procedure for pricing basket default swaps and CDO under different structure of dependency and assessing the influence of different price drivers (correlation, hazard rates and recovery rates) on modeling portfolio losses. Gaussian copulas and Monte Carlo simulation to measure the default risk.

Econophysics

As per one of the recent Yale publications Econophysicists have found that the income distributions of most nations fit a power law strikingly well, a fact first observed by Vilfredo Pareto in 1897. A critical feature of power laws is that they possess extended tails, accounting for the vast difference in income between a wealthy doctor earning $300,000 a year and a billionaire such as Bill Gates.

As per Yale Economic Review (Spring 2006) the philosophical approach of econophysics is certainly different from that of economics in general. While economists begin with a few fundamental assumptions and then construct a theoretical model to explain observations, econophysicists tend to start with the empirical evidence and extract patterns from the data. In doing so, econophysicists do not rely on assumptions of rationality, which have proven experimentally inconsistent in some cases, such as transitivity of preferences. In fact, some illustrative econophysics models that produce qualitatively accurate results instead mimic random processes, a finding that neoclassical economists have difficulty reconciling with their assumption of rationality.

e-physics

R-square and it’s RiskLab have created an international group on Econophysics research and practice. The group aims to bring together international researchers in all areas of finance to discuss recent developments in financial research and practice mathematical sciences conducts research in mathematical theories and models, and their applications with chaotic dynamics and fractals. We pitch to research institute, government & corporate to work closely or exchange ideas on all issues in the field of finance, including financial economics, macro and micro finance, mathematical finance, empirical finance as well as financial econometrics and the interaction with combination of economy and physics which enables such productive grounds for research of “physics of complexity” for entire arena of Finance.

These days world participants are primarily NOT finance or risk professional with mathematics background, therefore they have nothing mathematically new to say about probability space, and measure space. Risk managers, who are not very motivated in bringing the core mathematics of risk management forward, they pick out of fashionable recipe books, which is very dangerous. We aim working with experienced practitioners closely on inferences. We are interested in non-orthodox mathematical complexities in our offerings (chaos, fuzzy, phase transitions). We have motivated and we posse as an immediate plan to develop Kolmogorov theory for Risk management practice.

The aim of the product input would be from Physics, Economics, Finance, Mathematics, Biology, Computer Science and Engineering etc. We are as well planning to form a forum from 2010 regarding exchange ideas and methods and confront different view points on common problems linking economics and physical sciences, or broadly speaking soft and hard sciences.

Telecom Analytic
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