In the beam of risks involved, please undertake your transactions only if you know the real life situations in the market. Otherwise your investments are subject to profit/loss. The investments in forex, equity shares, derivatives and others have varying element of risk. You should therefore carefully consider whether such trading is suitable for your financial condition. In case you trade and undergo unpleasant consequences, no one is responsible for such a trade. You must acknowledge and accept that there can be no guarantee of profits or no exception from losses while trading in different markets.

Trading is done in forex and some other markets on the basis of mutual trust where both the parties do not disclose their identity. All activities are executed via the Trading Software, where a buyer quotes his price and a seller accepts the price. This provides transparency to the trading system. Trading members identify the clients on whose behalf the orders are placed or else the orders are accounted to their own account. Trading system provides a time stamp for each order entered and trades executed.

We are providing this risk disclosure to heighten your awareness about the risk engaged in business in general and financial market in particular and to inform the various programs and options available in the market. In business a very few of the risks are predictable, all other is on the path we move and in order to minimise the risk in business the investors have a curious eye towards the market they are trading. If there is any lesson that history teaches us, it is the need to take a cold look at risk versus reward.

It is well known that risk management is not particularly relevant to firm value in a complete markets setting, since investors can undo the financial structuring according to their preferences. However, such an environment does not capture many relevant and interesting motives for risk management due to information asymmetries and other transaction costs. In the context of bank regulations, if markets are incomplete, risk constraints can have real effects on bank value.

Still other events can directly affect your day-to-day operations, reduce profits and result in unexpected financial losses serious enough to cripple or even bankrupt your business. You've probably considered the most obvious risks, such as fire or injury, and have bought insurance to protect against them. But there are hundreds of other losses and liabilities that every small business faces, many of which are overlooked or ignored. The growth in volume and complexity of financial markets during the past few years, especially derivatives markets with infamous financial disasters in transactions increased concern over the risk introduced and other complex instruments in the context of worldwide integration of financial markets.

At individual firms’ level, this posses an increasing threat to their ability to keep control over their exposure to risk in a diverse environment. At an aggregate level, they have some fear that default by one firm could spread out to others in the same country or even cross-borders, and become a financial crisis of huge proportions. This is a major concern not only for regulators, but also for markets participants’ altogether.

In this context, risk management has become an essential part of business in firm and regulator activities. A risk warning system is a valuable instrument for assessing the exposure to risk that participants in the financial sector in general are subject to. Using such systems, managers can measure risk across markets in terms of their potential impact on profit and loss, quantify capital allocation to markets and dealers, establish meaningful risk limits and supervise performance.

Risk systems also provide a measure of the amount of capital necessary to provide a cushion against potential future losses, a vital element for both managers and regulators. The financial marketplace strength, as a whole, ultimately depends upon individual’s ability to cover unexpected losses with capital reserves. Firms are using the best risk management systems in order to avoid maximum losses that are even subject to the financial market portfolio and creating a proper capital cushion in the client side to overcome the essential systems. Not surprisingly, setting capital adequacy standards is at the core of regulators’ responsibilities, together with efficient surveillance and supervision of market participants.

Every promoter and market maker is giving adequate knowledge regarding the risk associated with the currency markets. It’s the role of client itself to avoid the loss in trading. We as a provider and advisor will help you to minimise the risk in the markets. But anyone in the market can’t predict the upcoming scenario that trails or mounts the markets. As part of risk in financial markets the scope of discussion of the current stage has been extended to the member clients in the existing markets.

The institutions and corporate companies are continuously informing the investors about the various risks associated with the money markets. Firms and supervisors in the three sectors place different emphases on the various risks facing financial firms. The risk management approaches across the different sectors are discussed constantly in order to minimise the risk to generate maximum profit from the amount invested. In examining risk management techniques, the major focus is on the key risks faced by each sector.

One of the primary concerns of any regulator is that supervised institutions are able to meet their financial promises to customers. However, the nature of these promises can differ greatly: from obligations to repay fixed amounts of deposits and other borrowings along with interest calculated at a pre-determined rate (as is common in banking and securities firms), to obligations to make payments in which the rate of return involved is determined by the performance of financial markets (such as a unit linked life insurance product), to obligations in which the contractual payments are contingent on some future event (for example, under a general insurance policy). Because the nature of these financial promises differs, the risks which might cause a supervised institution to be unable to meet its financial obligations can arise from quite different sources.

The integration of emerging market countries into the global economy and their greater access to external sources of financing have produced a corresponding increase in their exposure to swings in international asset prices. Developing sovereign entities are especially exposed to international disturbances because of their large stock of un-hedged foreign currency debt and the risky structure of their debt portfolios which includes currency composition and maturity profile. In a relatively unfamiliar, and at times volatile, international financial environment, the benefits earned by countries through prudent macroeconomic management and structural reforms can be severely compromised by losses due to unexpected changes in interest rates and exchange rates.

The major multinational firms, both financial and non financial have adapted to similar risks by extensively using hedging techniques and derivative instruments to manage their risk exposures. The use of such techniques has been facilitated by important advances in financial technology in the last decade and by specialized risk management techniques developed by institutional funds managers. In contrast, many sovereign entities—some of them major players in international financial markets with large financial assets and liabilities—have lagged, by and large, behind the private sector in this respect. The recent experience of a small, but growing, number of sovereign borrowers that have reformed their liabilities management practices demonstrates that sound risk management can reduce the impact of external financial developments on debt portfolios, and potentially lower the cost of borrowing. The existing literature on risk management is rich in its treatment of portfolio allocation problems, but it provides little guidance for sovereigns on how to manage the risk associated with sovereign debt exposures. By drawing on the experience and the well established methodologies of large institutional investors and pension funds, and on the experience they reformed their debt management policies.

Risk factors are different according to the varying markets. The major ones are here for you immediate notifications.

Practice of financial risk management has its roots in the broader and older field of risk management in a general context. This broader field of risk management is usually termed decision analysis and forms a sub discipline of statistics, operations research and economics. We are performing this for the need for risk management by discussing some of the recent financial disasters and the response this has received in the form of government regulations and guidelines.

The word ‘risk’ has many meanings and connotations. It is widely used by professional traders, risk managers, and the public. A proliferation of names has emerged to describe various risks: business risk, financial risk, market risk, liquidity risk, default risk, systematic risk, specific risk, residual risk, credit risk, counterparty risk, operations risk, settlement risk, country risk, portfolio risk, systemic risk, legal risk, reputation risk, and many more.

Financial Risks can broadly be divided into market risk, credit risk, liquidity risk, operational risk, and legal or regulatory risk. The following definitions of the different risk types are taken from Crouhy, Galai and Mark.

Market Risk is the risk that changes in the financial market prices and rates will reduce the value of the firm’s positions. Market risk for a fund is often measured relative to a benchmark index or portfolio, and is referred to as the risk or tracking error. Market risk also includes basis risk, a term used in the risk management industry to describe a chance of a breakdown in the relationship between the price of a product, on the one hand, and the price of the instrument used to hedge the price exposure on the other. The market-VaR (value-at-risk) methodology attempts to capture multiple components of market risk such as directional risk, convexity risk, volatility risk, basis risk, etc.

Credit Risk is the risk that a change in the credit quality of counterparty will affect the value of the bank’s position. Default, whereby counterparty unwilling or unable to fulfil its contractual obligations, is the extreme case; however, banks are also exposed to the risk that the counterparty might be downgraded by a rating agency. Credit risk is only an issue when the position is in an asset, i.e. when it exhibits a positive replacement value. In that instance, if the counterparty defaults, the bank either loses all of the market value of the position, or, more commonly, the part of the value that cannot be recovered following the credit event. The value is likely to recover is called the '‘recovery value”; the amount it is expected to lose is called the “loss given default”. Several models are there in the market to measure credit risk.

Liquidity Risk comprises both ‘funding liquidity risk’ and ‘trading-related liquidity risk’ though these two dimensions of liquidity risk are closely related. Funding liquidity risk relates to a financial institution’s ability to raise the necessary cash to roll over its debt, to meet cash, margin and collateral requirements of counterparties, and to satisfy capital withdrawals. Trading-related liquidity risk is the risk that an institution will not be able to execute a transaction at the prevailing market price because there is, temporarily, no appetite for the deal on the “other side” of the market. If the transaction cannot be postponed, its execution may lead to a substantial loss on the position. This risk is generally very hard to quantify.

Operational Risk refers to potential losses resulting from inadequate systems, management failure, faulty controls, fraud, and human errors. Derivatives trading are more prone to operational risk, because derivatives are, by their nature, leveraged transactions. This means that a trader can make very large commitments on behalf of a bank, and generate huge exposures into the future, using only a small amount of cash (at the same time that the transaction is executed). Very tight controls are an absolute necessity if a bank is to avoid large losses.

Legal Risk arises for a whole variety of reasons. For example, the counterparty might lack the legal or regulatory authority to engage in a transaction. Legal risks usually only become apparent, when a counter party, or an investor, loses money on a transaction and decides to sue the bank to avoid meeting its obligations. Another aspect of regulatory risk is the potential impact of a change in tax law on the market value of a position.

Majority of the market players and market makers established an integrated and effective risk management framework where all risks are identified, quantified, and managed in order to achieve an optimal risk/reward profile.

Correlation Risk is the risk associated with the cross dependencies among loans, such as the concentration of loans in a certain geographical area or industry, was often ignored. Credit correlations are one source of risk, but it has become increasingly clear that concentrations among different kinds of risk are also crucial. Perhaps the most striking case of correlation risk across market and credit risk is the crisis of the savings and loan industry. It is only in the late 1990’s that the banking industry has begun to appreciate the risks of correlations between credit and market risk, on the one hand, and liquidity risk on the other. The industry as a whole is now looking at how the relationship between liquidity risk, leverage risk, and market and credit risk can be incorporated into risk measurement and stress testing models.

If ‘correlation risk’ can be identified as one principal source of hidden bank risk, then operational risks are the second major source. Financial disasters of the kind just discussed are a serious concern for national governments.

Because a national government, in effect, acts as a guarantor for commercial banks in their country, they have a very direct interest in ensuring that banks remain capable of meeting their obligations. Therefore regulators impose a unique set of minimum required regulatory capital rules on commercial banks. By acting as a buffer against unanticipated losses, regulatory capital helps to privatise a burden that would otherwise be borne by national governments. In 1988 the Basle Accord established international minimum capital guidelines that linked banks’ capital requirements to their credit exposures. During the last decade we have seen a number of changes in the policies, guidelines and regulations issued by international and national regulators. The 1996 Amendment to the Basle Accord made it clear that banks must implement a risk management infrastructure that is fully integrated with their daily risk management. In particular, with setting internal trading limits and monitoring the risk of operations. It is not enough for banks to develop sophisticated analytical approaches to measure and report regulatory capital. The bank’s risk managers and traders should themselves use these analytical approaches to monitor their positions and their limits. It should be noted that the international regulators encourage banks to develop their own proprietary risk measurement models to assess regulatory, as well as economic, capital.

Currently the focus is very much on Enterprise Wide Risk Management (ERM). The essence of ERM is the management of overall institutional risk across all risk categories and business units. The challenge is now to develop integrated risk models where the interaction between the risk components is well understood and modelled. What is important though, is that risk research, risk modelling and risk analysis are encouraged and often enforced by international regulations on financial risk management. This presents a great challenge as well as an opportunity for statistical science, and for statisticians. Banks must be concerned with these issues and therefore should ensure that they employ people having appropriate statistical modelling skills.

Evolution of risk management

Risk management evolved from a strictly banking activity, related to the quality of loans, to a very complex set of procedures and instruments in the modern financial environment. The first remarkable step to build a framework for systematic risk analysis was the Basle Capital Accord, issued in July 1988. The aim of the Basle initiative was to reach international convergence of rules governing the calculation of levels of capital reserves for banks. The Accord set out the details and the agreed framework for measuring capital adequacy and minimum standards to be achieved by banks within the jurisdiction of the national supervisory authorities represented on the Committee, intended to be implemented in their respective countries.

The Basle framework, in its original version, is mainly directed towards assessing capital in relation to credit risk. The model sets out capital requirements according to a formula based on risk factors applied to categories of assets, rated according to their potential risk. The Basle directives are standardized, and have been implemented not only in the ten countries that were original members of the Banking Supervision Committee of the Bank for International Settlements, but also in many other countries throughout the world.

In 1993, the Basle methodology was revised, and credit risk analysis was improved. But, more importantly, new provisions, to take into account of market risk, already recognized as a major source of risk, were announced as a necessary development. A new methodology was put forward for discussion, contemplating a standard model for the assessment of market risk. However, by that time many leading banks and securities houses had already developed their own proprietary models for the assessment of market risk. These models were based on the Value-at-Risk methodology, or VaR, and provided levels of capital reserves lower than those produced by the Basle Committee's proposed methodology. This is so because VaR uses a portfolio approach, measuring risk in a comprehensive and integrated manner, taking into account the correlations between the behaviors of prices of different assets that exist in diversified portfolios.

The standard Basle methodology, on the other hand, uses a partial analysis, measuring risk as the summation of risks of individual assets, ignoring correlations and thus the effects of diversification, thereby tending to overestimate total risk. Firms argued that the VaR models were more accurate in capturing the overall exposure of large and diversified portfolios than the standard Basle methodology, and consequently their lower levels of capital reserves did not mean less safety. Therefore, in January 1996, the Basle Committee on Banking Supervision released an amendment to the July 1988 Capital Accord to apply capital charges to the market risks incurred by banks. Another important innovation of the amendment was that it permits banks to calculate their market risk capital charges according to one of two models, the standardized measurement method or proprietary models based on VaR. Banks using internal models will be subject to a set of qualitative and quantitative standards, the outcome of their VaR calculations will have to be multiplied by three (i.e., take the model outcome and multiply it by 3 to set the level of regulatory capital required) and their models are subject to approval by national regulators. The amendment will come into effect by the end of 1997.

Currently, market risk management is a major concern not only for banks, which are usually subject to stricter regulations in terms of capital adequacy, but also for securities firms and broker-dealers. Also clearinghouses have developed models for the calculation of margins in derivatives markets and monitoring of risks incurred by their participants.

A word about Value-at-Risk

VaR can be defined as the maximum loss on a portfolio, over a standardized period of time, usually one day, that would result from an adverse market movement expected to occur once in a longer period of time, usually one hundred days, within a confidence interval, usually 99%. Alternatively, it can be seen as the estimated change from the present price of an instrument (or portfolio) until the point at which it could be liquidated.

The VaR methodology views a firm as a giant portfolio, and produces a single currency-denominated figure indicating the risk across many financial instruments and markets on a firm-wide level, avoiding the overestimation problem caused by partial analysis. Besides, it provides a tool for establishing meaningful risk limits on market activities and for assessing performance. The concept is simple, although the implementation is less so. Price data relating to the components of a portfolio are collected for a chosen observation period. Volatilities or standard deviations of assets prices, and correlations between assets prices movements are calculated. Statistical analysis combines all these data and allows the estimation of an interval for the value of the portfolio in response to changes in the prices of its components, with a certain probability. It also provides a distribution of values for losses or gains that would occur if the current positions were held for a specified holding period. A confidence interval is then applied to the distribution to assess the maximum loss that would be expected, not to be exceeded with a certain probability, thereby determining the Value-at-Risk of the current portfolio. In other words, this enables management to calculate the likely currency denominated maximum loss for a certain period, and the figure is expressed in terms of a confidence level. A confidence interval of 99% means that the risk manager can define the maximum loss at 99% probability, that is, the loss that should be exceeded only one day out of a hundred.

If the portfolio contains derivatives, the analysis becomes more complex, since the prices of derivatives depend non-linearly on the prices of the underlying assets, especially in the case of options. Therefore, the changes in the values of derivatives in response to changes in the prices of the underlying assets, or risk factors, such as interest rates, exchange rates or equity indexes, must also be calculated and added to the mainstream analysis. Since the relationships between prices are not linear, this task involves a great deal of statistical work.

The main shortcomings of a VaR measure are: historical volatilities and correlations may not be representative of the future ones; lack of liquidity of some instruments is not taken into account; confidence intervals are only statistical assumptions, and not only can a firm lose more than the Value-at-Risk, it can lose more on certain occasions; there is the need for worst case stress tests; the nonlinearity of risks associated with options, futures and other instruments with embedded options features; and, above all, it cannot be rigidly interpreted: personal judgement is needed to interpret the information, ask the right questions, make more realistic evaluations of what the future may hold and take the right action.

A benchmark in the development of VaR models was the Risk Metrics methodology, firstly released in the end of 1994, at the beginning of the discussion about whether or not VaR was a adequate tool for establishing levels of capital reserves. There are also other well-known risk management models, adopted by some clearinghouses that are based on the VaR methodology.

The Role of Regulators in Risk Management

The basic role of regulators regarding risk management is to seek an assemblage of rules and requirements that may, at the lowest possible cost, effectively contribute to prevent an isolated failure or a crisis of small proportions from becoming a systemic problem threatening the market as a whole. In other words, the best solution for the trade-off referred to above. As mentioned earlier, this concern also pertains to the industry, as a matter of collective safety. Regulators and industry should thus work in tandem for the development and improvement of risk control systems and rules. This has been so in general, but on some occasions voluntary convergence is not easily reachable.

Regulators have recognized models based on the VaR methodology as good predictors for potential losses, and these models will be accepted for purposes of calculation of capital charges by banks and securities firms as from the end of 1997. But there is and there will always be a tension between the uses of VaR for management and for regulators. Take the issue of the multiplier factor of 3 imposed by the Basle Committee to allow banks to use proprietary VaR models to set capital reserves. The reason for the use of this factor is that VaR produces, say, 99% confidence intervals for its predictions. In one sense, what happens within the interval is not of concern to regulators. It is what happens when reality falls outside the interval that scares regulators. But conservativeness is present also in the standard methodology put forward as an alternative to proprietary models, as well as in the original methodology for credit risk analysis: the multiplier and other forms of conservatism only try to “exaggerate normality” rather than establishing genuine and accurate worst case scenarios.

Traditionally, regulators have focused their job in the following main areas, with regard to risk management:

• market surveillance, with a special attention on large positions and aggregated cross-market supervision;
• setting levels of capital reserves;
• disclosure of data and information about market value of financial instruments and risk policies;
• together with capital charges, this is an area where firms’ costs may increase significantly as a result of additional requirements;
• it has been suggested that firms be allowed to use for regulatory purposes the same kind of information used for internal purposes, to avoid duplication;
• auditing of firms’ books and financial registers and internal controls, integrity and soundness of the models and segregation of accounts;
• cooperation and exchange of information between regulators both at domestic and international level;
• this is one of the areas where regulators have concentrated a great deal of joint efforts and initiatives;
• Development of emergency procedures, that is, procedures to react effectively at the time of market emergencies.

Risk is the fundamental element that influences financial behaviour. In its absence, the financial system necessary for efficient allocation of resources would be vastly simplified. In that world, only a few institutions and financial instruments would be needed, and the practice of finance would require relatively elementary analytical tools. But, of course, in the real world, risk is everywhere. Much of the structure of the financial system we see serves the function of the efficient distribution of risk. Much of the financial decision making by households, business firms, governments, and especially financial institutions is focused on the management of risk. Measuring the influence of risk, and analysing ways of controlling and allocating it, require a wide range of sophisticated mathematical and computational tools. Indeed, mathematical models of modern finance practice contain some of the most complex applications of probability, optimisation, and estimation theories. Those applications challenge the most powerful of computational technologies”

Our aim is to give the reader a broad overview of the field of Financial Risk Management. We already covered risk management, the definition of risk and risk types, and risk modelling in general. Statistical models in the areas of market risk, credit risk, operational risk and enterprise-wide risk management where discussed briefly. We will conclude that the subject of statistics is necessary for doing cutting-edge research in this area, especially in the area of pricing financial instruments and in risk modelling in general.

Clearly the management of financial risk has become an essential requirement for any financial institution. In the last two decades there has been an enormous growth in the financial risk management industry. Not only in its size, but perhaps even more so, in its complexity, has due to an ever-growing number and variety of investment instrumented available. Furthermore, the financial service industry has become more and more of a global market, leading not only to increased global competition, but also to increasing global opportunities. This, however, also means that the management of the risk involved has become increasingly complex. In addition, development in technology has made vast amounts of data much more freely available, as well as the necessary methodologies and software to analyse such data.

On the other hand, increased volatility and a number of major financial catastrophes during the last years of the previous millennium have highlighted the need for, inter alias, more stringent and sophisticated risk management procedures. Since risk is due to uncertainty about the future and since statistics can be thought of as the “science of making decisions under uncertainty”, there is a very natural role for statistics, and its associated probability theory, in developing risk management procedures.