Market Risk and Financial Markets Modeling

Investors’ Uncertainty and Stock Market Risk
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Vragen over deze vacature? Meer informatie. The vendor provides, individually, a minimum necessary set of identifier information to enable banks to map real prices observed to risk factors. The vendor is subject to an audit regarding the validity of its pricing information. The results and reports of this audit must be made available on request to the relevant supervisory authority and to banks as a precondition for the bank to be allowed to use real price observations collected by the third-party vendor.

If the audit of a third-party vendor is not satisfactory to a supervisory authority, the supervisory authority may decide to prevent the bank from using data from this vendor. In this case, the bank may be permitted to use real price observations from this vendor for other risk factors.

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A real price is representative for a risk factor of a bank where the bank is able to extract the value of the risk factor from the value of the real price. The bank must have policies and procedures that describe its mapping of real price observations to risk factors.

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The bank must provide sufficient information to its supervisory authorities in order to determine if the methodologies the bank uses are appropriate. Where a risk factor is a point on a curve or a surface and other higher dimensional objects such as cubes , in order to count real price observations for the RFET, banks may choose from the following bucketing approaches:. The own bucketing approach.

Under this approach, the bank must define the buckets it will use and meet the following requirements:. The regulatory bucketing approach.

How Long Memory in Volatility Affects Market Risk Estimation

Under this approach, the bank must use the following set of standard buckets as set out in Table 1. For interest rate, foreign exchange and commodity risk factors with one maturity dimension excluding implied volatilities t, where t is measured in years , the buckets in row A below must be used. For interest rate, foreign exchange and commodity risk factors with several maturity dimensions excluding implied volatilities t, where t is measured in years , the buckets in row B below must be used. Credit spread and equity risk factors with one or several maturity dimensions excluding implied volatilities t, where t is measured in years , the buckets in row C below must be used.

For expiry and strike dimensions of implied volatility risk factors excluding those of interest rate swaptions , only the buckets in rows C and D below must be used. For maturity, expiry and strike dimensions of implied volatility risk factors from interest rate swaptions, only the buckets in row B , C and D below must be used.

Standard buckets for the regulatory bucketing approach. The requirement to use the same buckets or segmentation of risk factors for the PLA test and the RFET recognises that there is a trade-off in determining buckets for an ES model. Banks should consider this trade-off when designing their ES models. For options markets where alternative definitions of moneyness are standard, banks shall convert the regulatory delta buckets to the market-standard convention using their own approved pricing models.

Banks may count all real price observations allocated to a bucket to assess whether it passes the RFET for any risk factors that belong to the bucket. A real price observation must be allocated to a bucket for which it is representative of any risk factors that belong to the bucket. As debt instruments mature, real price observations for those products that have been identified within the prior 12 months are usually still counted in the maturity bucket to which they were initially allocated per MAR When banks no longer need to model a credit spread risk factor belonging to a given maturity bucket, banks are allowed to re-allocate the real price observations of this bucket to the adjacent shorter maturity bucket.

For example, if a bond with an original maturity of four years, had a real price observation on its issuance date eight months ago, banks can opt to allocate the real price observation to the bucket associated with a maturity between 1. A bank may use systematic credit or equity risk factors within its models that are designed to capture market-wide movements for a given economy, region or sector, but not the idiosyncratic risk of a specific issuer the idiosyncratic risk of a specific issuer would be a non-modellable risk factor NMRF unless there are sufficient real price observations of that issuer.

Market Risk and Financial Markets Modeling | Didier Sornette | Springer

Real price observations of market indices or instruments of individual issuers may be considered representative for a systematic risk factor as long as they share the same attributes as the systematic risk factor. In addition to the approach set out in MAR Once a risk factor has passed the RFET, the bank should choose the most appropriate data to calibrate its model.

The data used for calibration of the model does not need to be the same data used to pass the RFET. Once a risk factor has passed the RFET, the bank must demonstrate that the data used to calibrate its ES model are appropriate based on the principles contained in MAR Where a bank has not met these principles to the satisfaction of its supervisory authority for a particular risk factor, the supervisory authority may choose to deem the data unsuitable for use to calibrate the model and, in such case, the risk factor must be excluded from the ES model and subject to capital requirements as an NMRF.

There may, on very rare occasions, be a valid reason why a significant number of modellable risk factors across different banks may become non-modellable due to a widespread reduction in trading activities for instance, during periods of significant cross-border financial market stress affecting several banks or when financial markets are subjected to a major regime shift.

One possible supervisory response in this instance could be to consider as modellable a risk factor that no longer passes the RFET. However, such a response should not facilitate a decrease in capital requirements. Supervisory authorities should only pursue such a response under the most extraordinary, systemic circumstances. Banks use many different types of models to determine the risks resulting from trading positions.

The data requirements for each model may be different. Banks must not rely solely on the number of observations of real prices to determine whether a risk factor is modellable. The accuracy of the source of the risk factor real price observation must also be considered. In addition to the requirements specified in MAR Banks are required to demonstrate to their supervisory authorities that these principles are being followed.

Supervisory authorities may determine risk factors to be non-modellable in the event these principles are not applied. Principle one. The data used may include combinations of modellable risk factors. Banks often price instruments as a combination of risk factors. Generally, risk factors derived solely from a combination of modellable risk factors are modellable. For example, risk factors derived through multifactor beta models for which inputs and calibrations are based solely on modellable risk factors, can be classified as modellable and can be included within the ES model.

Interpolation based on combinations of modellable risk factors should be consistent with mappings used for PLA testing to determine the RTPL and should not be based on alternative, and potentially broader, bucketing approaches. Subject to the approval of the supervisor, banks may extrapolate up to a reasonable distance from the closest modellable risk factor. The extrapolation should not rely solely on the closest modellable risk factor but on more than one modellable risk factor.

In the event that a bank uses extrapolation, the extrapolation must be considered in the determination of the RTPL. Principle two. The data used must allow the model to pick up both idiosyncratic and general market risk.

Idiosyncratic risk is the risk associated with a particular issuance, including default provisions, maturity and seniority. The data must allow both components of market risk to be captured in any market risk model used to determine capital requirements. If the data used in the model do not reflect either idiosyncratic or general market risk, the bank must apply an NMRF charge for those aspects that are not adequately captured in its model.

Principle three. The data used must allow the model to reflect volatility and correlation of the risk positions. Banks must ensure that they do not understate the volatility of an asset eg by using inappropriate averaging of data or proxies.

Different data sources can provide dramatically different volatility and correlation estimates for asset prices. The bank should choose data sources so as to ensure that i the data are representative of real price observations; ii price volatility is not understated by the choice of data; and iii correlations are reasonable approximations of correlations among real price observations.

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Investing Essentials What are the primary sources of market risk? Login Advisor Login Newsletters. Endogenous market risk: updated primer Editor - November 16, Investing Essentials. Financial Analysis.

Principle four. Where data used are not derived from real price observations, the bank must demonstrate that the data used are reasonably representative of real price observations. To that end, the bank must periodically reconcile price data used in a risk model with front office and back office prices. Just as the back office serves to check the validity of the front office price, risk model prices should be included in the comparison.

Measuring and Managing Market Risk

The comparison of front or back office prices with risk prices should consist of comparisons of risk prices with real price observations, but front office and back office prices can be used where real price observations are not widely available. Banks must document their approaches to deriving risk factors from market prices. Principle five. The data used must be updated at a sufficient frequency.

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A market risk model may require large amounts of data, and it can be challenging to update such large data sets frequently. Banks should strive to update their model data as often as possible to account for frequent turnover of positions in the trading portfolio and changing market conditions. Banks should update data at a minimum on a monthly basis, but preferably daily.

Additionally, banks should have a workflow process for updating the sources of data. Furthermore, where the bank uses regressions to estimate risk factor parameters, these must be re-estimated on a regular basis, generally no less frequently than every two weeks. Calibration of pricing models to current market prices must also be sufficiently frequent, ideally no less frequent than the calibration of front office pricing models. Principle six. The data for the ES R,S model should be sourced directly from the historical period whenever possible. There are cases where the characteristics of current instruments in the market differ from those in the stress period. Nevertheless, banks must empirically justify any instances where the market prices used for the stress period are different from the market prices actually observed during that period.