Research methodology pdf for mba

Come on! Research Methodology Important Question and Answer Unit I Introduction Concept, nature, process and significance of management; Managerial roles; An overview of functional areas of management; Development of Management Thought; Classical and neo-classical systems; Contingency approaches Unit II Planning Planning Concept, process and types.

What is Management? Define — Management. Write some characteristics of Management. What are the roles of management in organization? Write any two points in favor for management as a science. Explain the different Business organization. Is Management Profession-discuss?

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Share via. Since much of the business value from risk analytics derives from risk-based pricing there are strong demands that risk analytics can appropriately rank the relative risks of products. Banks with poor granular-level risk models and risk analytics run the risk of mispricing relative to the market and eventually end up with the nonproitable portfolios. Banks with strong granular-level risk analytics and empirically and economically vetted risk models can be more competitive and can also avoid creating excessive risks in portfolios.

They also create more trust in the organization for the use of risk analytics. It has become a practice sometimes to refer to model simplicity itself as an absolute good. The relevant concept is relative simplicity. For example, we would favor a more simplistic model instead of a more complex model if suficient evidence is not available for the added complexity. As Albert Einstein once said, everything should be kept as simple as possible but not simpler than that.

Throughout this book readers can ind numerous topics on risk-adjusted performance and risk-constrained optimization methodologies that help banks create value from proper risk management methods and models.

Accurate risk measurement and monitoring of risk levels is clearly one core objective of risk management models. Using risk management mod- els to also aid risk-based decision making, especially the performance analysis and proit maximization, also makes risk management models central to the business. It is not an exaggeration to say that the growth of risk management goes hand-in-hand with the development of com- putational technology. A inancial risk management system is certainly the platform where the quantitative risk management methodologies materialize in practice.

Traditionally banks have employed silo risk systems to calculate the different types of risks such as market, credit, and asset and liability management risk. Introduction 11 While the risk calculation itself is the core of a inancial risk management system the system capabilities also rely on its risk infrastructure and what risk technology the system can use.

The risk infrastructure should support the use of the system, for example, with proper worklows and coniguration capabilities. The risk technology should be aligned to the requirements of the risk calculation. In many cases the development of the risk calcula- tion algorithms and methods themselves must also take into account the risk technology to take full advantage of the computer resources such as processors and memory.

Risk analytics or risk calculation capabilities 2. Risk infrastructure capabilities 3. Indeed, constructing or implement- ing a new risk system is not a trivial task. Banks often face many competing challenges, such as a balanced decision weighing the trade-off between cost and performance, short-term and long-term needs, regulatory and internal compliance, security and new technology, and risk and inance reconciliation. Risk Analytics A risk system can be designed to only calculate for example market risk or can be designed more comprehensively to be able to calculate risk across many risk types.

In general, a inan- cial risk system implements a risk calculation low where at each step in the low risk analysts can assign or create new methods or models that are used in the risk analysis. Examples include pricing functions, market simulation models, and credit risk evaluation models. Many modern risk systems are designed to be comprehensive in the analysis of inancial risk.

That is, cover all the inancial risks of a position that can come from market, credit, and liquidity risks. To accommodate the required risk analytics the risk calculation low follows a few core sequential steps: 1. The calibration of risk factor models and joint scenario generation from risk factor models such as models for equity prices, interest rates, credit drivers, behavioral risk factors, etc.

The scenario generation also includes the creation of ad-hoc stress scenarios. The transformation of risk factors used in scenario models or ad-hoc stress scenarios to actual risk factors used for pricing and other risk calculations. For example, transforma- tion of bond price and yield curve risk factors to zero-coupon curves used in pricing of cash low instruments such as swaps, bonds, or loans.

Other examples include currency triangulations for currency pairs that can be derived from existing currency pair scenar- ios, and factor models used for generating comprehensive risk factor scenarios from a core reduced set of macroeconomic risk factor scenarios.

The credit model rating assessment or probability of default calculation on instrument loan , counterparty, or pool level needed to price or assign default and migration losses.

The pricing and cash low generation of the inancial instruments, conditional on rating model assessments and the market risk factors. The pricing and cash low generation of traditional banking book instruments can also depend on behavioral risk factors such as deposit withdrawal behavior and prepayment rates.

Aggregation of pricing and cash lows and computation of relevant measures such as expo- sure. Aggregation examples include counterparty or netting set. The application of collateral agreements or hedge strategies to reduce instrument-level exposures as well as aggregated exposure levels such as at counterparty or netting sets. Here, the collateral or hedge can itself be stochastic and depend on the risk factors and hence be priced in step 4 together with the exposures.

Examples include market risk and liquidity hedging portfolios as well as credit collaterals such as mortgage property collat- eral and other collateral agreements with counterparties. The application of risk and statistical summary measures on proit-and-loss results as well as on cash lows—for example, value at risk or other measures on proit-and-loss or tra- ditional cash low—based asset and liability management measures such as hedged and unhedged interest rate and liquidity gap.

The application of post-aggregation processing measures such as risk-adjusted returns that depend on the computed risk measures. The eight steps in the risk calculation low in a inancial risk system are also illustrated in Figure 1. Of course, in this process we have assumed the necessary portfolio and mar- ket data are prepared for the models and the risk analytics.

This process is not just pure data management but rather a joint effort between data and risk modeling teams to deine a standardized risk data model. Regulatory requirements and the fast evolution of market practices in risk analysis require risk systems to perform more and more risk calculations faster and faster, cover more risk Model calibration and scenario generation Risk factor transformation Counterparty or pool-level credit quality assignment Instrument-level pricing and g cash flow generation Aggregation and measure computation e.

For example, a counterparty in a country is subject to the country risk and a subsidiary is subject to the parent company counterparty risk. Introduction 13 types i.

For example, requirements such as CCAR and EBA stress testing exercises promote banks to analyze irmwide stress results in the context of impacts on projected future income statements and balance sheet statements. There can also be a feedback effect to the risk calculation when management actions are taken based on the projected income and balance sheet statements that affects the portfolios in the next risk analysis horizon.

Risk Infrastructure Apart from the risk analytics and the risk calculation low, a inancial risk system needs a risk infrastructure. The risk infrastructure should both support the current use of the system as well as being able to adapt to future needs.

We approach the requirements on a inancial risk management systems risk infrastructure from a few core principles that the system should support. That is, the system must have access to all the material risk data, by business line, legal entity, asset type, industry, region, and other groupings, as relevant for the risks in question.

The system must also implement the risk models and analytics that are appropriate for the mission of the system, adequate to allow identifying and reporting risk exposures, concentrations, and emerging risks and adaptive to the granularity of the data. The data comprehensiveness should also encompass the multiple conigurations the system is commissioned to do.

Oftentimes a risk system has to be adaptive to multiple conigurations even for a single risk type. Any international accord of risk regulation like Basel accords is subject to local modiications. Sometimes the modiication can be quite signiicant due to the local business practice. In this case institutions have a choice to build different systems for different jurisdictions, which is not ideal for economic and integrity reasons, or have one system that can cater to different conigurations on subset portfolios and economic data for the jurisdictions.

A good example is the Basel II and Basel III regulations; many multinational banks that operate directly or indirectly through subsidiaries in very diverse regions face the challenge of being compliant with different jurisdictions.

These interna- tional holding companies also need to aggregate and reconcile group risk measures to report to the group home regulators. For example, in the development and applications of the credit assessment models in some countries certain private customer information cannot be collected or used in the internal rating models even if the information is generally known as good predictors of the credit quality of customers.

Another example is that the loan tenor can be different in different countries; some can be quite long e. Therefore, the analysis horizon can be of various length. Business calendars also vary across regions that call for different payment convention, pricing, and rate resetting models. Can the data and the system accommodate multiple sets of models, such as the champion and challengers?

Can the data and the system sustain evolving regulations especially during the transition periods when different versions of the regulatory settings have to be accommodated? Obviously accuracy is a key requirement for a risk system. Accuracy does not necessarily mean that every balance sheet and off—balance sheet item has to be evaluated or projected up to a certain precision.

There are many approximation-based risk methodologies still actively used. However, banks must be able justify the validity of any approximation methods within the system. The timeliness principle is related to the fact that risk analysis should relect the current market situation and current portfolios to enable risk decisions to be taken on current risk information.

With the important role of risk management in the value creation process in the banks, the delayed risk analysis is obviously not good for a bank to remain competitive. Meeting timeliness for a market risk application in trading book with frequent portfolio and market changes may require frequent intraday updates of risk while meeting timeliness for a credit risk application on the retail and commercial banking side may require less frequent updates due to relatively slowly changing portfolio compositions and credit conditions of borrowers.

A risk management process is not just about data and analytic results. The governance of the entire risk process is critical. The same requirement also imposes security policies to the system. For example, when a data or risk analyst logs into the system, what kind of data and analysis does the analyst have access to?

Another example is that not all the available data may be at the required quality for risk analytics. Banks usually have to approve the variables whose data can be used in modeling. The governance should also include approval and report submission worklows. When an analyst has a risk result ready the result should be validated and approved before reports are created and distributed.

Transparency is a core principle for a risk system. An analyst must be able to explain what the system does, including the data used, speciic model assumptions, and risk calculations. Introduction 15 That the risk system is transparent is a key requirement for the bank to be able to understand the speciic models and assumptions used in the system and hence to be able to validate them, either by data, expertise judgment, or both.

The scope of the traceability depends on how the user manages the input. That means the system should archive the inputs, including portfolio, market data, coniguration, and models. Traceability also includes lineage. The lineage information can provide useful impact analysis results. The input data or a model parameter change lineage helps to trace which part of results or inter- mediate results would be affected.

Another usage of lineage is to trace a risk result to see how it is attained. Traceability is a prerequisite for reproducibility but the system has to now make sure all inputs are correctly restored and retrievable back to the system in order to reproduce the same input environment required by the result.

Transparency, process, and governance are certainly critical to decision making. A decision must sustain questions, validation, and examination. Traceability, auditability, and reproducibility are obviously directly applicable. Risk-based decision making and performance analysis are in fact the ultimate purpose of risk management. In practice two categories of decision support are expected from a risk system. The irst category of decision support is made almost completely by the system with minimal human intervention.

Such decisions are usually based on well-established models and market practice. Automated market-hedging strategies can fall into this category as well. The second system-generated decision support type is more referential and provides guidance to the decision makers.

Accuracy and timeliness are two critical characteristics of a good decision-making framework. Risk management is a quickly evolving discipline. This is not only because the methodol- ogy and technology change rapidly but is also due to the fast-changing economic and market environment in which banks are running business. The risk system should be adaptive to newly identiied risks, new methodologies, and new reporting requirements. For example, during the Greek debt crisis, global and European banks were demanded to submit their total exposures to Greek counterparties and a few other European countries that had similar debt size concerns to the senior management and regulators.

It turned out to be a dificult task for many banks. This was because the aggregated risk to such a level was never calculated and reported before. If a system cannot support quick extensibility, it will not only induce more operational cost and risk at the time of the new risk analysis requirement but also disable the accuracy and timeliness of the risk analysis. Risk Technology The proper use of the technology is key to inancial risk systems and the actual implementation of the quantitative methods discussed in this book.

However, the irst challenge is to ensure that the algorithm itself, if possible, is not deployed as a sequential calculation. Copy link. Copy Copied. Business Research Methods Notes. Business Research Methods Book. Business Research Methods Syllabus. Business Research Methods Question Paper. Business Research Methods Questions and Answers.