Philippe Jorion - Value at Risk - The New Benchmark for Managing Financial Risk 3rd Ed T. Aguiar Pinheiro. Loading Preview. Sorry, preview is currently. Value at Risk - Philippe Jorion - Ebook download as PDF File .pdf), Text File .txt ) or read book online. VALUE AT RISK: The New Benchmark for. Managing Financial Risk. THIRD EDITION. Answer Key to End-of-Chapter Exercises. PHILIPPE JORION. McGraw- .
|Language:||English, Spanish, German|
|Genre:||Politics & Laws|
|Distribution:||Free* [*Registration Required]|
Request PDF on ResearchGate | On Jan 1, , Philippe Jorion and others published Value at Risk: The New Benchmark for Managing Financial Risk. khadictasmimou.ga: Value at Risk: The New Benchmark for Managing Financial Risk, 3rd Edition (): Philippe Jorion: Books. Editorial Reviews. About the Author. Philippe Jorion is a professor of finance at the University of California, Irvine. Editor in chief of the Journal of Risk, Jorion is a .
Probability estimates are meaningful, because there are enough data to test them.
In a sense, there is no true risk because you have a sum of many independent observations with a left bound on the outcome. A casino doesn't worry about whether red or black will come up on the next roulette spin. Risk managers encourage productive risk-taking in this regime, because there is little true cost. People tend to worry too much about these risks, because they happen frequently, and not enough about what might happen on the worst days.
Risk should be analyzed with stress testing based on long-term and broad market data. The risk manager should concentrate instead on making sure good plans are in place to limit the loss if possible, and to survive the loss if not. You expect periodic VaR breaks. The loss distribution typically has fat tails , and you might get more than one break in a short period of time. Moreover, markets may be abnormal and trading may exacerbate losses, and you may take losses not measured in daily marks such as lawsuits, loss of employee morale and market confidence and impairment of brand names.
So an institution that can't deal with three times VaR losses as routine events probably won't survive long enough to put a VaR system in place. Three to ten times VaR is the range for stress testing. Institutions should be confident they have examined all the foreseeable events that will cause losses in this range, and are prepared to survive them.
Foreseeable events should not cause losses beyond ten times VaR. If they do they should be hedged or insured, or the business plan should be changed to avoid them, or VaR should be increased. It's hard to run a business if foreseeable losses are orders of magnitude larger than very large everyday losses.
It's hard to plan for these events, because they are out of scale with daily experience. Of course there will be unforeseeable losses more than ten times VaR, but it's pointless to anticipate them, you can't know much about them and it results in needless worrying. Better to hope that the discipline of preparing for all foreseeable three-to-ten times VaR losses will improve chances for surviving the unforeseen and larger losses that inevitably occur. VaR is the border.
Within any portfolio it is also possible to isolate specific position that might better hedge the portfolio to reduce, and minimise, the VaR. An example of market-maker employed strategies for trading linear interest rate derivatives and interest rate swaps portfolios is cited. Backtesting[ edit ] A key advantage to VaR over most other measures of risk such as expected shortfall is the availability several backtesting procedures for validating a set of VaR forecasts.
Early examples of backtests can be found in Christoffersen ,  later generalized by Pajhede ,  which models a "hit-sequence" of losses greater than the VaR and proceed to tests for these "hits" to be independent from one another and with a correct probability of occurring.
A number of other backtests are available which model the time between hits in the hit-sequence, see Christoffersen ,  Haas ,  Tokpavi et al. Backtest toolboxes are available in Matlab  , or R —though only the first implements the parametric bootstrap method. History[ edit ] The problem of risk measurement is an old one in statistics , economics and finance.
Financial risk management has been a concern of regulators and financial executives for a long time as well. Retrospective analysis has found some VaR-like concepts in this history. But VaR did not emerge as a distinct concept until the late s. The triggering event was the stock market crash of This was the first major financial crisis in which a lot of academically-trained quants were in high enough positions to worry about firm-wide survival.
A reconsideration of history led some quants to decide there were recurring crises, about one or two per decade, that overwhelmed the statistical assumptions embedded in models used for trading , investment management and derivative pricing. These affected many markets at once, including ones that were usually not correlated , and seldom had discernible economic cause or warning although after-the-fact explanations were plentiful. If these events were excluded, the profits made in between "Black Swans" could be much smaller than the losses suffered in the crisis.
Institutions could fail as a result. It was hoped that "Black Swans" would be preceded by increases in estimated VaR or increased frequency of VaR breaks, in at least some markets.
The extent to which this has proven to be true is controversial. It was well established in quantitative trading groups at several financial institutions, notably Bankers Trust , before , although neither the name nor the definition had been standardized.
There was no effort to aggregate VaRs across trading desks. Since many trading desks already computed risk management VaR, and it was the only common risk measure that could be both defined for all businesses and aggregated without strong assumptions, it was the natural choice for reporting firmwide risk.
Morgan CEO Dennis Weatherstone famously called for a " report" that combined all firm risk on one page, available within 15 minutes of the market close. Development was most extensive at J. Morgan , which published the methodology and gave free access to estimates of the necessary underlying parameters in This was the first time VaR had been exposed beyond a relatively small group of quants. Securities and Exchange Commission ruled that public corporations must disclose quantitative information about their derivatives activity.
The web site contains other materials, including additional questions that course instructors can assign to their students. Jorion leaves no stone unturned, addressing the building blocks of VAR from computing and backtesting models to forecasting risk and correlations. He outlines the use of VAR to measure and control risk for trading, for investment management, and for enterprise-wide risk management. He also points out key pitfalls to watch out for in risk-management systems.
The value-at-risk approach continues to improve worldwide standards for managing numerous types of risk. Now more than ever, professionals can depend on Value at Risk for comprehensive, authoritative counsel on VAR, its application, and its results-and to keep ahead of the curve.
Other books in this series. The Accelerated Learning Handbook: Add to basket. Investing Between the Lines: When Markets Collide: Lean Six Sigma Michael L.
Investing the Templeton Way: The Leader's Handbook: Micro-Trend Trading for Daily Income: Statistics for Six Sigma Made Easy!
The Six Sigma Way: The Investopedia Guide to Wall Speak: Monster Stocks: Social Entrepreneurship for the 21st Century: Among his previous books is Financial Risk Management: Domestic and International Dimensions. Rating details. Book ratings by Goodreads.
Goodreads is the world's largest site for readers with over 50 million reviews.
We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Close X. Follow us. Please enter manually: