"Does extreme downside risk require a risk premium in the pricing of individual assets? Extreme downside risk is a conditional measure for the co-movement of individual stocks with the market, given that the state of the world is extremely bad. This measure, derivedfrom statistical extreme value theory, is non-parametric. Extreme down-side risk is used in double-sorted portfolios, where I control for the five Fama-French and various non-linear asset pricing factors. I find that the average annual excess return between high- and low-exposure stocks is around 3.5%"--Abstract.

We propose a measure for extreme downside risk (EDR) to investigate whether bearing such a risk is rewarded by higher expected stock returns. Constructing an EDR proxy with the left tail index in the classical generalized extreme value distribution, we document a significantly positive premium on firm-specific EDR in cross-section of stock returns even after controlling for market, size, value, momentum, and liquidity effects. The EDR premium is more prominent among glamour stocks and when high market returns are expected. High-EDR stocks generally have high idiosyncratic risk, large downside beta, lower coskewness and cokurtosis, and high bankruptcy risk. The EDR premium persists after these characteristics are controlled for. EDR is also closely related to firm-specific Value at Risk (VaR) which substantially impacts EDR's effect on expected stock returns. EDR supplements VaR in predicting stock returns by exhibiting additional explanatory power.

Inhaltsangabe:Abstract: Investors should not and in fact do not hold a single asset, they hold groups or portfolios of assets. An important aspect in portfolio theory is that the risk of a portfolio is more complex than the risk of its components. It depends on how much the assets represented in the portfolio move together, that is, on the correlation between the single assets. In portfolio theory, there are several definitions of risk: First of all, the Capital Asset Pricing Model (CAPM) relies on the beta factor of an asset relative to the market as a measure for the asset s risk. On the other hand, also downside risk can be used in order to determine a portfolio s risk. The kind of risk in question is market risk, which is the risk of losses arising from adverse movements in market prices or rates. Market risk can be subdivided into interest rate risk, equity price risk, exchange rate risk and commodity price risk. For many investment decisions, there is a minimum return that has to be reached in order to meet different criteria. Returns above this minimum acceptable return ensure that these goals are reached and thus are not considered risky. Standard deviation captures the risk associated with achieving the mean, while downside risk assumes that only those returns that fall below the minimal acceptable return incur risk. One has to distinguish between good and bad volatility. Good volatility is dispersion above the minimal acceptable return, the farther above the minimal acceptable return, the better it is. One way of measuring downside risk is to consider the shortfall probability or chances of falling below the minimal acceptable return. Another possibility is measuring downside variance, i.e. variance of the returns falling below the minimal acceptable return. As a consequence, downside variance is very sensitive to the estimate of the mean of the return function, while standard deviation does not suffer from this problem. Thus the calculation of downside deviation is more difficult than the calculation of standard deviation. The quality of the calculation also depends on the choice of differencing interval of the time series. The calculation of downside risk assumes that financial time series follow either a normal or lognormal distribution. Finally, there is no universal risk measure for the many broad categories of risk. For example, standard deviation captures the risk of not achieving the mean, beta captures the risk of investing [...]

Quantitative methods have revolutionized the area of trading, regulation, risk management, portfolio construction, asset pricing and treasury activities, and governmental activity such as central banking to name but some of the applications. Downside-risk, as a quantitative method, is an accurate measurement of investment risk, because it captures the risk of not accomplishing the investor's goal. 'Downside Risk in Financial Markets' demonstrates how downside-risk can produce better results in performance measurement and asset allocation than variance modelling. Theory, as well as the practical issues involved in its implementation, is covered and the arguments put forward emphatically show the superiority of downside risk models to variance models in terms of risk measurement and decision making. Variance considers all uncertainty to be risky. Downside-risk only considers returns below that needed to accomplish the investor's goal, to be risky. Risk is one of the biggest issues facing the financial markets today. 'Downside Risk in Financial Markets' outlines the major issues for Investment Managers and focuses on "downside-risk" as a key activity in managing risk in investment/portfolio management. Managing risk is now THE paramount topic within the financial sector and recurring losses through the 1990s has shocked financial institutions into placing much greater emphasis on risk management and control. Free Software Enclosed To help you implement the knowledge you will gain from reading this book, a CD is enclosed that contains free software programs that were previously only available to institutional investors under special licensing agreement to The pension Research Institute. This is our contribution to the advancement of professionalism in portfolio management. The Forsey-Sortino model is an executable program that: 1. Runs on any PC without the need of any additional software. 2. Uses the bootstrap procedure developed by Dr. Bradley Effron at Stanford University to uncover what could have happened, instead of relying only on what did happen in the past. This is the best procedure we know of for describing the nature of uncertainty in financial markets. 3. Fits a three parameter lognormal distribution to the bootstrapped data to allow downside risk to be calculated from a continuous distribution. This improves the efficacy of the downside risk estimates. 4. Calculates upside potential and downside risk from monthly returns on any portfolio manager. 5. Calculates upside potential and downside risk from any user defined distribution. Forsey-Sortino Source Code: 1. The source code, written in Visual Basic 5.0, is provided for institutional investors who want to add these calculations to their existing financial services. 2. No royalties are required for this source code, providing institutions inform clients of the source of these calculations. A growing number of services are now calculating downside risk in a manner that we are not comfortable with. Therefore, we want investors to know when downside risk and upside potential are calculated in accordance with the methodology described in this book. Riddles Spreadsheet: 1. Neil Riddles, former Senior Vice President and Director of Performance Analysis at Templeton Global Advisors, now COO at Hansberger Global Advisors Inc., offers a free spreadsheet in excel format. 2. The spreadsheet calculates downside risk and upside potential relative to the returns on an index Brings together a range of relevant material, not currently available in a single volume source. Provides practical information on how financial organisations can use downside risk techniques and technological developments to effectively manage risk in their portfolio management. Provides a rigorous theoretical underpinning for the use of downside risk techniques. This is important for the long-run acceptance of the methodology, since such arguments justify consultant's recommendations to pension funds and other plan sponsors.

In Part I of this dissertation, I propose a measure for the extreme downside risk (EDR) to investigate whether bearing such a risk can be rewarded by higher expected stock returns. By constructing an EDR measure with the left tail index in the classical generalized extreme value distribution, I find a significant positive premium on firm-specific EDR in cross-section of stock returns even after I control for size, value, return reversal, momentum, and liquidity factors. EDR serves as a good indicator of extreme market plunges. High-EDR stocks generally exhibit high idiosyncratic volatility, large value-at-risk, large negative co-skewness, and high bankruptcy risk. I also controlled for these characteristics to find that the EDR premium remains robust. Furthermore, the EDR effect exhibits long-run persistence and is not subsumed by business cycles. In Part II, I apply the concept of extreme downside risk to a policy-related issue: In August 1991, NASDAQ introduced a controversial SI minimum bid price threshold as part of its listing maintenance criteria (the dollar delisting rule or one-dollar rule). This part empirically evaluates this rule through an extreme value approach. Utilizing the Generalized Extreme Value distribution model to capture extreme price plummets, I find NASDAQ stocks frequently trading below S1 in the pre-rule period are extremely vulnerable to catastrophic losses. The implementation of the one-dollar rule effectively curbs the extreme downside price movements, which helps to protect investors' interest, uphold their faith in the exchange, and improve the credibility of the market. Such a pattern is prevalent across all industries and is not affected by market movements. The S1 benchmark serves as an appropriate cutoff point in screening the issues listed on the exchange. The minimum price continued listing standard on NASDAQ is justified and has proved to be successful.