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.

This book adds to the resolution of two problems in finance and economics: i) what is macro-financial uncertainty? : How to measure it? How is it different from risk? How important is it for the financial markets? And ii) what sort of asymmetries underlie financial risk and uncertainty propagation across the global financial markets? That is, how risk and uncertainty change according to factors such as market states or market participants. In Chapter 2, which is entitled “Momentum Uncertainties”, the relationship between macroeconomic uncertainty and the abnormal returns of a momentum trading strategy in the stock market is studies. We show that high levels of uncertainty in the economy impact negatively and significantly the returns of a portfolio of stocks that consist of buying past winners and selling past losers. High uncertainty reduces below zero the abnormal returns of momentum, extinguishes the Sharpe ratio of the momentum strategy, while increases the probability of momentum crashes both by increasing the skewness and the kurtosis of the momentum return distribution. Uncertainty acts as an economic regime that underlies abrupt changes over time of the returns generated by momentum strategies. In Chapter 3, “Measuring Uncertainty in the Stock Market”, a new index for measuring stock market uncertainty on a daily basis is proposed. The index considers the inherent differentiation between uncertainty and the common variations between the series. The second contribution of chapter 3 is to show how this financial uncertainty index can also serve as an indicator of macroeconomic uncertainty. Finally, the dynamic relationship between uncertainty and the series of consumption, interest rates, production and stock market prices, among others, is analized. In chapter 4: “Uncertainty, Systemic Shocks and the Global Banking Sector: Has the Crisis Modified their Relationship?” we explore the stability of systemic risk and uncertainty propagation among financial institutions in the global economy, and show that it has remained stable over the last decade. Additionally, a new simple tool for measuring the resilience of financial institutions to these systemic shocks is provided. We examine the characteristics and stability of systemic risk and uncertainty, in relation to the dynamics of the banking sector stock returns. This sort of evidence is supportive of past claims, made in the field of macroeconomics, which hold that during the global financial crisis the financial system may have faced stronger versions of traditional shocks rather than a new type of shock. In chapter 5, “Currency downside risk, liquidity, and financial stability”, downside risk propagation across global currency markets and the ways in which it is related to liquidity is analyzed. Two primary contributions to the literature follow. First, tail-spillovers between currencies in the global FX market are estimated. This index is easy to build and does not require intraday data, which constitutes an important advantage. Second, we show that turnover is related to risk spillovers in global currency markets. Chapter 6 is entitled “Spillovers from the United States to Latin American and G7 Stock Markets: A VAR-Quantile Analysis”. This chapter contributes to the studies of contagion, market integration and cross-border spillovers during both regular and crisis episodes by carrying out a multivariate quantile analysis. It focuses on Latin American stock markets, which have been characterized by a highly positive dynamic in recent decades, in terms of market capitalization and liquidity ratios, after a far-reaching process of market liberalization and reforms to pension funds across the continent during the 80s and 90s. We document smaller dependences between the LA markets and the US market than those between the US and the developed economies, especially in the highest and lowest quantiles.

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.