Nonparametric tail risk, macroeconomics and stock returns: predictability and risk premia

This paper proposes a new novel to calculate tail risks incorporating risk-neutral information without dependence on options data. Proceeding via a non parametric approach we derive a stochastic discount factor that correctly price a chosen panel of stocks returns. With the assumption that states probabilities are homogeneous we back out the risk neutral distribution and calculate five primitive tail risk measures, all extracted from this risk neutral probability. The final measure is than set as the first principal component of the preliminary measures. Using six Fama-French size and book to market portfolios to calculate our tail risk, we find that it has significant predictive power when forecasting market returns one month ahead, aggregate U.S. consumption and GDP one quarter ahead and also macroeconomic activity indexes. Conditional Fama-Macbeth two-pass cross-sectional regressions reveal that our factor present a positive risk premium when controlling for traditional factors.

Tail risk in the hedge fund industry

The dissertation goal is to quantify the tail risk premium embedded into hedge funds' returns. Tail risk is the probability of extreme large losses. Although it is a rare event, asset pricing theory suggests that investors demand compensation for holding assets sensitive to extreme market downturns. By de nition, such events have a small likelihood to be represented in the sample, what poses a challenge to estimate the e ects of tail risk by means of traditional approaches such as VaR. The results show that it is not su cient to account for the tail risk stemming from equities markets. Active portfolio management employed by hedge funds demand a speci c measure to estimate and control tail risk. Our proposed factor lls that void inasmuch it presents explanatory power both over the time series as well as the cross-section of funds' returns.

An SDF approach to hedge funds’ tail risk: evidence from Brazilian funds

The main purpose of this paper is to propose a methodology to obtain a hedge fund tail risk measure. Our measure builds on the methodologies proposed by Almeida and Garcia (2015) and Almeida, Ardison, Garcia, and Vicente (2016), which rely in solving dual minimization problems of Cressie Read discrepancy functions in spaces of probability measures. Due to the recently documented robustness of the Hellinger estimator (Kitamura et al., 2013), we adopt within the Cressie Read family, this specific discrepancy as loss function. From this choice, we derive a minimum Hellinger risk-neutral measure that correctly prices an observed panel of hedge fund returns. The estimated risk-neutral measure is used to construct our tail risk measure by pricing synthetic out-of-the-money put options on hedge fund returns of ten specific categories. We provide a detailed description of our methodology, extract the aggregate Tail risk hedge fund factor for Brazilian funds, and as a by product, a set of individual Tail risk factors for each specific hedge fund category.

Modeling Dependence Structure and Forecasting Portfolio Value-at-Risk with Dynamic Copulas

We study the asymmetric and dynamic dependence between financial assets and demonstrate, from the perspective of risk management, the economic significance of dynamic copula models. First, we construct stock and currency portfolios sorted on different characteristics (ex ante beta, coskewness, cokurtosis and order flows), and find substantial evidence of dynamic evolution between the high beta (respectively, coskewness, cokurtosis and order flow) portfolios and the low beta (coskewness, cokurtosis and order flow) portfolios. Second, using three different dependence measures, we show the presence of asymmetric dependence between these characteristic-sorted portfolios. Third, we use a dynamic copula framework based on Creal et al. (2013) and Patton (2012) to forecast the portfolio Value-at-Risk of long-short (high minus low) equity and FX portfolios. We use several widely used univariate and multivariate VaR models for the purpose of comparison. Backtesting our methodology, we find that the asymmetric dynamic copula models provide more accurate forecasts, in general, and, in particular, perform much better during the recent financial crises, indicating the economic significance of incorporating dynamic and asymmetric dependence in risk management.

Risk Parity: A Modern Tool for Diversification

The purpose of this study is to examine how well risk parity works in terms of risk, return and diversification relative to more traditional minimum variance, 1/N and 60/40 portfolios. Risk parity portfolios were constituted of five risk sources; three common asset classes and two alternative beta investment strategies. The three common asset classes were equities, bonds and commodities, and the alternative beta investment strategies were carry trade and trend following. Risk parity portfolios were constructed using five different risk measures of which four were tail risk measures. The risk measures were standard deviation, Value-at-Risk, Expected Shortfall, modified Value-at-Risk and modified Expected Shortfall. We studied also how sensitive risk parity is to the choice of risk measure. The hypothesis is that risk parity portfolios provide better return with the same amount of risk and are better diversified than the benchmark portfolios. We used two data sets, monthly and weekly data. The monthly data was from the years 1989-2011 and the weekly data was from the years 2000-2011. Empirical studies showed that risk parity portfolios provide better diversification since the diversification is made at the risk level. Risk based portfolios provided superior return compared to the asset based portfolios. Using tail risk measures in risk parity portfolios do not necessarily provide better hedge from tail events than standard deviation.

Measuring Tail Risks at High Frequency

I develop a new methodology for measuring tail risks using the cross section of bid-ask spreads. Market makers embed tail risk information into spreads because (1) they lose to arbitrageurs when changes to asset values exceed the cost of liquidity and (2) underlying price movements and potential costs are linear in factor loadings. Using this insight, simple cross-sectional regressions relating spreads and trading volume to factor betas can recover tail risks in real time for priced or non-priced return factors. The methodology disentangles financial and aggregate market risks during the 2007-2008 Financial Crisis; anticipates jump risks associated with Federal Open Market Committee announcements; and quantifies a sharp, temporary increase in market tail risk before and throughout the 2010 Flash Crash. The recovered time series of implied market risks also aligns closely with both realized market jumps and the VIX.

Joint value at risk: a new conditional risk measure

In this PhD thesis a new firm level conditional risk measure is developed. It is named Joint Value at Risk (JVaR) and is defined as a quantile of a conditional distribution of interest, where the conditioning event is a latent upper tail event. It addresses the problem of how risk changes under extreme volatility scenarios. The properties of JVaR are studied based on a stochastic volatility representation of the underlying process. We prove that JVaR is leverage consistent, i.e. it is an increasing function of the dependence parameter in the stochastic representation. A feasible class of nonparametric M-estimators is introduced by exploiting the elicitability of quantiles and the stochastic ordering theory. Consistency and asymptotic normality of the two stage M-estimator are derived, and a simulation study is reported to illustrate its finite-sample properties. Parametric estimation methods are also discussed. The relation with the VaR is exploited to introduce a volatility contribution measure, and a tail risk measure is also proposed. The analysis of the dynamic JVaR is presented based on asymmetric stochastic volatility models. Empirical results with S&P500 data show that accounting for extreme volatility levels is relevant to better characterize the evolution of risk. The work is complemented by a review of the literature, where we provide an overview on quantile risk measures, elicitable functionals and several stochastic orderings.

Global Warming And Extreme Events: Rethinking The Timing And Intensity Of Environmental Policy

The possibility of low-probability extreme events has reignited the debate over the optimal intensity and timing of climate policy. In this paper we therefore contribute to the literature by assessing the implications of low-probability extreme events on environmental policy in a continuous-time real options model with “tail risk”. In a nutshell, our results indicate the importance of tail risk and call for foresighted pre-emptive climate policies.

Global Warming and Fat Tailed-uncertainty: Rethinking the Timing and Intensity of Climate Policy

The possibility of low-probability extreme natural events has reignited the debate over the optimal intensity and timing of climate policy. In this paper, we contribute to the literature by assessing the implications of low-probability extreme events on environmental policy in a continuous-time real options model with “tail risk”. In a nutshell, our results indicate the importance of tail risk and call for foresighted pre-emptive climate policies.

On the impact of fundamentals, liquidity and coordination on market stability

We develop a coordination game to model interactions betweenfundamentals and liquidity during unstable periods in financial markets.We then propose a flexible econometric framework for estimationof the model and analysis of its quantitative implications. The specificempirical application is carry trades in the yen dollar market, includingthe turmoil of 1998. We find a generally very deep market, withlow information disparities amongst agents. We observe occasionallyepisodes of market fragility, or turmoil with up by the escalator, downby the elevator patterns in prices. The key role of strategic behaviorin the econometric model is also confirmed.

Gold and the Stock Market: 3 Essays on Gold Investments

This thesis gives an overview of the history of gold per se, of gold as an investment good and offers some institutional details about gold and other precious metal markets. The goal of this study is to investigate the role of gold as a store of value and hedge against negative market movements in turbulent times. I investigate gold’s ability to act as a safe haven during periods of financial stress by employing instrumental variable techniques that allow for time varying conditional covariance. I find broad evidence supporting the view that gold acts as an anchor of stability during market downturns. During periods of high uncertainty and low stock market returns, gold tends to have higher than average excess returns. The effectiveness of gold as a safe haven is enhanced during periods of extreme crises: the largest peaks are observed during the global financial crises of 2007-2009 and, in particular, during the Lehman default (October 2008). A further goal of this thesis is to investigate whether gold provides protection from tail risk. I address the issue of asymmetric precious metal behavior conditioned to stock market performance and provide empirical evidence about the contribution of gold to a portfolio’s systematic skewness and kurtosis. I find that gold has positive coskewness with the market portfolio when the market is skewed to the left. Moreover, gold shows low cokurtosis with the market returns during volatile periods. I therefore show that gold is a desirable investment good to risk averse investors, since it tends to decrease the probability of experiencing extreme bad outcomes, and the magnitude of losses in case such events occur. Gold thus bears very important and under-researched characteristics as an asset class per se, which this thesis contributed to address and unveil.

Modelos de risco de mercado com fat tail : análise empírica de value at risk and expected shortfall para ativos financeiros brasileiros

O objetivo deste trabalho foi mostrar modelagens alternativas à tradicional maneira de se apurar o risco de mercado para ativos financeiros brasileiros. Procurou-se cobrir o máximo possível de fatores de risco existentes no Brasil; para tanto utilizamos as principais proxies para instrumentos de Renda Fixa. Em momentos de volatilidade, o gerenciamento de risco de mercado é bastante criticado por trabalhar dentro de modelagens fundamentadas na distribuição normal. Aqui reside a maior contribuição do VaR e também a maior crítica a ele. Adicionado a isso, temos um mercado caracterizado pela extrema iliquidez no mercado secundário até mesmo em certos tipos de títulos públicos federais. O primeiro passo foi fazer um levantamento da produção acadêmica sobre o tema, seja no Brasil ou no mundo. Para a nossa surpresa, pouco, no nosso país, tem se falado em distribuições estáveis aplicadas ao mercado financeiro, seja em gerenciamento de risco, precificação de opções ou administração de carteiras. Após essa etapa, passamos a seleção das variáveis a serem utilizadas buscando cobrir uma grande parte dos ativos financeiros brasileiros. Assim, deveríamos identificar a presença ou não da condição de normalidade para, aí sim, realizarmos as modelagens das medidas de risco, VaR e ES, para os ativos escolhidos, As condições teóricas e práticas estavam criadas: demanda de mercado (crítica ao método gausiano bastante difundido), ampla cobertura de ativos (apesar do eventual questionamento da liquidez), experiência acadêmica e conhecimento internacional (por meio de detalhado e criterioso estudo da produção sobre o tema nos principais meios). Analisou-se, desta forma, quatro principais abordagens para o cálculo de medidas de risco sendo elas coerentes (ES) ou não (VaR). É importante mencionar que se trata de um trabalho que poderá servir de insumo inicial para trabalhos mais grandiosos, por exemplo, aqueles que incorporarem vários ativos dentro de uma carteira de riscos lineares ou, até mesmo, para ativos que apresentem risco não-direcionais.

Assessment of Occupational Genotoxic Risk among Brazilian Hairdressers

Objectives: To evaluate the genotoxic risk to hairdressers exposed daily to chemical substances such as hair dyes, waving and straightening preparations and manicurists` products by the Comet assay test (single-cell gel electrophoresis). Methods: The Comet assay was performed on blood samples from 69 female hairdressers (36.4 +/- 10.7 years old) currently employed in 21 different beauty institutes in Sao Paulo, Brazil, and on 55 female control blood donors (32.6 +/- 10.0 years old) from the Sao Paulo University Clinical Hospital blood bank. All the control subjects had occupations other than hairdresser. Comet assays were performed by evaluating 100 blood lymphocytes per individual and graded by visual score according to comet tail length. Results: The hairdressers showed a higher frequency of DNA damage revealed by Comet Score (159.8 +/- 71) when compared to the control group (125.4 +/- 64.1), and the difference was statistically significant by the Student`s t-test (P = 0.005). Multiple regression analysis showed that in addition to the hairdressers` profession, tobacco use contributed to the higher frequency of cells with comets (P < 0.05). Conclusions: The observed DNA damage could be associated with the hairdressers` occupational environment, where different chemicals are chronically manipulated and inhaled. Considering that this profession in many countries, including Brazil, is not officially regulated, more attention should focus on these professionals not only by legislative bodies but also by multidisciplinary teams able to develop and implement risk prevention and control strategies for chemical, physical and biological agents to which hairdressers are exposed.