2,344 research outputs found

    The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

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    Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables\u2019 relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles

    Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility

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    This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of integrated variance, upside and downside semivariance, and jump variation. Our results for the S&P 500 and WTI Crude Oil futures contracts show that simple linear quantile regressions for returns and heterogenous quantile autoregressions for realized volatility perform very well in capturing the dynamics of the respective conditional distributions, both in absolute terms as well as relative to a couple of well-established benchmark models. The models can therefore serve as useful risk management tools for investors trading the futures contracts themselves or various derivative contracts written on realized volatility

    Are realized volatility models good candidates for alternative Value at Risk prediction strategies?

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    In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with realized volatility regressors. The α-th quantile of the innovation’s distribution is estimated with the fully parametric method using either the normal or the skewed student distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is based on two S&P 500 cash index out-of-sample forecasting periods, one of which covers exclusively the recent 2007-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the realized volatility and the augmented GARCH models with the FHS or the EVT quantile estimation methods produce superior VaR forecasts and allow for more efficient regulatory capital allocations. The skewed student distribution is also an attractive alternative, especially during periods of high market volatility.High frequency intraday data; Filtered Historical Simulation; Extreme Value Theory; Value-at-Risk forecasting; Financial crisis.

    The volatility of realized volatility

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    Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing "observable" or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. Klassifikation: C22, C51, C52, C5

    Moment Risk Premiums in Option Markets: On Measurement, Structure, and Investment Implications

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    Rationale Anleger sind im Allgemeinen risikoavers. Eine wichtige Implikation dieser Risikoaversion ist, dass Anleger für bestimmte Risiken, die sie eingehen, eine Kompensation verlangen – Risikoprämien. Ein Beispiel für solche Risikoprämien sind Momentenrisikoprämien. Sie sind definiert als die Differenz zwischen einem bestimmten statistischen Moment der risikoneutralen Renditeverteilung und dem entsprechenden Moment der physischen Renditeverteilung. Ziel dieser Dissertation ist es, die Messung, die Struktur und die Investment-Implikationen von Momentenrisikoprämien auf Optionsmärkte zu untersuchen. Im Hinblick auf die Messung von Momentenrisikoprämien untersucht die Dissertation quantilsbasierte Alternativen zu traditionellen Momentenswaps. Dabei wird gezeigt, wie diese Prämien auf robuste und flexible Weise gemessen und quantifiziert werden können. Die Struktur von Momentenrisikoprämien wird analysiert, indem sie in Downside- und Upside-Prämien zerlegt werden. Darüber hinaus werden die Prämien auf ihre Vorhersagekraft für Überrenditen des Marktes untersucht. Schließlich werden die Investment-Implikationen untersucht, indem verschiedene Strategien vorgeschlagen und analysiert werden, die potenziell geeignet sind, eine bestimmte Momentenrisikoprämie – die Varianzrisikoprämie – zu verdienen. Die wichtigsten Ergebnisse dieser Arbeit lassen sich wie folgt zusammenfassen: (i) Die Dissertation findet eine neuartige Risikoprämie, die mit Asymmetrie der Renditeverteilung assoziiert ist, wenn Momentenrisikoprämien mit einem quantilsbasierten Ansatz gemessen werden, (ii) traditionelle Momentenrisikoprämien sind von Tail-Extremität getrieben und müssen vorsichtig interpretiert werden, (iii) Momentenrisikoprämien auf der Abwärtsseite übersteigen Momentenrisikoprämien auf der Aufwärtsseite und enthalten Informationen über zukünftig realisierte Marktrisikoprämien, (iv) die Varianzrisikoprämie kann zur Kapitalakkumulation genutzt werden, (v) und Handelsstrategien, die auf der Varianzrisikoprämie basieren, erfordern durchdachte Designentscheidungen, da ihre Performance entscheidend von einem geeigneten Design abhängt. Die Dissertation und ihre Ergebnisse sind sowohl für Forscher als auch für Praktiker auf dem Gebiet des (empirischen) Asset Pricing sowie des Vermögens- und Risikomanagements relevant.Rational investors are in general risk averse. An important implication of this risk aversion is that investors may demand compensation for certain risks they take – risk premiums. Moment risk premiums are an example of such risk premiums. They are defined as the difference between a particular statistical moment of the risk-neutral return distribution and the corresponding moment of the physical return distribution. It is the goal of this dissertation to study the measurement, structure, and investment implications of moment risk premiums in option markets. With respect to the measurement of moment risk premiums, the dissertation investigates quantile-based alternatives to traditional moment swaps. Thereby it shows how to measure and quantify these premiums in a robust and flexible manner. The structure of moment risk premiums is investigated by decomposing them into downside and upside premiums. Additionally, the premiums are analyzed regarding their predictive power for subsequent market excess returns. Lastly, the investment implications are studied by proposing and assessing various strategies that are potentially suitable to harvest a particular moment risk premium – the variance risk premium. The core findings of this thesis can be condensed as follows: (i) The dissertation finds a novel risk premium associated with asymmetry when moment risk premiums are measured with a quantile-based approach, (ii) traditional moment risk premiums are driven by tail extremity and need to be interpreted carefully, (iii) downside moment risk premiums exceed upside moment risk premiums and carry information about subsequently realized market risk premiums, (iv) the variance risk premium can be exploited to accumulate capital, (v) and trading strategies based on the variance risk premium require thoughtful design decisions since their performances critically depend on a proper design. The dissertation and its contributions are relevant for both researchers and practitioners in the fields of (empirical) asset pricing as well as asset and risk management.2021-11-2

    Estimating an NBA player's impact on his team's chances of winning

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    Traditional NBA player evaluation metrics are based on scoring differential or some pace-adjusted linear combination of box score statistics like points, rebounds, assists, etc. These measures treat performances with the outcome of the game still in question (e.g. tie score with five minutes left) in exactly the same way as they treat performances with the outcome virtually decided (e.g. when one team leads by 30 points with one minute left). Because they ignore the context in which players perform, these measures can result in misleading estimates of how players help their teams win. We instead use a win probability framework for evaluating the impact NBA players have on their teams' chances of winning. We propose a Bayesian linear regression model to estimate an individual player's impact, after controlling for the other players on the court. We introduce several posterior summaries to derive rank-orderings of players within their team and across the league. This allows us to identify highly paid players with low impact relative to their teammates, as well as players whose high impact is not captured by existing metrics.Comment: To appear in the Journal of Quantitative Analysis of Spor

    The Volatility of Realized Volatility

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    Using unobservable conditional variance as measure, latent–variable approaches, such as GARCH and stochastic–volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high–frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing “observable” or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time–series models for realized volatility exhibit non–Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time–varying volatility of realized volatility leads to a substantial improvement of the model’s fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting.Finance, Realized Volatility, Realized Quarticity, GARCH, Normal Inverse Gaussian Distribution, Density Forecasting

    Inference in Predictive Quantile Regressions

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    This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability with persistent regressors. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has reliable size in small samples and particularly good power when the predictor is persistent and endogenous, i.e., when the predictive regression problem is most acute. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors - the dividend price ratio, earnings price ratio, and book to market ratio - to predict the median, shoulders, and tails of the stock return distribution
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