Auswahl der wissenschaftlichen Literatur zum Thema „VOLATILITY MEASUREMENT“

Risk-averse investors will seek out stock investments with the minimum risk. One step that can be taken is to develop a model of stock prices and predict their fluctuations in the coming months. Significant studies on the modeling of stock movements have used the ARCH/GARCH method, but this method requires some assumptions. This paper will discuss the performance of stock modeling using Support Vector Regression. The performance is measured using the root mean square error value in two stock clusters based on its volatility value, e.g., stocks with large volatility and stocks with small volatility. This case study makes use of daily closing price data from 10 LQ-45 index shares from October 12, 2018 to October 11, 2019. In conclusion, SVR's performance on stocks with high volatility produces RMSE, which is considerably higher than SVR's performance on stocks with low volatility.

Abstract. Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate leaving the less volatile behind in the particulate phase. The typical result of a~thermodenuder measurement is the mass fraction remaining (MFR), which depends among other factors on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model and show that it is suitable for studies of OA volatility in the lab and the field.

Abstract. A variable residence time thermodenuder (TD) was combined with an Aerodyne Aerosol Mass Spectrometer (AMS) and a Scanning Mobility Particle Sizer (SMPS) to measure the volatility distribution of aged organic aerosol in the Eastern Mediterranean during the Finokalia Aerosol Measurement Experiment in May of 2008 (FAME-2008). A new method for the quantification of the organic aerosol volatility distribution was developed combining measurements of all three instruments together with an aerosol dynamics model. Challenges in the interpretation of ambient thermodenuder-AMS measurements include the potential resistances to mass transfer during particle evaporation, the effects of particle size on the evaporated mass fraction, the changes in the AMS collection efficiency and particle density as the particles evaporate partially in the TD, and finally potential losses inside the TD. Our proposed measurement and data analysis method accounts for all of these problems combining the AMS and SMPS measurements. The AMS collection efficiency of the aerosol that passed through the TD was found to be approximately 10% lower than the collection efficiency of the aerosol that passed through the bypass. The organic aerosol measured at Finokalia is approximately 2 orders of magnitude less volatile than fresh laboratory-generated biogenic secondary organic aerosol. This low volatility is consistent with its highly oxygenated AMS mass spectrum. The results are found to be highly sensitive to the mass accommodation coefficient of the evaporating species.

Abstract. Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate, leaving the less volatile components behind in the particulate phase. The typical result of a thermodenuder measurement is the mass fraction remaining (MFR), which depends, among other factors, on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error, and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model, and show that it is suitable for studies of OA volatility in the lab and the field.

Abstract. A variable residence time thermodenuder (TD) was combined with an Aerodyne Aerosol Mass Spectrometer (AMS) and a Scanning Mobility Particle Sizer (SMPS) to measure the volatility distribution of aged organic aerosol in the Eastern Mediterranean during the Finokalia Aerosol Measurement Experiment in May of 2008 (FAME-2008). A new method for the quantification of the organic aerosol volatility distribution was developed combining measurements of all three instruments together with an aerosol dynamics model. Challenges in the interpretation of ambient thermodenuder-AMS measurements include the potential resistances to mass transfer during particle evaporation, the effects of particle size on the evaporated mass fraction, the changes in the AMS collection efficiency and particle density as the particles evaporate partially in the TD, and finally potential losses inside the TD. Our proposed measurement and data analysis method accounts for all of these problems combining the AMS and SMPS measurements. The AMS collection efficiency of the aerosol that passed through the TD was found to be approximately 10% lower than the collection efficiency of the aerosol that passed through the bypass. The organic aerosol measured at Finokalia is approximately 2 or more orders of magnitude less volatile than fresh laboratory-generated monoterpene (α-pinene, β-pinene and limonene under low NOx conditions) secondary organic aerosol. This low volatility is consistent with its highly oxygenated AMS mass spectrum. The results are found to be highly sensitive to the mass accommodation coefficient of the evaporating species. This analysis is based on the assumption that there were no significant reactions taking place inside the thermodenuder.

This paper empirically examines the statistical properties of realized volatility and the relationships between volatility and correlation measurements of realized volatility by using intraday high-frequency foreign exchange (FX) rates. Results regarding the distributional and dynamic properties of realized volatility are in agreement with the findings of previous studies. However, the positive correlation present in previous studies is not found in the case of JPY. On trading days with low volatility in the FX market, realized correlation coefficients between JPY and other currencies have positive values, while realized correlation coefficients on trading days with high volatility show negative values. These results are due to the Japanese government's intervention in the FX market, particularly during trading days with high volatility. In this regard, our results suggest that the positive relationships between volatility and correlations verified in previous studies are not a general phenomenon in the case of government intervention and government intervention may distort the efficiency of the FX market. In addition, we show that the multivariate measurement of realized volatility based on intraday high-frequency data can be a useful tool for determining the occurrence of external intervention in the FX market.

La volatilité des prix alimentaires est devenue un sujet de préoccupation constante dans les pays en développement suite à la flambée des prix des produits alimentaires en 2007/08 et 2010/11. Cette thèse s’intéresse à la caractérisation de la volatilité des prix au Burkina Faso. La volatilité des prix est définie comme la part imprévisible des variations de prix. Les objectifs de cette thèse sont en particulier i) d’évaluer les caractéristiques de la volatilité des prix du maïs au Burkina Faso, ii) d’analyser ses déterminants et iii) ses impacts sur le comportement des producteurs. Pour répondre à ces questions complémentaires, nous avons combiné des données originales et riches de prix céréaliers sur plusieurs marchés et des données sur l’activité agricole de près de 2000 producteurs sur l’ensemble du territoire Burkinabé. Plusieurs résultats émergent dans cette thèse. Premièrement, ces données ont permis d’isoler le secteur clé du maïs pour ensuite présenter de manière détaillée les données sur les prix du maïs et sur l’activité agricole des ménages utilisés dans la suite de la thèse (chapitre 1). Deuxièmement, l’analyse des séries de prix du maïs sur chaque marché propose le processus ARCH comme modèle de séries chronologiques qui explique le mieux les caractéristiques de la volatilité des prix sur la majorité des marchés. Sur ces marchés les baisses et les hausses de prix ont une contribution similaire sur la volatilité des prix, et seuls les chocs de court terme l’affectent. Les autres marchés sont caractérisés par une persistance de la volatilité avec un effet différencié des variations de prix qui s’expliquent par les caractéristiques géographiques (chapitre 2). Troisièmement, l'analyse des séries de prix en panel révèle que la volatilité des prix du maïs est élevée sur les marchés les plus enclavés (chapitre 3). Quatrièmement, l’analyse des séries de prix du maïs combinés aux données sur l’activité agricole des ménages indiquent qu’une hausse des prix du maïs accroît l'utilisation des engrais chimiques. Toutefois, les variations de prix imprévisibles diminuent le niveau d'utilisation de ces engrais ; tandis que les variations des prix prévisibles n’ont aucun effet significatif sur leur utilisation (chapitre 4). La principale originalité de cette thèse réside dans le traitement des questions relatives à la volatilité des prix à l’échelle des marchés locaux et à un niveau microéconomique avec des données de ménage, alors que cette problématique est généralement perçue sous un angle macroéconomique à l’échelle internationale
Food price volatility is an ongoing concern in developing countries since the food price spikes in 2007/08 and 2010/11. This dissertation focuses on the patterns of food price volatility in Burkina Faso. Price volatility is defined as the unpredictable component of price variations. The aim of this dissertation is to contribute to a better understanding of three complementary issues i) the nature of maize price volatility in Burkina Faso, ii) its determinants and iii) its impacts on agricultural producers’ behavior. We combine an original database of grain prices on 28 local markets in the last 15 years and a panel database of almost 2,000 farm households’ production choices throughout the. Our results can be summarized as follows. First, these data allowed isolating the key sector of maize and then presenting detailed data on maize price series and the agricultural activity of households used in the remainder of this thesis (chapter 1). Second, the analysis of maize price series in each market suggests that ARCH model as the dominant time-series model to describe price volatility patterns in most markets in Burkina Faso. In these markets, price drops and peaks have a similar contribution to price volatility, and only recent episodes of price variations increase current volatility. Other markets are characterized by long term volatility episodes with a differential effect of price variations due to the geographical position (Chapter 2).Third, the analysis with panel method of maize price series shows that maize price volatility is greater in remote markets (Chapter 3). Fourth, by combining price series on local cereal markets and a panel data set on farm households’ production choices, we find that higher maize prices increase the quantity of chemical fertilizer use. However, unpredictable maize price variations decrease the level of fertilizer use; while predictable maize prices have no significant effect on fertilizer use (Chapter 4). The novelty of this thesis lies in the analysis of price volatility on local markets and at a micro level with household data, whereas this issue is usually perceived at the macroeconomic scale

This study comprises of three essays on the subject of financial risk management with applications in the fields of portfolio optimization, continuous and discrete time stochastic volatility (SV) modelling. We jointly consider two risk measures: Value-at-Risk (VaR) and conditional Value-at-Risk (CVaR) to measure the financial market risk. In order to model the distribution of financial asset returns which is characterized by skewness, heavy tails and leptokurtosis, we employ the Asymmetric Laplace distribution (ALD) in the first and third essay while constructing the risk model on the basis of the Heston stochastic volatility (SV) model in the second essay. Specifically, in the first essay, we provide a comprehensive empirical examination of the viability of the new proposed Mean-CVaR-Skewness optimization model under ALD by Zhao et al. (2015). In addition, we propose the Mean-VaR-Skewness model under ALD by employing VaR as risk measure. The closed-form solution of the two optimization models is shown to be consistent and is obtainable by using the Lagrange Multiplier approach. In the second essay, we construct the VaR and CVaR models for the financial dynamics that do not have a closed-form probability density function. The only input required in our approach is the knowledge of the characteristic function of the underlying asset. In the numerical analysis, we investigate the elements that could impact the VaR and CVaR approximations in the Heston model. The third essay contributes to the existing literature by extending the ALD (Kotz et al., 2001) to the return error term of a standard discrete time SV model. We give the closed-form VaR and CVaR formulas for oil supply and demand. As additional contribution, we propose a new scale mixture of uniform (SMU) representation for the AL density so that the model can be implemented efficiently within the Bayesian Markov Chain Monte Carlo framework.

Exchange rate movements play substantial role in risk measurement and their effective management. Volatility in exchange rates has been quite large and it has affected sales as well as profit margins of multinationals in India. Based on statistic analysis, some suggestion have been drawn for improving functioning of forex exchange market in India.
1. Most hedging instruments are required to cope up extreme volatility of INR against all major currencies of the world. 2. Steady liberalization of financial markets is need more attention on business who invest back in India. 3. Promotion of invoicing of trade in domestic currency will be extremely helpful and beneficial to cope up with extreme volatility. 4. There has been wide progress and enhancement of INR market across globe especially in Dubai, Singapore, London and New York, so it is need to try relocate of offshore activities on shore. 5. RBI has taken a number of steps in the recent past to liberalize currency futures market to obviate/reduce the need for the NDF market. 6. There is need for effective coalition between OTC and exchange traded markets for currency futures. 7. More focus should be to advocate the importance and practicability of risk management techniques in particular using options. 8. There is need to develop strict monitoring mechanism by liberalizing open position limits of banks.

The focus of this thesis is on the employment of theoretical and practical quantile methods in addressing prediction, risk measurement and inference problems. From a prediction perspective, a problem of creating model-free prediction intervals for a future unobserved value of a random variable drawn from a sample distribution is considered. With the objective of reducing prediction coverage error, two common distribution transformation methods based on the normal and exponential distributions are presented and they are theoretically demonstrated to attain exact and error-free prediction intervals respectively. The second problem studied is that of estimation of expected shortfall via kernel smoothing. The goal here is to introduce methods that will reduce the estimation bias of expected shortfall. To this end, several one-step bias correction expected shortfall estimators are presented and investigated via simulation studies and compared with one-step estimators. The third problem is that of constructing simultaneous confidence bands for quantile regression functions when the predictor variables are constrained within a region is considered. In this context, a method is introduced that makes use of the asymmetric Laplace errors in conjunction with a simulation based algorithm to create confidence bands for quantile and interquantile regression functions. Furthermore, the simulation approach is extended to an ordinary least square framework to build simultaneous bands for quantiles functions of the classical regression model when the model errors are normally distributed and when this assumption is not fulfilled. Finally, attention is directed towards the construction of prediction intervals for realised volatility exploiting an alternative volatility estimator based on the difference of two extreme quantiles. The proposed approach makes use of AR-GARCH procedure in order to model time series of intraday quantiles and forecast intraday returns predictive distribution. Moreover, two simple adaptations of an existing model are also presented.

Thesis: Ph. D. in Environmental Chemistry, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Organic aerosol is a central topic in environmental science due to its role in climate forcing and negative health effects. The transformation of organic species from primary gas phase emissions to secondary organic aerosol (SOA) is highly complex and poorly understood, proving difficult for even stateof- the-art computational models to predict. This thesis describes the in-depth characterization and redesign of a previously developed technique for the quantification of intermediate volatility organic compounds (IVOCs), which are compounds with saturation vapor pressures of 10³-10⁷ [mu]g/m³. This analytical technique, the thermal-desorption electron ionization mass spectrometer (TD-EIMS) provides a volatility separated, bulk measurement of IVOCs and will be used to investigate the primary emissions as well as production and evolution of IVOCs in a series of experiments described in this thesis. Primary emissions of IVOCs have been previously measured in vehicle exhaust and have been theorized as a significant precursor to secondary organic aerosol (SOA) in urban atmospheres. IVOCs are predominately emitted during cold start periods, but maintain a similar chemical composition across all engine states. As emissions controls have tightened, emissions of non-methane hydrocarbons and primary particulate matter have decreased, however emissions of IVOCs have only decreased significantly (as much as 80%) between the newest ULEV and SULEV emissions control tiers. Laboratory studies examining the atmospheric oxidation of common biogenic and anthropogenic SOA precursors in environmental "smog" chambers show different production and evolution profiles of IVOCs. The comparison of IVOCs measured by the TD-EIMS with other analytical techniques sampling in parallel show the TD-EIMS may detect a previously characterized fraction of carbon. Production of secondary low volatility organic compounds can also occur in low oxygen systems, such as in planetary atmospheres or in the process of soot formation. Ultraviolet light or heat can form radical hydrocarbon species, which, in low oxygen environments, will react with other hydrocarbon or radical species, undergoing oxidation by molecular growth. Particles made from ethane and ethylene are composed of very saturated compounds. The particles produced from the photolysis of acetylene are fundamentally different showing significantly larger molecule sizes and substantially higher degrees of unsaturation. The results from this thesis demonstrate measurements of the production and evolution of primary and secondary low volatility organic gases by new analytical techniques and provide a new insight to the complex chemical processes in the atmosphere leading to the production of secondary organic aerosol.
by Jonathan Pfeil Franklin.
Ph. D. in Environmental Chemistry

Diese Arbeit konzentriert sich auf die Lösung des Problems der Unsicherheitsmessung und ihrer Auswirkungen auf Geschäftsentscheidungen, wobei zwei Ziele verfolgt werden: Erstens die Entwicklung und Validierung robuster Modelle zur Quantifizierung der Unsicherheit, wobei insbesondere sowohl die etablierten statistischen Modelle als auch neu entwickelte maschinelle Lernwerkzeuge zum Einsatz kommen. Das zweite Ziel dreht sich um die industrielle Anwendung der vorgeschlagenen Modelle. Die Anwendung auf reale Fälle bei der Messung der Volatilität oder bei einer riskanten Entscheidung ist mit einem direkten und erheblichen Gewinn oder Verlust verbunden. Diese These begann mit der Untersuchung der impliziten Volatilität (IV) als Proxy für die Wahrnehmung der Unsicherheit von Anlegern für eine neue Klasse von Vermögenswerten - Kryptowährungen. Das zweite Papier konzentriert sich auf Methoden zur Identifizierung risikofreudiger Händler und nutzt die DNN-Infrastruktur, um das Risikoverhalten von Marktakteuren, das auf Unsicherheit beruht und diese aufrechterhält, weiter zu untersuchen. Das dritte Papier befasste sich mit dem herausfordernden Bestreben der Betrugserkennung 3 und bot das Entscheidungshilfe-modell, das eine genauere und interpretierbarere Bewertung der zur Prüfung eingereichten Finanzberichte ermöglichte. Angesichts der Bedeutung der Risikobewertung und der Erwartungen der Agenten für die wirtschaftliche Entwicklung und des Aufbaus der bestehenden Arbeiten von Baker (2016) bot das vierte Papier eine neuartige DL-NLP-basierte Methode zur Quantifizierung der wirtschaftspolitischen Unsicherheit. Die neuen Deep-Learning-basierten Lösungen bieten eine überlegene Leistung gegenüber bestehenden Ansätzen zur Quantifizierung und Erklärung wirtschaftlicher Unsicherheiten und ermöglichen genauere Prognosen, verbesserte Planungskapazitäten und geringere Risiken. Die angebotenen Anwendungsfälle bilden eine Plattform für die weitere Forschung.
This thesis focuses on solving the problem of uncertainty measurement and its impact on business decisions while pursuing two goals: first, develop and validate accurate and robust models for uncertainty quantification, employing both the well established statistical models and newly developed machine learning tools, with particular focus on deep learning. The second goal revolves around the industrial application of proposed models, applying them to real-world cases when measuring volatility or making a risky decision entails a direct and substantial gain or loss. This thesis started with the exploration of implied volatility (IV) as a proxy for investors' perception of uncertainty for a new class of assets - crypto-currencies. The second paper focused on methods to identify risk-loving traders and employed the DNN infrastructure for it to investigate further the risk-taking behavior of market actors that both stems from and perpetuates uncertainty. The third paper addressed the challenging endeavor of fraud detection and offered the decision support model that allowed a more accurate and interpretable evaluation of financial reports submitted for audit. Following the importance of risk assessment and agents' expectations in economic development and building on the existing works of Baker (2016) and their economic policy uncertainty (EPU) index, it offered a novel DL-NLP-based method for the quantification of economic policy uncertainty. In summary, this thesis offers insights that are highly relevant to both researchers and practitioners. The new deep learning-based solutions exhibit superior performance to existing approaches to quantify and explain economic uncertainty, allowing for more accurate forecasting, enhanced planning capacities, and mitigated risks. The offered use-cases provide a road-map for further development of the DL tools in practice and constitute a platform for further research.

The research presented in this thesis addresses different aspects of dynamic portfolio construction and portfolio risk measurement. It brings the research on dynamic portfolio optimization, replicating portfolio construction, dynamic portfolio risk measurement and volatility forecast together. The overall aim of this research is threefold. First, it is aimed to examine the portfolio construction and risk measurement performance of a broad set of volatility forecast and portfolio optimization model. Second, in an effort to improve their forecast accuracy and portfolio construction performance, it is aimed to propose new models or new formulations to the available models. Third, in order to enhance the replication performance of hedge fund returns, it is aimed to introduce a replication approach that has the potential to be used in numerous applications, in investment management. In order to achieve these aims, Chapter 2 addresses risk measurement in dynamic portfolio construction. In this chapter, further evidence on the use of multivariate conditional volatility models in hedge fund risk measurement and portfolio allocation is provided by using monthly returns of hedge fund strategy indices for the period 1990 to 2009. Building on Giamouridis and Vrontos (2007), a broad set of multivariate GARCH models, as well as, the simpler exponentially weighted moving average (EWMA) estimator of RiskMetrics (1996) are considered. It is found that, while multivariate GARCH models provide some improvements in portfolio performance over static models, they are generally dominated by the EWMA model. In particular, in addition to providing a better risk-adjusted performance, the EWMA model leads to dynamic allocation strategies that have a substantially lower turnover and could therefore be expected to involve lower transaction costs. Moreover, it is shown that these results are robust across the low - volatility and high-volatility sub-periods. Chapter 3 addresses optimization in dynamic portfolio construction. In this chapter, the advantages of introducing alternative optimization frameworks over the mean-variance framework in constructing hedge fund portfolios for a fund of funds. Using monthly return data of hedge fund strategy indices for the period 1990 to 2011, the standard mean-variance approach is compared with approaches based on CVaR, CDaR and Omega, for both conservative and aggressive hedge fund investors. In order to estimate portfolio CVaR, CDaR and Omega, a semi-parametric approach is proposed, in which first the marginal density of each hedge fund index is modelled using extreme value theory and the joint density of hedge fund index returns is constructed using a copula-based approach. Then hedge fund returns from this joint density are simulated in order to compute CVaR, CDaR and Omega. The semi-parametric approach is compared with the standard, non-parametric approach, in which the quantiles of the marginal density of portfolio returns are estimated empirically and used to compute CVaR, CDaR and Omega. Two main findings are reported. The first is that CVaR-, CDaR- and Omega-based optimization offers a significant improvement in terms of risk-adjusted portfolio performance over mean-variance optimization. The second is that, for all three risk measures, semi-parametric estimation of the optimal portfolio offers a very significant improvement over non-parametric estimation. The results are robust to as the choice of target return and the estimation period. Chapter 4 searches for improvements in portfolio risk measurement by addressing volatility forecast. In this chapter, two new univariate Markov regime switching models based on intraday range are introduced. A regime switching conditional volatility model is combined with a robust measure of volatility based on intraday range, in a framework for volatility forecasting. This chapter proposes a one-factor and a two-factor model that combine useful properties of range, regime switching, nonlinear filtration, and GARCH frameworks. Any incremental improvement in the performance of volatility forecasting is searched for by employing regime switching in a conditional volatility setting with enhanced information content on true volatility. Weekly S&P500 index data for 1982-2010 is used. Models are evaluated by using a number of volatility proxies, which approximate true integrated volatility. Forecast performance of the proposed models is compared to renowned return-based and range-based models, namely EWMA of Riskmetrics, hybrid EWMA of Harris and Yilmaz (2009), GARCH of Bollerslev (1988), CARR of Chou (2005), FIGARCH of Baillie et al. (1996) and MRSGARCH of Klaassen (2002). It is found that the proposed models produce more accurate out of sample forecasts, contain more information about true volatility and exhibit similar or better performance when used for value at risk comparison. Chapter 5 searches for improvements in risk measurement for a better dynamic portfolio construction. This chapter proposes multivariate versions of one and two factor MRSACR models introduced in the fourth chapter. In these models, useful properties of regime switching models, nonlinear filtration and range-based estimator are combined with a multivariate setting, based on static and dynamic correlation estimates. In comparing the out-of-sample forecast performance of these models, eminent return and range-based volatility models are employed as benchmark models. A hedge fund portfolio construction is conducted in order to investigate the out-of-sample portfolio performance of the proposed models. Also, the out-of-sample performance of each model is tested by using a number of statistical tests. In particular, a broad range of statistical tests and loss functions are utilized in evaluating the forecast performance of the variance covariance matrix of each portfolio. It is found that, in terms statistical test results, proposed models offer significant improvements in forecasting true volatility process, and, in terms of risk and return criteria employed, proposed models perform better than benchmark models. Proposed models construct hedge fund portfolios with higher risk-adjusted returns, lower tail risks, offer superior risk-return tradeoffs and better active management ratios. However, in most cases these improvements come at the expense of higher portfolio turnover and rebalancing expenses. Chapter 6 addresses the dynamic portfolio construction for a better hedge fund return replication and proposes a new approach. In this chapter, a method for hedge fund replication is proposed that uses a factor-based model supplemented with a series of risk and return constraints that implicitly target all the moments of the hedge fund return distribution. The approach is used to replicate the monthly returns of ten broad hedge fund strategy indices, using long-only positions in ten equity, bond, foreign exchange, and commodity indices, all of which can be traded using liquid, investible instruments such as futures, options and exchange traded funds. In out-of-sample tests, proposed approach provides an improvement over the pure factor-based model, offering a closer match to both the return performance and risk characteristics of the hedge fund strategy indices.

The drivers of human health and changing climate are important areas of environmental and atmospheric studies. Among many environmental factors present in our biosphere, small particles, also known as ultrafine particles or UFPs, have direct and indirect pathways to affect human health and climatic processes. The rapid change in their properties makes UFPs dynamic and often challenging to quantify their effect on health and radiative forcing. To reduce uncertainty in the climate effects of UFPs and to strengthen the evidence on health effects, accurate characterizations of physical and chemical properties of UFPs are needed. In this thesis, two broad aspects of UFPs were investigated: (1) the development of particle instrumentation to study particle properties; and (2) measurement of physical and chemical properties of UFPs relevant to human health and climate. These two broad aspects are divided into four specific aims in this thesis. The measurement of UFP concentration at different locations in an urban location, from roadside to various residential areas, can be improved by using a mobile particle counter. A TSI 3786 Condensation Particle Counter (CPC) was modified for mobile battery-power operation. This design provided high-frequency, one second time resolution measurements of particle number and carbon dioxide (CO2). An independent electric power system, a central controller and robust data acquisition system, and a GPS system are the major components of this mobile unit. These capabilities make the system remotely deployable, and also offer flexibility to integrate other analog and digital sensors. A Volatility Tandem Differential Mobility Analyzer (V-TDMA) system was designed and built to characterize the volatility behavior of UFPs. The physical and chemical properties of UFPs are often challenging to measure due to limited availability of instruments, detection limit in terms of particle size and concentration, and sampling frequency. Indirect methods such as V-TDMA are useful, for small mass (<1 µg/m3), and nuclei mode particles (<30nm). Another advantage of V-TDMA is its fast response in terms of sampling frequency. A secondary motivation for building a V-TDMA system was to improve instrumentation capability of our group, thus enabling study of kinetic and thermodynamic properties of novel aerosols. Chapter four describes the design detail of the built V-TDMA system, which measures the change in UFP size and concentration during heated and non-heated (or ambient) condition. The V-TDMA system has an acceptable penetration efficiency of 85% for 10 nm and maintains a uniform temperature profile in the heating system. Calibration of V-TDMA using ammonium sulfate particles indicated that the system produces comparable evaporation curves (in terms of volatilization temperature) or volatility profiles to other published V-TDMA designs. Additionally the system is fully programmable with respect to particle size, temperature and sampling frequency and can be run autonomously after initial set up. The thesis describes a part of yearlong study to provide a complete perspective on particle formation and growth in a rural and agricultural Midwestern site. Volatility characterizations of UFPs were conducted to enable inference about particle chemistry, and formation of low volatile core or evaporation resistant residue in the UFP in the Midwest. This study addresses identification of the volatility signature of particles in the UFP size range, quantification of physical differences of UFPs between NPF1 and non-NPF events and relation of evaporation resistant residue with particle size, seasonality and mixing state. K-means clustering was applied to determine three unique volatility clusters in 15, 30, 50 and 80 nm particle sizes. Based on the proposed average volatility, the identified volatility clusters were classified into high volatile, intermediate volatile and least volatile group. Although VFR alone is insufficient to establish chemical composition definitively, least volatile cluster based on average volatility may be characteristically similar to the pure ammonium sulfate. The amount of evaporation residue at 200 °C was positively correlated with particle size and showed significant correlation with ozone, sulfur dioxide and solar radiation. Residue also indicated the presence of external mixture, often during morning and night time. Air quality science and management of an accidental urban tire fire occurring in Iowa City in May and June of 2012 were investigated. Urban air quality emergencies near populated areas are difficult to evaluate without a proper air quality management and response system. To support the development of an appropriate air quality system, this thesis identified and created a rank for health-related acute and chronic compounds in the tire smoke. For health risk assessment, the study proposed an empirical equation for estimating multi-pollutant air quality index. Using mobile measurements and a dispersion model in conjunction with the proposed air quality index, smoke concentrations and likely health impact were evaluated for Iowa City and surrounding areas. It was concluded that the smoke levels reached unhealthy outdoor levels for sensitive groups out to distances of 3.1 km and 18 km at 24 h and 1h average times. Tire smoke characterization was another important aspect of this study which provided important and new information about tire smoke. Revised emission factors for coarse particle mass and aerosol-PAH and new emission factors and enhancement ratios values for a wide range of fine particulate mass, particle size (0.001-2.5 µm), and trace gas were estimated. Overall the thesis added new instrumentation in our research group to measure various physical properties such as size, concentration, and volatility UFP. The built instruments, data processing algorithm and visualization tools will be useful in estimation of accurate concentration and emission factors of UFP for health exposure studies, and generate a fast response measurement of kinetic and thermodynamics properties of ambient particles. This thesis also makes a strong case for the development of an air quality emergency system for accidental fires for urban location. It provides useful evaluation and estimation of many aspects of such system such as smoke characterization, method of air quality monitoring and impact assessment, and develops communicable method of exposure risk assessment.

This book provides an objective look into the complicated and rapidly changing world of hedge funds. The book does not attempt to promote hedge funds as an asset class but instead provides a synthesis of the theoretical and empirical literature on hedge funds. By providing objective evidence, the book dispels some common misconceptions about hedge funds involving their volatility and use of derivatives and leverage. The book examines hedge funds and provides important insights about such topics as their structure and how they work, hedge fund strategies, performance and measurement, and trends and future prospects. Readers gain an in-depth understanding about hedge funds from experts in this field from around the world. In today’s financial environment, hedge funds emerge as a dynamic area that continues to evolve at a rapid pace. This book takes readers not only through the core topics and issues of hedge funds but also examines the latest trends and leading issues and developments. Additionally, discussion of research on hedge funds permeates the book. The coverage extends from discussing basic concepts and their application to increasingly intricate and real-world situations. Thus, this volume spans the gamut from theoretical to practical, while attempting to offer a useful balance of detailed and user-friendly coverage. Readers interested in a broad survey will benefit as will those looking for more in-depth presentations of specific areas within this field of study.

Abstract This chapter presents a new measure of volatility: the residuals of a Box-Jenkins procedure applied to the time series of states’ behaviors toward their counterpart. It explains why this measure can systematically capture volatility over time and space. Measurements of volatility (such as standard deviation and relative change) are inadequate to accurately distinguish volatile change in states’ behaviors from other forms of change. The chapter then compares volatility in the behaviors of countries such as India, Cuba, Pakistan, Iran, Japan, and South Korea toward both allies and rivals. The chapter demonstrates that volatility cannot be reduced to other forms of change in states’ behaviors; that it greatly varies across time and cases; and that, by abandoning useful but at times too-strict dichotomizations such as those suggested in the concepts of “alliances” and “rivalries,” the concept and measurement of volatility provide a richer, more satisfying understanding of the international arena.

Fuel combustion performance was quantified through measurement of the gravimetric response of the fuel as well as the energetic behavior. A Simultaneous Thermogravimetric Analyzer (STA) was used to measure the gravimetric, sensible energy, and latent heat energy (including heat of pyrolysis) for fuels. The heat release rate and heat of combustion of the fuels as a function of temperature released due to the combustion of the pyrolysis gases was measured using a Micro-Combustion Calorimeter (MCC). Fuels were tested at a heating rate of 20°C/min from room temperature to 800°C in inert (nitrogen) environment. Fuels considered in this study included US eastern coal, biomass (cornstover and switchgrass), polystyrene, glycerol and mixtures of some of these fuels. Biomass feedstocks were also evaluated for the effect of water leaching on fuel performance. A lumped model energy balance on a fuel particle revealed that fuel volatility can be ranked based on the net energy required to produce the volatile gas times the ratio of the heat of combustion to the heat of decomposition. This is different compared with previous research results in that it includes the temperature dependence of the fuel production in the volatility.

In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we divide the time series in two parts (first 2000 measurement points in Part-1 and the rest is Part-2), we found that for Part-2 data, FIGARCH offers best performance indicating that conditional heteroscedasticity should be included in time series with high volatility.

Particulate matter (PM) emissions from gasoline direct injection (GDI) engines are a concern due to the health effects associated with ultrafine PM. This experimental study investigated sources of PM emissions measurement variability observed in previous tests and also examined the effect of ethanol content in gasoline on PM emissions. Some engine operating parameters (fuel and oil temperature, PCV filtration) and test conditions (dilution air conditions) were studied and controlled but could not account for the level of measurement variability observed. Fourier Transform Infrared Spectrometry (FTIR) measurements of gas phase hydrocarbon emissions provided evidence that changes in fuel composition were responsible for the variability. Exhaust emissions of toluene and ethanol were correlated positively with PM emissions, while emissions of isobutylene correlated negatively. Exhaust emissions of toluene and isobutylene were interpreted as markers of gasoline aromatic content and gasoline volatility respectively. Tests conducted with gasoline containing added toluene (10% v/v) supported this hypothesis and led to the overall conclusion that the PM emissions variability observed can be attributed to changes in the composition of the pump gasoline being used. Tests conducted with gasoline containing added ethanol (10% and 30% v/v) found that increasing ethanol fuel content increased PM emissions at the steady-state operating condition utilized.

We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be partially unobserved, resulting in a multivariate unobserved components model. In our empirical analysis, we provide quarterly term structures of expectations and uncertainty bands. Our preferred specification features stochastic volatility in forecast updates, which improves forecast performance and yields model estimates of forecast uncertainty that vary over time. We conclude by constructing SPF-based fan charts for calendar-year forecasts like those published by the Federal Reserve. Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts.

Macroeconomic summary Several factors contributed to an increase in projected inflation on the forecast horizon, keeping it above the target rate. These included inflation in December that surpassed expectations (5.62%), indexation to higher inflation rates for various baskets in the consumer price index (CPI), a significant real increase in the legal minimum wage, persistent external and domestic inflationary supply shocks, and heightened exchange rate pressures. The CPI for foods was affected by the persistence of external and domestic supply shocks and was the most significant contributor to unexpectedly high inflation in the fourth quarter. Price adjustments for fuels and certain utilities can explain the acceleration in inflation for regulated items, which was more significant than anticipated. Prices in the CPI for goods excluding food and regulated items also rose more than expected. This was partly due to a smaller effect on prices from the national government’s VAT-free day than anticipated by the technical staff and more persistent external pressures, including via peso depreciation. By contrast, the CPI for services excluding food and regulated items accelerated less than expected, partly reflecting strong competition in the communications sector. This was the only major CPI basket for which prices increased below the target inflation rate. The technical staff revised its inflation forecast upward in response to certain external shocks (prices, costs, and depreciation) and domestic shocks (e.g., on meat products) that were stronger and more persistent than anticipated in the previous report. Observed inflation and a real increase in the legal minimum wage also exceeded expectations, which would boost inflation by affecting price indexation, labor costs, and inflation expectations. The technical staff now expects year-end headline inflation of 4.3% in 2022 and 3.4% in 2023; core inflation is projected to be 4.5% and 3.6%, respectively. These forecasts consider the lapse of certain price relief measures associated with the COVID-19 health emergency, which would contribute to temporarily keeping inflation above the target on the forecast horizon. It is important to note that these estimates continue to contain a significant degree of uncertainty, mainly related to the development of external and domestic supply shocks and their ultimate effects on prices. Other contributing factors include high price volatility and measurement uncertainty related to the extension of Colombia’s health emergency and tax relief measures (such as the VAT-free days) associated with the Social Investment Law (Ley de Inversión Social). The as-yet uncertain magnitude of the effects of a recent real increase in the legal minimum wage (that was high by historical standards) and high observed and expected inflation, are additional factors weighing on the overall uncertainty of the estimates in this report. The size of excess productive capacity remaining in the economy and the degree to which it is closing are also uncertain, as the evolution of the pandemic continues to represent a significant forecast risk. margin, could be less dynamic than expected. And the normalization of monetary policy in the United States could come more quickly than projected in this report, which could negatively affect international financing costs. Finally, there remains a significant degree of uncertainty related to the duration of supply chocks and the degree to which macroeconomic and political conditions could negatively affect the recovery in investment. The technical staff revised its GDP growth projection for 2022 from 4.7% to 4.3% (Graph 1.3). This revision accounts for the likelihood that a larger portion of the recent positive dynamic in private consumption would be transitory than previously expected. This estimate also contemplates less dynamic investment behavior than forecast in the previous report amid less favorable financial conditions and a highly uncertain investment environment. Third-quarter GDP growth (12.9%), which was similar to projections from the October report, and the fourth-quarter growth forecast (8.7%) reflect a positive consumption trend, which has been revised upward. This dynamic has been driven by both public and private spending. Investment growth, meanwhile, has been weaker than forecast. Available fourth-quarter data suggest that consumption spending for the period would have exceeded estimates from October, thanks to three consecutive months that included VAT-free days, a relatively low COVID-19 caseload, and mobility indicators similar to their pre-pandemic levels. By contrast, the most recently available figures on new housing developments and machinery and equipment imports suggest that investment, while continuing to rise, is growing at a slower rate than anticipated in the previous report. The trade deficit is expected to have widened, as imports would have grown at a high level and outpaced exports. Given the above, the technical staff now expects fourth-quarter economic growth of 8.7%, with overall growth for 2021 of 9.9%. Several factors should continue to contribute to output recovery in 2022, though some of these may be less significant than previously forecast. International financial conditions are expected to be less favorable, though external demand should continue to recover and terms of trade continue to increase amid higher projected oil prices. Lower unemployment rates and subsequent positive effects on household income, despite increased inflation, would also boost output recovery, as would progress in the national vaccination campaign. The technical staff expects that the conditions that have favored recent high levels of consumption would be, in large part, transitory. Consumption spending is expected to grow at a slower rate in 2022. Gross fixed capital formation (GFCF) would continue to recover, approaching its pre-pandemic level, though at a slower rate than anticipated in the previous report. This would be due to lower observed GFCF levels and the potential impact of political and fiscal uncertainty. Meanwhile, the policy interest rate would be less expansionary as the process of monetary policy normalization continues. Given the above, growth in 2022 is forecast to decelerate to 4.3% (previously 4.7%). In 2023, that figure (3.1%) is projected to converge to levels closer to the potential growth rate. In this case, excess productive capacity would be expected to tighten at a similar rate as projected in the previous report. The trade deficit would tighten more than previously projected on the forecast horizon, due to expectations of an improved export dynamic and moderation in imports. The growth forecast for 2022 considers a low basis of comparison from the first half of 2021. However, there remain significant downside risks to this forecast. The current projection does not, for example, account for any additional effects on economic activity resulting from further waves of COVID-19. High private consumption levels, which have already surpassed pre-pandemic levels by a large margin, could be less dynamic than expected. And the normalization of monetary policy in the United States could come more quickly than projected in this report, which could negatively affect international financing costs. Finally, there remains a significant degree of uncertainty related to the duration of supply chocks and the degree to which macroeconomic and political conditions could negatively affect the recovery in investment. External demand for Colombian goods and services should continue to recover amid significant global inflation pressures, high oil prices, and less favorable international financial conditions than those estimated in October. Economic activity among Colombia’s major trade partners recovered in 2021 amid countries reopening and ample international liquidity. However, that growth has been somewhat restricted by global supply chain disruptions and new outbreaks of COVID-19. The technical staff has revised its growth forecast for Colombia’s main trade partners from 6.3% to 6.9% for 2021, and from 3.4% to 3.3% for 2022; trade partner economies are expected to grow 2.6% in 2023. Colombia’s annual terms of trade increased in 2021, largely on higher oil, coffee, and coal prices. This improvement came despite increased prices for goods and services imports. The expected oil price trajectory has been revised upward, partly to supply restrictions and lagging investment in the sector that would offset reduced growth forecasts in some major economies. Elevated freight and raw materials costs and supply chain disruptions continue to affect global goods production, and have led to increases in global prices. Coupled with the recovery in global demand, this has put upward pressure on external inflation. Several emerging market economies have continued to normalize monetary policy in this context. Meanwhile, in the United States, the Federal Reserve has anticipated an end to its asset buying program. U.S. inflation in December (7.0%) was again surprisingly high and market average inflation forecasts for 2022 have increased. The Fed is expected to increase its policy rate during the first quarter of 2022, with quarterly increases anticipated over the rest of the year. For its part, Colombia’s sovereign risk premium has increased and is forecast to remain on a higher path, to levels above the 15-year-average, on the forecast horizon. This would be partly due to the effects of a less expansionary monetary policy in the United States and the accumulation of macroeconomic imbalances in Colombia. Given the above, international financial conditions are projected to be less favorable than anticipated in the October report. The increase in Colombia’s external financing costs could be more significant if upward pressures on inflation in the United States persist and monetary policy is normalized more quickly than contemplated in this report. As detailed in Section 2.3, uncertainty surrounding international financial conditions continues to be unusually high. Along with other considerations, recent concerns over the potential effects of new COVID-19 variants, the persistence of global supply chain disruptions, energy crises in certain countries, growing geopolitical tensions, and a more significant deceleration in China are all factors underlying this uncertainty. The changing macroeconomic environment toward greater inflation and unanchoring risks on inflation expectations imply a reduction in the space available for monetary policy stimulus. Recovery in domestic demand and a reduction in excess productive capacity have come in line with the technical staff’s expectations from the October report. Some upside risks to inflation have materialized, while medium-term inflation expectations have increased and are above the 3% target. Monetary policy remains expansionary. Significant global inflationary pressures and the unexpected increase in the CPI in December point to more persistent effects from recent supply shocks. Core inflation is trending upward, but remains below the 3% target. Headline and core inflation projections have increased on the forecast horizon and are above the target rate through the end of 2023. Meanwhile, the expected dynamism of domestic demand would be in line with low levels of excess productive capacity. An accumulation of macroeconomic imbalances in Colombia and the increased likelihood of a faster normalization of monetary policy in the United States would put upward pressure on sovereign risk perceptions in a more persistent manner, with implications for the exchange rate and the natural rate of interest. Persistent disruptions to international supply chains, a high real increase in the legal minimum wage, and the indexation of various baskets in the CPI to higher inflation rates could affect price expectations and push inflation above the target more persistently. These factors suggest that the space to maintain monetary stimulus has continued to diminish, though monetary policy remains expansionary. 1.2 Monetary policy decision Banco de la República’s board of directors (BDBR) in its meetings in December 2021 and January 2022 voted to continue normalizing monetary policy. The BDBR voted by a majority in these two meetings to increase the benchmark interest rate by 50 and 100 basis points, respectively, bringing the policy rate to 4.0%.