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18
Financial Risk Measurement for Financial Risk Management
, 2011
"... Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating bot ..."
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Cited by 4 (2 self)
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Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfoliolevel and assetlevel analysis. Assetlevel analysis is particularly challenging because the demands of realworld risk management in financial institutions – in particular, realtime risk tracking in very highdimensional situations – impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also crossfertilize the academic and practitioner communities, promoting improved market risk measurement
An Evolutionary Game Theory Explanation of ARCH Effects
, 2006
"... While ARCH/GARCH equations have been widely used to model financial market data, formal explanations for the sources of conditional volatility are scarce. This paper presents a model with the property that standard econometric tests detect ARCH/GARCH effects similar to those found in asset returns. ..."
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Cited by 3 (1 self)
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While ARCH/GARCH equations have been widely used to model financial market data, formal explanations for the sources of conditional volatility are scarce. This paper presents a model with the property that standard econometric tests detect ARCH/GARCH effects similar to those found in asset returns. We use evolutionary game theory to describe how agents endogenously switch among different forecasting strategies. The agents evaluate past forecast errors in the context of an optimizing model of asset pricing given heterogeneous agents. We show that the prospects for divergent expectations depend on the relative variances of fundamental and extraneous variables and on how aggressively agents are pursuing the optimal forecast. Divergent expectations are the driving force leading to the appearance of ARCH/GARCH in the data. JEL Classification: C22, C73, G12, D84 Keywords:ARCH, autoregressive conditional heteroskedasticity, evolutionary game theory, rational expectations,
The Underlying Dynamics of Credit Correlations
"... We propose a hybrid model of portfolio credit risk where the dynamics of the underlying latent variables is governed by a one factor GARCH process. The distinctive feature of such processes is that the longterm aggregate return distributions can substantially deviate from the asymptotic Gaussian li ..."
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Cited by 2 (0 self)
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We propose a hybrid model of portfolio credit risk where the dynamics of the underlying latent variables is governed by a one factor GARCH process. The distinctive feature of such processes is that the longterm aggregate return distributions can substantially deviate from the asymptotic Gaussian limit for very long horizons. We introduce the notion of correlation surface as a convenient tool for comparing portfolio credit loss generating models and pricing synthetic CDO tranches. Analyzing alternative specifications of the underlying dynamics, we conclude that the asymmetric models with TARCH volatility specification are the preferred choice for generating significant and persistent credit correlation skews. The characteristic dependence of the correlation skew on term to maturity and portfolio hazard rate in these models has a significant impact on both relative value analysis and risk management of CDO tranches. 1
A noarbitrage analysis of economic determinants of the credit spread term structure
, 2005
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© 2007 INFORMS Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning
"... informs ..."
STATISTICS AND FINANCE: LIVING ON THE “HEDGE”
 ICOTS7
, 2006
"... Statistics plays a leading role in finance. The explosive development of increasingly complex markets makes it more and more difficult for practitioners to correctly value financial asset. Statistical analysis has become a powerful tool for a better market valuation, taking a leading role in the dev ..."
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Statistics plays a leading role in finance. The explosive development of increasingly complex markets makes it more and more difficult for practitioners to correctly value financial asset. Statistical analysis has become a powerful tool for a better market valuation, taking a leading role in the development of new financial products that try to hedge the increasing amount of risks that an investor has to take. Statistics knowledge demand is steadily increasing in Hedge Funds, Investment Banking and Financial Institutions in general, where statistics students could developed a professional career. Finance can be seen as a way to motivate students on the applications of almost any statistical tool we would like to teach them, since we could always find an example where these techniques are put into practice.
Summary Short‐Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1
, 2009
"... It is often noted that energy prices are quite volatile, reflecting market participants’ adjustments to new information from physical energy markets and/or markets in energyrelated financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This pape ..."
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It is often noted that energy prices are quite volatile, reflecting market participants’ adjustments to new information from physical energy markets and/or markets in energyrelated financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the marketclearing process for risk transfer can be used to generate “price bands ” around observed futures prices for crude oil, natural gas, and other commodities. These bands provide a quantitative measure of uncertainty regarding the range in which markets expect prices to trade. The Energy Information Administration’s (EIA) monthly ShortTerm Energy Outlook (STEO) publishes “base case ” projections for a variety of energy prices that go out 12 to 24 months (every January the STEO forecast is extended through December of the following year). EIA has recognized that all price forecasts are highly uncertain and has described the uncertainty by identifying the market factors that may significantly move prices away from their expected paths, such as economic growth, Organization of Petroleum Exporting Countries (OPEC) behavior, geopolitical events, and hurricanes.
Summary Short‐Term Energy Outlook Supplement: Energy Price Volatility and Forecast Uncertainty 1
, 2009
"... It is often noted that energy prices are quite volatile, reflecting market participants’ adjustments to new information from physical energy markets and/or markets in energyrelated financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This pape ..."
Abstract
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It is often noted that energy prices are quite volatile, reflecting market participants’ adjustments to new information from physical energy markets and/or markets in energyrelated financial derivatives. Price volatility is an indication of the level of uncertainty, or risk, in the market. This paper describes how markets price risk and how the marketclearing process for risk transfer can be used to generate “price bands ” around observed futures prices for crude oil, natural gas, and other commodities. These bands provide a quantitative measure of uncertainty regarding the range in which markets expect prices to trade. The Energy Information Administration’s (EIA) monthly ShortTerm Energy Outlook (STEO) publishes “base case ” projections for a variety of energy prices that go out 12 to 24 months (every January the STEO forecast is extended through December of the following year). EIA has recognized that all price forecasts are highly uncertain and has described the uncertainty by identifying the market factors that may significantly move prices away from their expected paths, such as economic growth, Organization of Petroleum Exporting Countries (OPEC) behavior, geopolitical events, and hurricanes.
The Return to Australian Fine Wine and The Optimal Wine Portfolio
"... This article uses an adjacent period hedonic price regression model to estimate the return to storing Australian fine wine for the period 1989Q4 to 2000Q4. The results indicate that the return to storing Australian fine wine is comparable, if not superior, to the return of Bordeaux wine and Californ ..."
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This article uses an adjacent period hedonic price regression model to estimate the return to storing Australian fine wine for the period 1989Q4 to 2000Q4. The results indicate that the return to storing Australian fine wine is comparable, if not superior, to the return of Bordeaux wine and California Cabernet. The article then compares the investment performance of an equally weighted wine portfolio to the investment performance of two different meanvariance optimised quarterly rebalanced wine portfolios.
The Distribution of Risk Aversion ∗
, 2005
"... This paper develops a framework for deriving and inferring the distribution of relative risk aversion from financial markets. The theoretical constructions (i) rely on a fairly robust form of aggregating the marginal rate of substitution of individuals that are either long or short the marketindex, ..."
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This paper develops a framework for deriving and inferring the distribution of relative risk aversion from financial markets. The theoretical constructions (i) rely on a fairly robust form of aggregating the marginal rate of substitution of individuals that are either long or short the marketindex, and (ii) specifies a positive measure for the risk aversion coefficient capturing the feature that a proportion of the population possesses a distinct risk aversion. The implementation of the theoretical model reveals substantial heterogeneity in the coefficient of relative risk aversion. Our empirical approach supports the competitive markets paradigm that enforces positive skewness in the risk aversion distribution. The evidence also points to the presence of a risk aversion distribution that is characterized by heavy tails. We discuss the asset pricing implications of theory and empirical findings.