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Evaluating Interval Forecasts
 International Economic Review
, 1997
"... This paper is intended to address the deficiency by clearly defining what is meant by a "good" interval forecast, and describing how to test if a given interval forecast deserves the label "good". One of the motivations of Engle's (1982) classic paper was to form dynamic int ..."
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Cited by 364 (11 self)
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This paper is intended to address the deficiency by clearly defining what is meant by a "good" interval forecast, and describing how to test if a given interval forecast deserves the label "good". One of the motivations of Engle's (1982) classic paper was to form dynamic interval forecasts around point predictions. The insight was that the intervals should be narrow in tranquil times and wide in volatile times, so that the occurrences of observations outside the interval forecast would be spread out over the sample and not come in clusters. An interval forecast that 3 fails to account for higherorder dynamics may be correct on average (have correct unconditional coverage), but in any given period it will have incorrect conditional coverage characterized by clustered outliers. These concepts will be defined precisely below, and tests for correct conditional coverage are suggested. Chatfield (1993) emphasizes that model misspecification is a much more important source of poor interval forecasting than is simple estimation error. Thus, our testing criterion and the tests of this criterion are model free. In this regard, the approach taken here is similar to the one taken by Diebold and Mariano (1995). This paper can also be seen as establishing a formal framework for the ideas suggested in Granger, White and Kamstra (1989). Recently, financial market participants have shown increasing interest in interval forecasts as measures of uncertainty. Thus, we apply our methods to the interval forecasts provided by J.P. Morgan (1995). Furthermore, the socalled "ValueatRisk" measures suggested for risk measurement correspond to tail forecasts, i.e., onesided interval forecasts of portfolio returns. Lopez (1996) evaluates these types of forecasts applying the procedures develo...
Common Persistence in Conditional Variances
 ECONOMETRIC REVIEWS
, 1993
"... Since the introduction of the autoregressive conditional heteroskedastic (ARCH) model in Engle (1982), numerous applications of this modeling strategy have already appeared. A common finding in many of these studies with high frequency financial or monetary data concerns the presence of an approxima ..."
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Cited by 347 (20 self)
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Since the introduction of the autoregressive conditional heteroskedastic (ARCH) model in Engle (1982), numerous applications of this modeling strategy have already appeared. A common finding in many of these studies with high frequency financial or monetary data concerns the presence of an approximate unit root in the autoregressive polynomial in the univariate time series representation for the conditional second order moments of the process, as in the socalled integrated generalized ARCH (IGARCH) class of models proposed in Engle and Bollerslev (1986). In the IGARCH models shocks to the conditional variance are persistent, in the sense that they remain important for forecasts of all horizons. This idea is readily extended to a multivariate framework. Even though many time series may exhibit persistence in variance, it is likely that several different variables share the same common longrun component. In that situation, the variables are naturally defined to be copersistent in variance, and the copersistent linear combination is interpretable as a longrun relationship. Conditions for copersistence to occur in the multivariate linear GARCH model are presented. These conditions parallel the conditions for linear cointegration in the mean, as developed by Engle and Granger (1987). The presence of copersistence has important implications for asset pricing relationships and in optimal portfolio allocation decisions. An empirical example relating to the time series properties of nominal U.S. dollar exchange rates for the deutschemark and the British pound provides a simple illustration of the ideas.
MULTIVARIATE GARCH MODELS: A SURVEY
"... This paper surveys the most important developments in multivariate ARCHtype modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research. ..."
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Cited by 285 (10 self)
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This paper surveys the most important developments in multivariate ARCHtype modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research.
Structural Models of Corporate Bond Pricing: An Empirical Analysis
, 2003
"... This paper empirically tests five structural models of corporate bond pricing: those of Merton (1974), Geske (1977), Leland and Toft (1996), Longsta# and Schwartz (1995), and CollinDufresne and Goldstein (2001). We implement the models using a sample of 182 bond prices from firms with simple capita ..."
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Cited by 245 (6 self)
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This paper empirically tests five structural models of corporate bond pricing: those of Merton (1974), Geske (1977), Leland and Toft (1996), Longsta# and Schwartz (1995), and CollinDufresne and Goldstein (2001). We implement the models using a sample of 182 bond prices from firms with simple capital structures during the period 19861997. The conventional wisdom is that structural models do not generate spreads as high as those seen in the bond market, and true to expectations we find that the predicted spreads in our implementation of the Merton model are too low. However, most of the other structural models predict spreads that are too high on average. Nevertheless, accuracy is a problem, as the newer models tend to severely overstate the credit risk of firms with high leverage or volatility and yet su#er from a spread underprediction problem with safer bonds. The Leland and Toft model is an exception in that it overpredicts spreads on most bonds, particularly those with high coupons. More accurate structural models must avoid features that increase the credit risk on the riskier bonds while scarcely a#ecting the spreads of the safest bonds.
On the Detection and Estimation of Long Memory in Stochastic Volatility
, 1995
"... Recent studies have suggested that stock markets' volatility has a type of longrange dependence that is not appropriately described by the usual Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential GARCH (EGARCH) models. In this paper, different models for describing ..."
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Cited by 214 (6 self)
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Recent studies have suggested that stock markets' volatility has a type of longrange dependence that is not appropriately described by the usual Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential GARCH (EGARCH) models. In this paper, different models for describing this longrange dependence are examined and the properties of a LongMemory Stochastic Volatility (LMSV) model, constructed by incorporating an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process in a stochastic volatility scheme, are discussed. Strongly consistent estimators for the parameters of this LMSV model are obtained by maximizing the spectral likelihood. The distribution of the estimators is analyzed by means of a Monte Carlo study. The LMSV is applied to daily stock market returns providing an improved description of the volatility behavior. In order to assess the empirical relevance of this approach, tests for longmemory volatility are described and applied to an e...
Asset pricing with a factor ARCH covariance structure: Empirical estimates for Treasury bills, Revised manuscript
 Journal of Political Economy LXXXI
, 1989
"... In this paper we suggest using the FACTORARCH model as a parsimonious structure for the conditional covariance matrix of asset excess returns. This structure allows us to study the dynamic relationship between asset risk premia and volatilities in a multivariate system. One and two FACTORARCH mode ..."
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Cited by 202 (11 self)
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In this paper we suggest using the FACTORARCH model as a parsimonious structure for the conditional covariance matrix of asset excess returns. This structure allows us to study the dynamic relationship between asset risk premia and volatilities in a multivariate system. One and two FACTORARCH models are succussfully applied to pricing of Treasury bills. The results show stability over time, pass a variety of diagnostic tests, and compare favorably with previous empirical findings. 1.
Chaos and Nonlinear Dynamics: Application to Financial Markets
 Journal of Finance
, 1991
"... After the stock market crash of October 19, 1987, interest in nonlinear dynamics, especially deterministic chaotic dynamics, has increased in both the financial press and the academic literature. This has come about because the frequency of large moves in stock markets is greater than would be expec ..."
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Cited by 195 (3 self)
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After the stock market crash of October 19, 1987, interest in nonlinear dynamics, especially deterministic chaotic dynamics, has increased in both the financial press and the academic literature. This has come about because the frequency of large moves in stock markets is greater than would be expected
TimeChanged Lévy Processes and Option Pricing
, 2002
"... As is well known, the classic BlackScholes option pricing model assumes that returns follow Brownian motion. It is widely recognized that return processes differ from this benchmark in at least three important ways. First, asset prices jump, leading to nonnormal return innovations. Second, return ..."
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Cited by 189 (23 self)
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As is well known, the classic BlackScholes option pricing model assumes that returns follow Brownian motion. It is widely recognized that return processes differ from this benchmark in at least three important ways. First, asset prices jump, leading to nonnormal return innovations. Second, return volatilities vary stochastically over time. Third, returns and their volatilities are correlated, often negatively for equities. We propose that timechanged Lévy processes be used to simultaneously address these three facets of the underlying asset return process. We show that our framework encompasses almost all of the models proposed in the option pricing literature. Despite the generality of our approach, we show that it is straightforward to select and test a particular option pricing model through the use of characteristic function technology.
Stock Prices and Volume
, 1990
"... We undertake a comprehensive investigation of price and volume comovement using daily New York Stock Exchange data from 1928 to 1987. We adjust the data to take into account wellknown calendar effects and longrun trends. To describt tbe process, we use a seminonparametric estimate of the joint de ..."
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Cited by 189 (12 self)
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We undertake a comprehensive investigation of price and volume comovement using daily New York Stock Exchange data from 1928 to 1987. We adjust the data to take into account wellknown calendar effects and longrun trends. To describt tbe process, we use a seminonparametric estimate of the joint density of current price change and volume conditional on past price changes and volume. Four empirical regularities are found: 1) positive correlation between conditional volatility and volume, 2) large price movements are followed by high volume, 3) conditioning on lagged volume substantially attenuates the "leverage " effect, and 4) after conditioning on lagged volume, there is a positive risk/return relation.