Please cite the publication as :
Sonnier,
G.,
and
K.
Train,
"Mixed Logit with Bounded Distributions of Correlated Partworths ",
Applications of Simulation Methods in Environmental Resource Economics
, Chapter 7.
Please cite the companion website as :
Sonnier, G., and K. Train, "Mixed Logit with Bounded Distributions of Correlated Partworths ", RunMyCode companion website, http://www.execandshare.org/CompanionSite/Site73
Variable/Parameters  Description, constraint  Comments 

People and choices  This (T,3) matrix must contain one row of data for each alternative in each choice situation for each person. The rows are grouped by person and by choice situation faced by each person. The 1st column identified the decisionmaker (numbered from 1 to NP, ascending order). The 2nd column identified the choice situation (numbered from 1 to NCS, ascending order for each individual). The 3rd column identified the chosen alternative (1 for chosen, 0 otherwise; only one choice is possible for each choice situation).  
Random Coef. Variables  Matrix (T,U) including the variables that enter the model with random coefficients. Leave it empty if there are no variables with random coefficients in the model. Note1: the distribution for the coefficient of each variable must be given (input 'Distribution'). Note2: if a discrete variable were to be included, a dummy variable for each modality should be constructed, but one of them must not be included in the model so as to avoid multicolinearity.  
Fixed Coef. Variables  Matrix (T,V) including the variables that enter the model with fixed coefficients. Leave it empty if there are no variables with random coefficients in the model.  
Distribution (random coef.)  A (R,1) vector with the choice of the distribution for each random coefficient (from 1 to 5). Possibilities: 1. Normal; 2. lognormal, 3. truncated normal (with the share below zero massed at zero), 4. Jonson's S_B, 5. normal with zero mean(for error components.)  
Bayesian Prior (random coef.)  This (T,1) vector contains the means of the distribution of the underlying normal for each random coefficient.  
Number of people  Number of people (decisionmakers) in the dataset.  
No. choice situations  Number of choice situations in dataset. This is the number faced by all the people combined.  
Alternatives  Total number of alternatives faced by all people in a choice situation combined.  
Correlation  Set FULLCV=1 to estimate covariance among all random coefficients. FULLCV=0 for no covariances.  
No. Burnin Iterations  Number of iterations to make prior to retaining draws (i.e., length of burnin).  
No. Retained Draws  Number of draws to retain after burnin.  
No. Draws  Number of draws from the normal distribution N(Ahat,Dhat) to use in simulating the estimated distribution of random coefficients.  
No. draws per person  Number of draws per person to use in calculating the simulated loglikelihood value at Ahat, Dhat, and Fhat, where A is the mean vector and D is the covariance matrix of the normal distribution that generates the latent terms. Besides, F are the fixed coefficients.  
Save draws  To save the draws of A (the mean vector) and D (the covariance matrix) for the normally distributed latent terms associated with the random coefficients as well as the draws F for the fixed coefficients to a file, set KEEPMN=1. If not, set KEEPMN=0.  
Save means of draws  To calculate and save the means of the NEREP draws of the individuallevel random coefficients set WANTINDC=1. If not, set WANTINDC=0. 
Variable/Parameters  Description  Visualisation 

People and choices  100 individus (1st column) face 1484 alternatives (2nd column) for choosing a vehicle.  
Random Coef. Variables  The variables with random coefficients are: 1. Negative of Price in tens of thousands of dollars 2. Negative of Operating cost in dollars per month 3. Range in hundreds of miles (0 if not electric) 4. Electric (1/0) 5. Hybrid (1/0) (The third dummy, Gas (1/0), has not been included to avoid multicolinearity).  
Fixed Coef. Variables  Variables with fixed coefficients: 1. High performance (1/0) 2. Medium or high performance (1/0)  
Distribution (random coef.)  The lognormal distribution is chosen for the first 3 coefficients, while the normal one is used for the last two.  
Bayesian Prior (random coef.)  The means of the distribution of the underlying normal for each random coefficient used in the demo data.  
Number of people  Number of people (decisionmakers) in dataset.  
No. choice situations  Number of choice situations in dataset. This is the number faced by all the people combined.  
Alternatives  Total number of alternatives faced by all people in all choice situations combined.  
Correlation  Estimate covariance among all random coefficients.  
No. Burnin Iterations  Number of iterations made prior to retaining draws.  
No. Retained Draws  Number of draws to retain after burnin .  
No. Draws  Number of draws from N(Ahat,Dhat) to use in simulating the estimated dist of random coefficients.  
No. draws per person  Number of draws per person to use in calculating the simulated loglikelihood value at Ahat, Dhat, and Fhat.  
Save draws  Save the draws of A, D, and F to a file by setting KEEPMN=1.  
Save means of draws  Calculate and save the means of the NEREP draws of the individuallevel random coefficients by setting NTINDC=1. 
Computing Date  Status  Actions 

Kenneth Train
University of California, Berkeley
United States
Kenneth Train also created these companion sites
Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level
Abstract
Mixed logit models, also called randomparameters or errorcomponents logit, are a generalization of standard logit that do not exhibit the restrictive "independence from irrelevant alternatives" property and explicitly account for correlations in unobserved utility over repeated choices by each customer. Mixed logits are estimated for households’ choices of appliances under utilitysponsored programs that offer rebates or loans on highefficiency appliances.
Train,
K.,
"Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level",
The Review of Economics and Statistics, 80, 647657.
Authors:
Revelt
Train
Coders:
Train
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Calvet,
E.
L.,
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A.
J.
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Calvet
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Francq,
C.,
and
J.
Zakoian,
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Francq
Zakoian
Coders:
Francq
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8
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Abstract
Financial returns typically display heavy tails and some skewness,
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including risk measures such as the expected shortfall. Here, using
a recent generalization of the asymmetric Studentt distribution to
allow separate parameters to control skewness and the thickness of
each tail, we fit daily financial returns and forecast expected shortfall
for the S&P 500 index and a number of individual company stocks;
the generalized distribution is used for the standardized innovations
in a nonlinear, asymmetric GARCHtype model. The results provide
empirical evidence for the usefulness of the generalized distribution in
improving prediction of downside market risk of financial assets.
Galbraith,
W.
J.,
and
D.
Zhu,
"Forcasting Expected Shortfall with a Generalized Asymetric Studentt Distribution",
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Coders:
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Abstract
This paper presents a validation framework for collateral requirements or margins
on a derivatives exchange. It can be used by investors, risk managers, and regulators
to check the accuracy of a margining system. The statistical tests presented in this
study are based either on the number, frequency, magnitude, or timing of margin
exceedances, which are de
ned as situations in which the trading loss of a market
participant exceeds his or her margin. We also propose an original way to validate
globally the margining system by aggregating individual backtesting statistics ob
tained for each market participant.
Hurlin,
C.,
and
C.
Perignon,
"Margin Backtesting",
University of Orleans, HEC Paris.
Authors:
Hurlin
Perignon
Coders:
Hurlin
Perignon Last update
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Testing Interval Forecasts: A GMMBased Approach
Abstract
This paper proposes a new evaluation framework of interval forecasts. Our model free test
can be used to evaluate intervals forecasts and/or High Density Region, potentially discontinuous and/or asymmetric. Using simple Jstatistic based on the moments defined by the orthonormal polynomials associated with the Binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypothesis. Third, MonteCarlo simulations show that for realistic sample sizes, our GMM test outperforms traditional LR test. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. The empirical application for financial returns confirms that using a GMM test leads to major consequences for the expost evaluation of interval forecasts produced by linear versus non linear models.
Dumitrescu,
E.,
C.
Hurlin,
"Testing Interval Forecasts: A GMMBased Approach",
Journal of Forecasting, .
Authors:
Dumitrescu
Hurlin Madkour
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Dumitrescu
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A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR
Abstract
In this paper, we propose a theoretical and empirical comparison of two popular systemic risk measures  Marginal Expected Shortfall (MES) and Delta Conditional Value at Risk (ΔCoVaR)  that can be estimated using publicly available data. First, we assume that the timevarying correlation completely captures the dependence between firm and market returns. Under this assumption, we derive three analytical results: (i) we show that the MES corresponds to the product of the conditional ES of market returns and the timevarying beta of this institution, (ii) we give an analytical expression of the ΔCoVaR and show that the CoVaR corresponds to the product of the VaR of the firm's returns and the timevarying linear projection coefficient of the market returns on the firm's returns and (iii) we derive the ratio of the MES to the ΔCoVaR. Second, we relax this assumption and propose an empirical comparison for a panel of 61 US financial institutions over the period from January 2000 to December 2010. For each measure, we propose a crosssectional analysis, a timeseries comparison and rankings analysis of these institutions based on the two measures.
Benoit,
S.,
G.
Colletaz,
C.
Hurlin,
and
C.
Perignon,
"A Theoretical and Empirical Comparison of Systemic Risk Measures: MES versus CoVaR",
SSRN.
Authors:
Benoit
Colletaz Hurlin Perignon
Coders:
Benoit
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398

Is Public Capital Really Productive? A Methodological Reappraisal
Abstract
We present an evaluation of the main empirical approaches used in the literature to estimate the contribution of public capital stock to growth and private factors' productivity. Based on a simple stochastic general equilibrium model, built as to reproduce the main longrun relations observed in US postwar historical data, we show that the production function approach may not be reliable to estimate this contribution. Our analysis reveals that this approach largely overestimates the public capital elasticity, given the presence of a common stochastic trend shared by all nonstationary inputs.
Minea,
A.,
and
C.
Hurlin,
"Is Public Capital Really Productive? A Methodological Reappraisal",
University of Orleans.
Authors:
Minea
Hurlin
Coders:
Minea
Hurlin Last update
09/10/2012
Ranking
43
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3
Visits
42

Are Public Investment Efficient in Creating Capital Stocks in Developing Countries? Estimates of Government Net Capital Stocks for 26 Developing Countries, 19702002
Abstract
In many poor countries, the problem is not that governments do not invest, but that these investments do not create productive capital. So, the cost of public investments does not correspond to the value of the capital stocks. In this paper, we propose an original non parametric approach to evaluate the efficiency function that links variations (net of depreciation) of stocks to public investments. We consider four sectors (electricity, telecommunications, roads and railways) of two Latin American countries (Mexico and Colombia). We show that there is a large discrepancy between the amount of investments and the value of increases in stocks.
Arestoff,
F.,
and
C.
Hurlin,
"Are Public Investment Efficient in Creating Capital Stocks in Developing Countries? Estimates of Government Net Capital Stocks for 26 Developing Countries, 19702002 ",
Economics Bulletin, 30, 15151531.
Authors:
Arestoff
Hurlin
Coders:
Arestoff
Hurlin Last update
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11
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Testing for Granger Noncausality in Heterogeneous Panels
Abstract
This paper proposes a very simple test of Granger (1969) noncausality for hetero
geneous panel data models. Our test statistic is based on the individual Wald statistics
of Granger non causality averaged across the crosssection units. First, this statistic is
shown to converge sequentially to a standard normal distribution. Second, the semi
asymptotic distribution of the average statistic is characterized for a fixed T sample.
A standardized statistic based on an approximation of the moments of Wald statistics
is hence proposed. Third, Monte Carlo experiments show that our standardized panel
statistics have very good small sample properties, even in the presence of crosssectional
dependence.
Dumitrescu,
E.,
and
C.
Hurlin,
"Testing for Granger Noncausality in Heterogeneous Panels",
Economic Modelling, Forthcoming.
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Dumitrescu
Hurlin
Coders:
Dumitrescu
Hurlin Last update
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502

Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM, and Portfolio Sorts
Abstract
Many theories in finance imply monotonic patterns in expected returns and other financial variables. The liquidity preference hypothesis predicts higher expected returns for bonds with longer times to maturity; the Capital Asset Pricing Model(CAPM)implies higher expected returns for stocks with higher betas; and standard asset pricing models imply that the pricing kernel is declining in market returns. The full set of implications of monotonicity is generally not exploited in empirical work, however. This paper proposes new and simple ways to test for monotonicity in financial variables and compares the proposed tests with extant alternatives such as ttests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.
Patton,
J.
A.,
and
A.
Timmermann,
"Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM, and Portfolio Sorts",
Journal of Financial Economics, 98, 605625.
Authors:
Patton
Timmermann
Coders:
Patton
Timmermann Last update
11/17/2012
Ranking
63
Runs
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Visits
116

Maximum Likelihood Estimation of Discretely Sampled Diffusions: A ClosedForm Approximation Approach
Abstract
When a continuoustime diffusion is observed only at discrete dates, in most cases the transition distribution and hence the likelihood function of the observation is not explicitely computable. Using Hermite polynomials, I construct an explicit sequences of closedform functions and show that it converges to the true (but unknown) likelihood function. I document that the approximation is very accurate and prove that maximizing the sequence results in an estimator that converges to the true maximum likelihood estimator and shares its asymptotic properties. Monte Carlo evidence reveals that this method outperforms other approximation schemes in situations relevant for financial models.
AïtSahalia,
Y.,
"Maximum Likelihood Estimation of Discretely Sampled Diffusions: A ClosedForm Approximation Approach",
Econometrica, 70, 223262.
Authors:
AïtSahalia
Coders:
AïtSahalia
Last update
10/29/2014
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2
Runs
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Visits
621

Adaptive Estimation of Vector Autoregressive Models with TimeVarying Variance: Application to Testing Linear Causality in Mean
Abstract
Linear Vector AutoRegressive (VAR) models where the innovations could be unconditionally heteroscedastic and serially dependent are considered. The volatility structure is deterministic and quite general, including breaks or trending variances as special cases. In this framework we propose Ordinary Least Squares (OLS), Generalized Least Squares (GLS) and Adaptive Least Squares (ALS) procedures.
The GLS estimator requires the knowledge of the timevarying variance structure while in the ALS approach the unknown variance
is estimated by kernel smoothing with the outer product of the OLS residuals vectors. Different bandwidths for the different cells
of the timevarying variance matrix are also allowed. We derive the asymptotic distribution of the proposed estimators for the VAR
model coefficients and compare their properties. In particular we show that the ALS estimator is asymptotically equivalent to the
infeasible GLS estimator. This asymptotic equivalence is obtained uniformly with respect to the bandwidth(s) in a given range and
hence justifies datadriven bandwidth rules. Using these results we build Wald tests for the linear Granger causality in mean which
are adapted to VAR processes driven by errors with a non stationary volatility. It is also shown that the commonly used standard Wald
test for the linear Granger causality in mean is potentially unreliable in our framework (incorrect level and lower asymptotic power).
Monte Carlo and realdata experiments illustrate the use of the different estimation approaches for the analysis of VAR models with timevarying variance innovations.
Raïssi,
H.,
"Adaptive Estimation of Vector Autoregressive Models with TimeVarying Variance: Application to Testing Linear Causality in Mean",
IRMARINSA and CREST ENSAI.
Authors:
Patilea
Coders:
Raïssi
Last update
10/08/2012
Ranking
58
Runs
9
Visits
169

CopulaBased Models for Financial Time Series
Abstract
This paper presents an overview of the literature on applications of copulas in the modelling of financial time series. Copulas have been used both in multivariate time series analysis, where they are used to charaterise the (conditional) crosssectional dependence between individual time series, and in univariate time series analysis, where they are used to characterise the dependence between a sequence of observations of a scalar time series process. The paper includes a broad, brief, review of the many applications of copulas in finance and economics.
Patton,
J.
A.,
"CopulaBased Models for Financial Time Series",
Handbook of Financial Time Series, Springer Verlag, .
Authors:
Patton
Coders:
Patton
Last update
10/08/2012
Ranking
3
Runs
38
Visits
572

Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level
Abstract
Mixed logit models, also called randomparameters or errorcomponents logit, are a generalization of standard logit that do not exhibit the restrictive "independence from irrelevant alternatives" property and explicitly account for correlations in unobserved utility over repeated choices by each customer. Mixed logits are estimated for households’ choices of appliances under utilitysponsored programs that offer rebates or loans on highefficiency appliances.
Train,
K.,
"Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level",
The Review of Economics and Statistics, 80, 647657.
Authors:
Revelt
Train
Coders:
Train
Last update
06/05/2012
Ranking
7
Runs
6
Visits
243

Network Effects and Infrastructure Productivity in Developing Countries
Abstract
This paper proposes to investigate the threshold effects of the productivity of infrastructure investment in developing countries within a panel data framework. Various speci.cations of an augmented production function that allow for endogenous thresholds are considered. The overwhelming outcome is the presence of strong threshold effects in the relationship between output and private and public inputs. Whatever the transition mechanism used, the testing procedures lead to strong rejection of the linearity of this relationship. In particular, the productivity of infrastructure investment generally exhibits some network effects. When the available stock of infrastructure is very low, investment in this sector has the same productivity as noninfrastructure investment. On the contrary, when a minimumnetwork is available, the marginal productivity of infrastructure investment is generally largely greater than the productivity of other investments. Finally, when the main network is achieved, its marginal productivity becomes similar to the productivity of other investment.
Candelon,
B.,
G.
Colletaz,
and
C.
Hurlin,
"Network Effects and Infrastructure Productivity in Developing Countries",
Maastricht University.
Authors:
Candelon
Colletaz Hurlin
Coders:
Candelon
Colletaz Hurlin Last update
03/14/2013
Ranking
35
Runs
414
Visits
431

Threshold Effects of the Public Capital Productivity : An International Panel Smooth Transition Approach
Abstract
Using a non linear panel data model we examine the threshold effects in the productivity of the public capital stocks for a panel of 21 OECD countries observed over 19652001. Using the socalled "augmented production function" approach, we estimate various specifications of a Panel Smooth Threshold Regression (PSTR) model recently developed by Gonzalez, Teräsvirta and Van Dijk (2004). One of our main results is the existence of strong threshold effects in the relationship between output and private and public inputs : whatever the transition mechanism specified, tests strongly reject the linearity assumption. Moreover this model allows crosscountry heterogeneity and time instability of the productivity without specification of an exante classification over individuals. Consequently it is posible to give estimates of productivity coefficients for both private and public capital stocks at any time and for each countries in the sample. Finally we proposed estimates of individual time varying elasticities that are much more reasonable than those previously published.
Hurlin,
C.,
"Threshold Effects of the Public Capital Productivity : An International Panel Smooth Transition Approach",
University of Orléans.
Authors:
Colletaz
Hurlin
Coders:
Hurlin
Last update
07/22/2014
Ranking
31
Runs
2290
Visits
677

Evaluating Interval Forecasts
Abstract
A complete theory for evaluating interval forecasts has not been worked out to date. Most of the literature implicitly assumes homoskedastic errors even when this is clearly violated and proceed by merely testing for correct unconditional coverage. Consequently, the author sets out to build a consistent framework for conditional interval forecast evaluation, which is crucial when higherorder moment dynamics are present. The new methodology is demonstrated in an application to the exchange rate forecasting procedures advocated in risk management.
Hurlin,
C.,
C.
Perignon,
"Evaluating Interval Forecasts",
International Economic Review, 39, 841862.
Authors:
Christoffersen
Coders:
Hurlin
Perignon Last update
03/09/2012
Ranking
32
Runs
57
Visits
167

Backtesting ValueatRisk: A DurationBased Approach
Abstract
Financial risk model evaluation or backtesting is a key part of the internal model’s
approach to market risk management as laid out by the Basle Committee on
Banking Supervision. However, existing backtesting methods have relatively low
power in realistic small sample settings. Our contribution is the exploration of
new tools for backtesting based on the duration of days between the violations of
the ValueatRisk. Our Monte Carlo results show that in realistic situations, the
new durationbased tests have considerably better power properties than the
previously suggested tests.
Hurlin,
C.,
and
C.
Perignon,
"Backtesting ValueatRisk: A DurationBased Approach",
Journal of Financial Econometrics, 2, 84108.
Authors:
Pelletier
Christoffersen
Coders:
Hurlin
Perignon Last update
07/23/2012
Ranking
26
Runs
17
Visits
207

Backtesting ValueatRisk Accuracy: A Simple New Test
Abstract
This paper proposes a new test of valueatrisk (VAR) validation. Our test exploits the idea that the sequence of VAR violations (hit function) – taking value 1  α if there is a violation, and α otherwise – for a nominal coverage rate α verifies the properties of a martingale difference if the model used to quantify risk is adequate (Berkowitz et al., 2005). More precisely, we use the multivariate portmanteau statistic of Li and McLeod (1981), an extension to
the multivariate framework of the test of Box and Pierce (1970), to jointly test the absence of autocorrelation in the vector of hit sequences for various coverage rates considered relevant for the management of extreme risks. We show that this shift to a multivariate dimension appreciably improves the power properties of the VAR validation test for reasonable sample sizes.
Hurlin,
C.,
and
S.
Tokpavi,
"Backtesting ValueatRisk Accuracy: A Simple New Test",
Journal of Risk, 9, 1937.
Authors:
Hurlin
Tokpavi
Coders:
Hurlin
Tokpavi Last update
03/13/2012
Ranking
49
Runs
2
Visits
240

How To Evaluate an Early Warning System? Towards a unified Statistical Framework for Assessing Financial Crises Forecasting Methods
Abstract
This paper proposes an original and unified toolbox to evaluate financial crisis Early
Warning Systems (EWS). It presents four main advantages. First, it is a modelfree method
which can be used to asses the forecasts issued from different EWS (probit, logit, markov
switching models, or combinations of models). Second, this toolbox can be applied to any
type of crisis EWS (currency, banking, sovereign debt, etc.). Third, it does not only provide various criteria to evaluate the (absolute) validity of EWS forecasts but also proposes some tests to compare the relative performance of alternative EWS. Fourth, our toolbox can be used to evaluate both insample and outofsample forecasts. Applied to a logit model for twelve emerging countries we show that the yield spread is a key variable for predicting currency crises exclusively for SouthAsian countries. Besides, the optimal cutoff correctly allows us to identify now on average more than 2/3 of the crisis and calm periods.
Candelon,
B.,
E.
Dumitrescu,
and
C.
Hurlin,
"How To Evaluate an Early Warning System? Towards a unified Statistical Framework for Assessing Financial Crises Forecasting Methods",
IMF Economic Review, 60.
Authors:
Candelon
Dumitrescu Hurlin
Coders:
Candelon
Dumitrescu Hurlin Last update
07/23/2012
Ranking
34
Runs
24
Visits
269

Forecasting Realized Volatility Using a Nonnegative Semiparametric Model
Abstract
This paper introduces a parsimonious and yet exible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of BarndorNielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the distributional form of its error component is left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Asymptotic properties are established for the new estimation method. Simulation studies validate the new estimation method. The outofsample performance of the proposed model is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential smoothing method, a linear AR(1) model, a loglinear AR(1) model, and two longmemory ARFIMA models. Various loss functions are utilized to evaluate the predictive accuracy of the alternative methods. It is found that the new model generally produces highly competitive forecasts.
Preve,
D.,
J.
Yu,
"Forecasting Realized Volatility Using a Nonnegative Semiparametric Model",
Uppsala University.
Authors:
Eriksson
Preve Yu
Coders:
Preve
Yu Last update
06/06/2012
Ranking
64
Runs
19
Visits
114

ValueatRisk (Chapter 5: Computing VaR)
Abstract
Book description: To accommodate sweeping global economic changes, the risk management field has evolved substantially since the first edition of Value at Risk, making this revised edition a must. Updates include a new chapter on liquidity risk, information on the latest risk instruments and the expanded derivatives market, recent developments in Monte Carlo methods, and more. Value at Risk will help professional risk managers understand, and operate within, today’s dynamic new risk environment.
Hurlin,
C.,
C.
Perignon,
"ValueatRisk (Chapter 5: Computing VaR)",
MacGrawHill, Third Edition.
Authors:
Jorion
Coders:
Hurlin
Perignon Last update
03/19/2012
Ranking
44
Runs
63
Visits
328

The pernicious effects of contaminated data in risk management
Abstract
Banks hold capital to guard against unexpected surges in losses and long freezes in financial markets. The minimum level of capital is set by banking regulators as a function of the banks’ own estimates of their risk exposures. As a result, a great challenge for both banks and regulators is to validate internal risk models. We show that a large fraction of US and international banks uses contaminated data when testing their models. In particular, most banks validate their market risk model using profitandloss (P/L) data that include fees and commissions and intraday trading revenues. This practice is inconsistent with the definition of the employed market risk measure. Using both bank data and simulations, we find that data contamination has dramatic implications for model validation and can lead to the acceptance of misspecified risk models. Moreover, our estimates suggest that the use of contaminated data can significantly reduce (marketrisk induced) regulatory capital.
Fresard,
L.,
C.
Perignon,
and
A.
Wilhelmsson,
"The pernicious effects of contaminated data in risk management",
Journal of Banking and Finance, 35.
Authors:
Fresard
Perignon Wilhelmsson
Coders:
Fresard
Perignon Wilhelmsson Last update
11/23/2012
Ranking
9999
Runs
N.A.
Visits
42

Techniques for Verifying the Accuracy of Risk Management Models
Abstract
Risk exposures are typically quantified in terms of a "Value at Risk" (VaR) estimate. A VaR estimate corresponds to a specific critical value of a portfolio's potential oneday profit and loss probability distribution. Given their function both as internal risk management tools and as potential regulatory measures of risk exposure, it is important to quantify the accuracy of an institution's VaR estimates. This study shows that the formal statistical procedures that would typically be used in performancebased VaR verification tests require large samples to produce a reliable assessment of a model's accuracy in predicting the size and likelihood of very low probability events. Verification test statistics based on historical trading profits and losses have very poor power in small samples, so it does not appear possible for a bank or its supervisor to verify the accuracy of a VaR estimate unless many years of performance data are available. Historical simulationbased verification test statistics also require long samples to generate accurate results: Estimates of 0.01 critical values exhibit substantial errors even in samples as large as ten years of daily data.
Hurlin,
C.,
C.
Perignon,
"Techniques for Verifying the Accuracy of Risk Management Models",
Journal of Derivatives, 3, 7384.
Authors:
Kupiec
Coders:
Hurlin
Perignon Last update
04/17/2012
Ranking
57
Runs
26
Visits
339

Outliers and GARCH Models in Financial Data
Abstract
We propose to extend the additive outlier (AO) identification procedure developed by Franses and Ghijsels(Franses, P.H., Ghijsels, H., 1999. Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 15, 1–9) to take into account the innovative outliers (IOs) in a GARCH model. We apply it to three daily stock market indexes and examine the effects of outliers on the diagnostics of normality.
Charles,
A.,
and
O.
Darné,
D.
Banulescu,
E.
Dumitrescu,
"Outliers and GARCH Models in Financial Data",
Economics Letters, 86, 347352.
Authors:
Charles
Darné
Coders:
Charles
Darné Banulescu Dumitrescu Last update
06/22/2012
Ranking
15
Runs
61
Visits
238

Extracting Factors from Heteroskedastic Asset Returns
Abstract
This paper proposes an alternative to the asymptotic principal components procedure
of Connor and Korajczyk (Journal of Financial Economics, 1986) that is robust to time
series heteroskedasticity in the factor model residuals. The new method is simple to use
and requires no assumptions stronger than those made by Connor and Korajczyk. It is
demonstrated through simulations and analysis of actual stock market data that
allowing heteroskedasticity sometimes improves the quality of the extracted factors
quite dramatically. Over the period from 1989 to 1993, for example, a single factor
extracted using the Connor and Korajczyk method explains only 8.2% of the variation
of the CRSP valueweighted index, while the factor extracted allowing heteroskedasticity
explains 57.3%. Accounting for heteroskedasticity is also important for tests of
the APT, with pvalues sometimes depending strongly on the factor extraction method
used.
Jones,
S.
C.,
"Extracting Factors from Heteroskedastic Asset Returns",
Journal of Financial Economics, 62, 293325.
Authors:
Jones
Coders:
Jones
Last update
11/17/2012
Ranking
30
Runs
17
Visits
81

Currency Crises Early Warning Systems: why they should be Dynamic
Abstract
This paper introduces a new generation of Early Warning Systems (EWS) which takes into
account the dynamics, i.e. the persistence in the binary crisis indicator. We elaborate on
Kauppi and Saikonnen (2008), which allows to consider several dynamic specifications by re
lying on an exact maximum likelihood estimation method. Applied so as to predict currency
crises for fifteen countries, this new EWS turns out to exhibit significantly better predic
tive abilities than the existing models both within and out of the sample, thus vindicating
dynamic models in the quest for optimal EWS.
Candelon,
B.,
E.
Dumitrescu,
and
C.
Hurlin,
"Currency Crises Early Warning Systems: why they should be Dynamic",
Maastricht University.
Authors:
Candelon
Dumitrescu Hurlin
Coders:
Candelon
Dumitrescu Hurlin Last update
06/04/2012
Ranking
45
Runs
64
Visits
112

Modeling State Credit Risks in Illinois and Indiana
Abstract
I use an opensource budgetsimulation model to evaluate Illinois’s credit risk and to compare it to that of Indiana, a neighboring state generally believed to have better fiscal management. Based on a review of the history and theory of state credit performance, I assume that a state will default if the aggregate of its interest and pension costs reaches 30 percent of total revenues. In Illinois, this ratio is currently 10 percent, compared to 4 percent in Indiana. My analysis finds that neither state will reach the critical threshold in the next few years under any reasonable economic scenario, suggesting no material default risk. Over the longer term, Illinois has some chance of reaching the default threshold, but it would likely be able to take policy actions to lower the ratio before then. If market participants accept my finding that Illinois does not have material default risk, Illinois’s bond yields willfall, yielding cost savings for taxpayers as the state rolls over its debt.
Joffe,
D.
M.,
"Modeling State Credit Risks in Illinois and Indiana",
Mercatus Center.
Authors:
Joffe
Coders:
Joffe
Last update
08/01/2013
Ranking
9999
Runs
N.A.
Visits
N.A.

Pitfalls in backtesting Historical Simulation VaR models
Abstract
Abstract
Historical Simulation (HS) and its variant, the Filtered Historical Simulation (FHS), are the most popular ValueatRisk forecast methods at commercial banks. These forecast methods are traditionally evaluated by means of the unconditional backtest. This paper formally shows that the unconditional backtest is always inconsistent for backtesting HS and FHS models, with a power function that can be even smaller than the nominal level in large samples. Our findings have fundamental implications in the determination of market risk capital requirements, and also explain Monte Carlo and empirical findings in previous studies. We also propose a datadriven weighted backtest with good power properties to evaluate HS and FHS forecasts. A Monte Carlo study and an empirical application with three US stocks confirm our theoretical findings. The empirical application shows that multiplication factors computed under the current regulatory framework are downward biased, as they inherit the inconsistency of the unconditional backtest.
Escanciano,
J.,
and
P.
Pei,
"Pitfalls in backtesting Historical Simulation VaR models",
Journal of Banking and Finance, 36, 22332244.
Authors:
Escanciano
Pei
Coders:
Escanciano
Pei Last update
02/22/2013
Ranking
9999
Runs
N.A.
Visits
32

A Generalized Asymmetric Studentt Distribution with Application to Financial Econometrics
Abstract
This paper proposes a new class of asymmetric Studentt (AST) distributions, and investigates its properties, gives procedures for estimation, and indicates applications in financial econometrics. We derive analytical expressions for the cdf, quantile function, moments, and quantities useful in financial econometric applications such as the Expected Shortfall. A stochastic representation of the distribution is also given. Although the AST density does not satisfy the usual regularity conditions for maximum likelihood estimation, we establish consistency, asymptotic normality and efficiency of ML estimators and derive an explicit analytical expression for the asymptotic covariance matrix. A Monte Carlo study indicates generally good finitesample conformity with these asymptotic properties.
Colletaz,
G.,
"A Generalized Asymmetric Studentt Distribution with Application to Financial Econometrics",
Journal of Econometrics, 157, 297305.
Authors:
Zhu
Galbraith
Coders:
Colletaz
Last update
05/05/2012
Ranking
38
Runs
6
Visits
95

Determining the Number of Factors in Approximate Factors Models
Abstract
In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The theory is developed under the framework of large crosssections (N) and large time dimensions (T). No restriction is imposed on the relation between N and T. Simulations show that the proposed criteria have good finite sample properties in many configurations of the panel data encountered in practice.
Hurlin,
C.,
"Determining the Number of Factors in Approximate Factors Models",
Econometrica, 70, 191221.
Authors:
Bai
Ng
Coders:
Hurlin
Last update
01/29/2013
Ranking
39
Runs
66
Visits
230

Unit Root Tests in Panel Data: Asymptotic and FiniteSample Properties
Abstract
We consider pooling crosssection time series data for testing the unit root hypothesis. The degree of persistence in individual regression error, the intercept and trend coefficient are allowed to vary freely across individuals. As both the crosssection and time series dimensions of the panel grow large, the pooled tstatistic has a limiting normal distribution that depends on the regression specification but is free from nuisance parameters. Monte Carlo simulations indicate that the asymptotic results provide a good approximation to the test statistics in panels of moderate size, and that the power of the panelbased unit root test is dramatically higher, compared to performing a separate unitroottest for each individual time series.
Hurlin,
C.,
"Unit Root Tests in Panel Data: Asymptotic and FiniteSample Properties",
Journal of Econometrics, 108, 124.
Authors:
Levin
Lin Chu
Coders:
Hurlin
Last update
06/28/2012
Ranking
37
Runs
220
Visits
322

Maximum Likelihood Methods for Models of Markets in Disequilibrium
Authors:
Maddala
Nelson
Coders:
Hurlin
Last update
02/15/2013
Ranking
50
Runs
85
Visits
391

A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test
Abstract
The panel data unit root test suggested by Levin and Lin (LL) has been widely used in several applications, notably in papers on tests of the purchasing power parity hypothesis. This test is based on a very restrictive hypothesis which is rarely ever of interest in practice. The Im–Pesaran–Shin (IPS) test relaxes the restrictive assumption of the LL test. This paper argues that although the IPS test has been offered as a generalization of the LL test, it is best viewed as a test for summarizing the evidence from a number of independent tests of the sample hypothesis. This problem has a long statistical history going back to R. A. Fisher. This paper suggests the Fisher test as a panel data unit root test, compares it with the LL and IPS tests, and the Bonferroni bounds test which is valid for correlated tests. Overall, the evidence points to the Fisher test with bootstrapbased critical values as the preferred choice. We also suggest the use of the Fisher test for testing stationarity as the null and also in testing for cointegration in panel data.
Hurlin,
C.,
"A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test",
Oxford Bulletin of Economics and Statistics, 61, 631652.
Authors:
Maddala
Wu
Coders:
Hurlin
Last update
10/08/2012
Ranking
59
Runs
217
Visits
129

Unit Root Tests for Panel Data
Abstract
This paper develops unit root tests for panel data. These tests are devised under more general assumptions than the tests previously proposed. First, the number of groups in the panel data is assumed to be either finite or infinite. Second, each group is assumed to have different types of nonstochastic and stochastic components. Third, the time series spans for the groups are assumed to be all different. Fourth, the alternative where some groups have a unit root and others do not can be dealt with by the tests. The tests can also be used for the null of stationarity and for cointegration, once relevant changes are made in the model, hypotheses, assumptions and underlying tests. The main idea for our unit root tests is to combine pvalues from a unit root test applied to each group in the panel data. Combining pvalues to formulate tests is a common practice in metaanalysis. This paper also reports the finite sample performance of our combination unit root tests and Im et al.'s [Mimeo (1995)] tbar test. The results show that most of the combination tests are more powerful than the tbar test in finite samples. Application of the combination unit root tests to the postBretton Woods US real exchange rate data provides some evidence in favor of the PPP hypothesis.
Hurlin,
C.,
"Unit Root Tests for Panel Data",
Journal of International Money and Finance, 20, 249272.
Authors:
Choi
Coders:
Hurlin
Last update
10/08/2012
Ranking
60
Runs
62
Visits
261

Testing for Unit Roots in Heterogeneous Panels
Abstract
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized tbar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite nonnegative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized tbar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the tbar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).
Hurlin,
C.,
"Testing for Unit Roots in Heterogeneous Panels ",
Journal of Econometrics, 115, 5374.
Authors:
Im
Pesaran Shin
Coders:
Hurlin
Last update
10/08/2012
Ranking
61
Runs
57
Visits
106

Testing for a Unit Root in Panels with Dynamic Factors
Abstract
This paper studies testing for a unit root for large n and T panels in which the crosssectional units are correlated. To model this crosssectional correlation, we assume that the data are generated by an unknown number of unobservable common factors. We propose unit root tests in this environment and derive their (Gaussian) asymptotic distribution under the null hypothesis of a unit root and local alternatives. We show that these tests have significant asymptotic power when the model has no incidental trends. However, when there are incidental trends in the model and it is necessary to remove heterogeneous deterministic components, we show that these tests have no power against the same local alternatives. Through Monte Carlo simulations, we provide evidence on the finite sample properties of these new tests.
Hurlin,
C.,
"Testing for a Unit Root in Panels with Dynamic Factors",
Journal of Econometrics, 122, 81126.
Authors:
Moon
Perron
Coders:
Hurlin
Last update
10/08/2012
Ranking
62
Runs
399
Visits
124

Tests of Conditional Predictive Ability
Abstract
We propose a framework for outofsample predictive ability testing and forecast
selection designed for use in the realistic situation in which the forecasting model is
possibly misspecified, due to unmodeled dynamics, unmodeled heterogeneity, incorrect
functional form, or any combination of these. Relative to the existing literature
(Diebold and Mariano (1995) and West (1996)), we introduce two main innovations:
(i) We derive our tests in an environment where the finite sample properties of the
estimators on which the forecasts may depend are preserved asymptotically. (ii) We
accommodate conditional evaluation objectives (can we predict which forecast will be
more accurate at a future date?), which nest unconditional objectives (which forecast
was more accurate on average?), that have been the sole focus of previous literature.
As a result of (i), our tests have several advantages: they capture the effect of estimation
uncertainty on relative forecast performance, they can handle forecasts based on
both nested and nonnested models, they allow the forecasts to be produced by general
estimation methods, and they are easy to compute. Although both unconditional and
conditional approaches are informative, conditioning can help finetune the forecast
selection to current economic conditions. To this end, we propose a twostep decision
rule that uses current information to select the best forecast for the future date of interest.
We illustrate the usefulness of our approach by comparing forecasts from leading
parameterreduction methods for macroeconomic forecasting using a large number of
predictors.
Giacomini,
R.,
"Tests of Conditional Predictive Ability ",
Econometrica, 74, 15451578.
Authors:
White
Giacomini
Coders:
Giacomini
Last update
07/04/2012
Ranking
25
Runs
17
Visits
67

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