beyond stochastic volatility and jumps in returns and volatility while a great deal of attention has been focused on stochastic volatility in stock returns there is strong evidence suggesting that return distributions have time varying skewness and kurtosis as well under the risk neutral measure for example this can be observed from variation across time in the shape of black scholes implied volatility smiles this article investigates model characteristics that are consistent with variation in the shape of return distributions using a stochastic volatility model with a regime switching feature to allow for random changes in the parameters governing volatility of volatility leverage effect and jump intensity the analysis consists of two steps first the models are estimated using only information from observed returns and option implied volatility standard model assessment tools indicate a strong preference in favor of the proposed models since the information from option implied skewness and kurtosis is not used in fitting the models it is available for dia

evaluating forecasts from setar models of exchange rates we consider the forecasting performance of two setar exchange rate models proposed by krager and kugler j int money fin 12 1993 195 assuming that the models are good approximations to the data generating process we show that whether the non linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the state of nature evaluation based on traditional measures such as root mean squared forecast errors may mask the superiority of the non linear models generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models

model complexity and out of sample performance evidence from s p 500 index returns abstract we apply a range of out of sample specification tests to more than forty competing stochastic volatility models to address how model complexity affects out of sample performance using daily s p 500 index returns model confidence set estimations provide strong evidence that the most important model feature is the non affinity of the variance process despite testing alternative specifications during the turbulent market regime of the global financial crisis of 2008 we find no evidence that either finite or infinite activity jump models or other previously proposed model extensions improve the out of sample performance further applications to value at risk demonstrate the economic significance of our results furthermore the out of sample results suggest that standard jump diffusion models are misspecified

the out of sample success of term structure models as exchange rate predictors a step beyond abstract a large literature suggests that standard exchange rate models cannot outperform a random walk forecast and that the forward rate is not an optimal predictor of the spot rate however there is evidence that the term structure of forward premia contains valuable information for forecasting future spot exchange rates and that exchange rate dynamics display nonlinearities this paper proposes a term structure forecasting model of exchange rates based on a regime switching vector equilibrium correction model which is novel in this context our model significantly outperforms both a random walk and to a lesser extent a linear term structure vector equilibrium correction model for four major dollar rates across a range of horizons

the income effect and supplier induced demand evidence from primary physician services in norway a much debated issue within the health economic literature is whether physicians can induce demand for their services the relationship between physicians nonpractice income and supply of primary physician services in norway is examined it is argued that if inducement exists physicians with a low nonpractice income who work in municipalities where competition for patients is high compensate for lack of patients by inducing demand this model is adapted to the institutional setting of the norwegian primary physician services where there is a fixed fee schedule the analyses were performed on a large set of data encompassing all primary care physicians in norway who are remunerated per item of treatment data on output in practice were merged with information about nonpractice income from the tax forms of the physician and her his spouse in municipalities with high physician density nonpractice income had no effect on the number of consultations per physician or on the number of treatment items

modeling the co movements between crude oil and refined petroleum markets abstract in this paper we investigate two types of asymmetries i e the asymmetry in the lower and upper tail dependences and the asymmetry in the propagation of crisis bubble between crude oil market and refined petroleum markets based on copula models thirteen copula models with different types of dependence structures and time varying dependence parameters are considered we find that in general the malm copula fits our sample data best based on aic criterion we find that lower and upper tail dependences are both positive indicating that crude oil and refined product markets tend to move together using the asymmetric copulas we find asymmetry in tail dependence between crude oil and heating oil returns and that between crude oil and jet fuel returns interestingly upper tail dependence is significantly greater than the lower tail dependence for the pre crisis period while the result is reversed for the post crisis period finally although our data prefers the nonexchangeable malm copula the asymmetry in the propagation of crisis bubble between crude oil and refined product returns is very weak

price density forecasts in the u s hog markets composite procedures we develop and evaluate quarterly out of sample individual and composite density forecasts for u s hog prices individual density forecasts are generated using time series models and the implied distributions of usda and iowa state university outlook forecasts composite density forecasts are constructed using linear and logarithmic combinations of the individual forecasts and several weighting schemes density forecasts are evaluated on predictive accuracy sharpness goodness of fit calibration and their economic value in a hedging simulation logarithmic combinations using equal and mean square error weights outperform all individual density forecasts and are modestly better than linear composites comparison of the outlook forecasts to the best composite demonstrates the usefulness of the composite procedure and identifies the economic value that more accurate expected price probability distributions can provide to producers

oil prices brownian motion or mean reversion a study using a one year ahead density forecast criterion abstract for oil related investment appraisal an accurate description of the evolving uncertainty in the oil price is essential for example when using real option theory to value an investment a density function for the future price of oil is central to the option valuation the literature on oil pricing offers two views the arbitrage pricing theory literature for oil suggests geometric brownian motion and mean reversion models empirically driven literature suggests arma garch models in addition to reflecting the volatility of the market the density function of future prices should also incorporate the uncertainty due to price jumps a common occurrence in the oil market in this study the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour both those proposed in the literature and following from data analysis the kullbach leibler information criterion is used to measure the accuracy of density forecasts using two crude oil price series brent and west texas intermediate wti representing the us market we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two gaussians innovation processes with garch and no mean reversion

estimation and forecasting in models with multiple breaks this paper develops a new approach to change point modelling that allows the number of change points in the observed sample to be unknown the model we develop assumes that regime durations have a poisson distribution it approximately nests the two most common approaches the time varying parameter tvp model with a change point every period and the change point model with a small number of regimes we focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters that characterize each regime a markov chain monte carlo posterior sampler is constructed to estimate a version of our model which allows for change in conditional means and variances we show how real time forecasting can be done in an efficient manner using sequential importance sampling our techniques are found to work well in an empirical exercise involving u s gdp growth and inflation empirical results suggest that the number of change points is larger than previously estimated in these series and the implied model is similar to a tvp with stochastic volatility model

rational forecasts or social opinion dynamics identification of interaction effects in a business climate survey this paper develops a methodology for estimating the parameters of dynamic opinion or expectation formation processes with social interactions we study a simple stochastic framework of a collective process of opinion formation by a group of agents who face a binary decision problem the aggregate dynamics of the individuals decisions can be analyzed via the stochastic process governing the ensemble average of choices numerical approximations to the transient density for this ensemble average allow the evaluation of the likelihood function on the base of discrete observations of the social dynamics this approach can be used to estimate the parameters of the opinion formation process from aggregate data on its average realization our application to a well known business climate index provides strong indication of social interaction as an important element in respondents assessment of the business climate

dynamic discrete mixtures for high frequency prices the tick structure of the financial markets entails discreteness of stock price changes based on this empirical evidence we develop a multivariate model for discrete price changes featuring a mec

out of sample density forecasts with affine jump diffusion models abstract we conduct out of sample density forecast evaluations of the affine jump diffusion models for the s p 500 stock index and its options contracts we also examine the time series consistency between the model implied spot volatilities using options returns and only returns in particular we focus on the role of the time varying jump risk premia particle filters are used to estimate the model implied spot volatilities we also propose the beta transformation approach for recursive parameter updating our empirical analysis shows that the inconsistencies between options returns and only returns are resolved by the introduction of the time varying jump risk premia for density forecasts the time varying jump risk premia models dominate the other models in terms of likelihood criteria we also find that for medium term horizons the beta transformation can weaken the systematic effect of misspecified ajd models using options returns

constructing density forecasts from quantile regressions the departure from the traditional concern with the central tendency is in line with the increasing recognition that an assessment of the degree of uncertainty surrounding a point forecast is indispensable clements 2004 we propose an econometric model to estimate the conditional density without relying on assumptions about the parametric form of the conditional distribution of the target variable the methodology is applied to the u s unemployment rate and the survey of professional forecasts specification tests based on koenker and xiao 2002 and gaglianone et al 2011 indicate that our approach correctly approximates the true conditional density

forecast encompassing tests and probability forecasts we consider tests of forecast encompassing for probability forecasts for both quadratic and logarithmic scoring rules we propose test statistics for the null of forecast encompassing present the limiting distributions of the test statistics and investigate the impact of estimating the forecasting models parameters on these distributions the small sample performance is investigated in terms of small numbers of forecasts and model estimation sample sizes we show the usefulness of the tests for the evaluation of recession probability forecasts from logit models with different leading indicators as explanatory variables and for evaluating survey based probability forecasts copyright c 2010 john wiley sons ltd

out of sample performance of discrete time spot interest rate models we provide a comprehensive analysis of the out of sample performance of a wide variety of spot rate models in forecasting the probability density of future interest rates although the most parsimonious models perform best in forecasting the conditional mean of many financial time series we find that the spot rate models that incorporate conditional heteroscedasticity and excess kurtosis or heavy tails have better density forecasts generalized autoregressive conditional heteroscedasticity significantly improves the modeling of the conditional variance and kurtosis whereas regime switching and jumps improve the modeling of the marginal density of interest rates our analysis shows that the sophisticated spot rate models in the existing literature are important for applications involving density forecasts of interest rates

bias in federal reserve inflation forecasts is the federal reserve irrational or just cautious abstract inflation forecasts of the federal reserve seem to have systematically under predicted inflation from the fourth quarter of 1968 until volcker s appointment as chairman and to systematically over predict it afterwards until the second quarter of 1998 furthermore under quadratic loss commercial forecasts seem to have information not contained in those forecasts to investigate the cause of this apparent irrationality this paper recovers the loss function implied by federal reserve s inflation forecasts the results suggest that the cost of having inflation above an implicit time varying target was larger than the cost of having inflation below it for the period since volcker and that the opposite was true for the pre volcker era once these asymmetries are taken into account the federal reserve s inflation forecasts are found to be rational

explaining the absence of international factor price convergence abstract as capital markets have become integrated factor prices have not converged as expected this paper provides an explanation the key observation is that non specific output risks are borne by capital making international portfolio diversification a dominant strategy capital market integration can elicit many patterns of factor price response under this circumstance factor prices will move in parallel if countries are identical when countries differ factor prices may diverge or even have their relative sizes reversed the integration of capital markets affects welfare through changes in risk and in overall returns to savings welfare need not improve

monitoring and forecasting currency crises can we improve forecasts of currency crises by using a large number of predictors which economic variables are most important to predict currency crises this paper evaluates the performance of traditional leading indicators and a new diffusion index di method as early warning systems to monitor the risk and forecast the likelihood of the recent currency crises in east asia we find that relative to traditional leading indicators such as money multipliers terms of trade etc the diffusion index performs quite well in real time with a higher probability of providing correct signals we also find that the economic variables that help in forecasting future crises are output growth the balance of payments interest rates and money growth and that their relative importance changes dramatically around the time of the crises

multivariate density forecast evaluation and calibration in financial risk management high frequency returns on foreign exchange we provide a framework for evaluating and improving multivariate density forecasts among other things the multivariate framework lets us evaluate the adequacy of density forecasts involving cross variable interactions such as time varying conditional correlations we also provide conditions under which a technique of density forecast calibration can be used to improve deficient density forecasts and we show how the calibration method can be used to generate good density forecasts from econometric models even when the conditional density is unknown finally motivated by recent advances in financial risk management we provide a detailed application to multivariate high frequency exchange rate density forecasts

euromind d a density estimate of monthly gross domestic product for the euro area euromind d is a density estimate of monthly gross domestic product gdp constructed according to a bottom up approach pooling the density estimates of eleven gdp components by output and expenditure type the components density estimates are obtained from a medium size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged edged data structures they reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters and conditional simulation filters for simulating from the predictive distribution of gdp both algorithms process sequentially the data as they become available in real time the gdp density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules

modeling eu allowances and oil market interdependence implications for portfolio management abstract this paper examines the dependence structure between european union allowances euas and crude oil markets during the second commitment period of the european union emissions trading scheme and the implications for portfolio management using different copula models our findings suggest positive average dependence and extreme symmetric independence that is consistent with interdependence and no contagion effects between the eua and crude oil markets the implication of this result for eua oil portfolios points to the existence of diversification benefits hedging effectiveness and value at risk reductions the eua market is therefore an attractive market for investors in terms of diversifying market risk and reducing downside risk in crude oil markets

are china s new energy stock prices driven by new energy policies abstract this paper studies the impact of china s new energy policies on expected changes and volatility in new energy stock prices considering different kinds of policies energy legislation binding targets for new energies economic incentives and technological research and development and several new energy indexes china s new energy sector and the solar wind nuclear and lithium battery subsectors we used a regression model with a garch specification and dummy variables to differentiate policy pre and post announcement effects on expected returns and volatility our evidence indicates that pre and post announcement energy legislation policies dampened price volatility in all subsector indexes and that economic incentives had a positive policy announcement effect on all subsector index prices other new energy policies had no significant impact on either the mean or the volatility of the new energy assets the potential implications for policy makers and investors are discussed

a conditional distribution model for limited stock index returns abstract when a price limit regime exists for all of the stocks involved in an index the index return is an aggregate of limited variables and thereby it is restricted to the same limits we argue that neither a censored nor a truncated distribution model is appropriate for the aggregate return the proposed mixed beta distribution allows for varying conditional mean and volatility and with increasing volatility it changes from leptokurtic to platykurtic densities the model is illustrated and statistically evaluated with an empirical application to the shanghai stock market index returns under a 10 price change limit regime

comparing smooth transition and markov switching autoregressive models of us unemployment logistic smooth transition and markov switching autoregressive models of a logistic transform of the monthly us unemployment rate are estimated by markov chain monte carlo methods the markov switching model is identified by constraining the first autoregression coefficient to differ across regimes the transition variable in the lstar model is the lagged seasonal difference of the unemployment rate out of sample forecasts are obtained from bayesian predictive densities although both models provide very similar descriptions bayes factors and predictive efficiency tests both bayesian and classical favor the smooth transition model copyright 2008 john wiley sons ltd

the copula garch model of conditional dependencies an international stock market application abstract modeling the dependency between stock market returns is a difficult task when returns follow a complicated dynamics when returns are non normal it is often simply impossible to specify the multivariate distribution relating two or more return series in this context we propose a new methodology based on copula functions which consists in estimating first the univariate distributions and then the joining distribution in such a context the dependency parameter can easily be rendered conditional and time varying we apply this methodology to the daily returns of four major stock markets our results suggest that conditional dependency depends on past realizations for european market pairs only for these markets dependency is found to be more widely affected when returns move in the same direction than when they move in opposite directions modeling the dynamics of the dependency parameter also suggests that dependency is higher and more persistent between european stock markets

do food and oil prices co move abstract this paper studies co movements between world oil prices and global prices for corn soybean and wheat using copulas several copula models with different conditional dependence structures and time varying dependence parameters were considered empirical results for weekly data from january 1998 to april 2011 showed weak oil food dependence and no extreme market dependence between oil and food prices these results support the neutrality of agricultural commodity markets to the effects of changes in oil prices and non contagion between the crude oil and agricultural markets however dependence increased significantly in the last three years of the sampling period even though upper tail dependence remained insignificant indicating that food price spikes are not caused by positive extreme oil price changes these results have implications for policy design risk management and hedging strategies

asymmetry loss aversion and forecasting conditional volatility models such as garch have been used extensively in financial applications to capture predictable variation in the second moment of asset returns however with recent theoretical literature emphasising the loss averse nature of agents this paper considers models which capture time variation in the second lower partial moment utility based evaluation is carried out on several approaches to modelling the conditional second order lower partial moment or semi variance including distribution and regime based models the findings show that when agents are loss averse

evaluating point and density forecasts of dsge models this paper investigates the accuracy of point and density forecasts of four dsge models for inflation output growth and the federal funds rate model parameters are estimated and forecasts are derived successively from historical u s data vintages synchronized with the fed s greenbook projections point forecasts of some models are of similar accuracy as the forecasts of nonstructural large dataset methods despite their common underlying new keynesian modeling philosophy forecasts of different dsge models turn out to be quite distinct weighted forecasts are more precise than forecasts from individual models the accuracy of a simple average of dsge model forecasts is comparable to greenbook projections for medium term horizons comparing density forecasts of dsge models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts

an empirical test of pricing kernel monotonicity summary a large class of asset pricing models predicts that securities which have high payoffs when market returns are low tend to be more valuable than those with high payoffs when market returns are high more generally we expect the projection of the stochastic discount factor on the market portfolio that is the discounted pricing kernel evaluated at the market portfolio to be a monotonically decreasing function of the market portfolio numerous recent empirical studies appear to contradict this prediction the non monotonicity of empirical pricing kernel estimates has become known as the pricing kernel puzzle in this paper we propose and apply a formal statistical test of pricing kernel monotonicity we apply the test using 17 years of data from the market for european put and call options written on the s p 500 index statistically significant violations of pricing kernel monotonicity occur in a substantial proportion of months suggesting that observed non monotonicities are unlikely to be the product of statistical noise copyright 2014 john wiley sons ltd

sv mixture models with application to s p 500 index returns understanding both the dynamics of volatility and the shape of the distribution of returns conditional on the volatility state is important for many financial applications a simple single factor stochastic volatility model appears to be sufficient to capture most of the dynamics it is the shape of the conditional distribution that is the problem this paper examines the idea of modeling this distribution as a discrete mixture of normals the flexibility of this class of distributions provides a transparent look into the tails of the returns distribution model diagnostics suggest that the model sv mix does a good job of capturing the salient features of the data in a direct comparison against several affine jump models sv mix is strongly preferred by akaike and schwarz information criteria

is it better to average probabilities or quantiles we consider two ways to aggregate expert opinions using simple averages averaging probabilities and averaging quantiles we examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd in terms of location the two average forecasts have the same mean the average quantile forecast is always sharper it has lower variance than the average probability forecast even when the average probability forecast is overconfident the shape of the average quantile forecast still offers the possibility of a better forecast using probability forecasts for gross domestic product growth and inflation from the survey of professional forecasters we present evidence that both when the average probability forecast is overconfident and when it is underconfident it is outperformed by the average quantile forecast our results show that averaging quantiles is a viable alternative and indicate some conditions under which it is likely to be more useful than averaging probabil

conditional jump dynamics in stock market returns this article develops a new conditional jump model to study jump dynamics in stock market returns we propose a simple filter to infer ex post the distribution of jumps this permits construction of the shock affecting the time t conditional jump intensity and is the main input into an autoregressive conditional jump intensity model the model allows the conditional jump intensity to be time varying and follows an approximate autoregressive moving average arma form the time series characteristics of 72 years of daily stock returns are analyzed using the jump model coupled with a generalized autoregressive conditional heteroscedasticity garch specification of volatility we find significant time variation in the conditional jump intensity and evidence of time variation in the jump size distribution the conditional jump dynamics contribute to good in sample and out of sample fits to stock market volatility and capture the rally often observed in equity markets following a significant downturn

evaluating density forecasts with applications to financial risk management density forecasting is increasingly more important and commonplace for example in financial risk management yet little attention has been given to the evaluation of density forecasts the authors develop a simple and operational framework for density forecast evaluation they illustrate the framework with a detailed application to density forecasting of asset returns in environments with time varying volatility finally the authors discuss several extensions copyright 1998 by economics department of the university of pennsylvania and the osaka university institute of social and economic research association

optimal binary prediction for group decision making we address the problem of optimally forecasting a binary variable for a heterogeneous group of decision makers facing various binary decision problems that are tied together only by the unknown outcome a typical example is a weather forecaster who needs to estimate the probability of rain tomorrow and then report it to the public given a conditional probability model for the outcome of interest e g logit or probit we introduce the idea of maximum welfare estimation and derive conditions under which traditional estimators such as maximum likelihood or nonlinear least squares are asymptotically socially optimal even when the underlying model is misspecified

the role of asymmetries and regime shifts in the term structure of interest rates we examine the relationship between interest rates of different maturities for the us germany and japan over the period 1982 2000 using a general multivariate vector equilibrium correction modelling framework capable of simultaneously allowing for asymmetric adjustment and regime shifts this approach has a very general underlying theoretical rationale that allows for time varying term premia and other short run deviations from the expectations model of the term structure the resulting nonlinear models provide good in sample fits display regime switches closely related to key state variables driving monetary policy decisions and have satisfactory out of sample forecasting properties

managing volumetric risk of long term power purchase agreements abstract a negative dependence between wind power production and electricity spot price exists this is an important fact to consider for risk management of long term power purchase agreements ppas in this study we investigate this dependence by constructing a joint model using constant as well as time varying copulas we propose to use score driven models as marginal model for the spot price of electricity as these are more robust to extreme events compared to arma garch models we apply the new model to pricing and risk management of ppas and benchmark it against the arma garch specification our comparison shows that the score driven model results in a statistically significant improvement of predicting the value at risk var which is of high importance for risk management of long term ppas further comparing constant and time varying copulas we find that all time varying copulas are significantly better than their constant counterparts at predicting the var hence time varying copulas should be used in risk management of ppas

functional ross recovery theoretical results and empirical tests abstract recently ross 2015 showed that the real world probability distribution of a discrete markovian state variable can be recovered from observed option prices the so called recovery theorem follows from perron frobenius matrix theory when the pricing kernel is transition independent in this paper we generalize the recovery theorem to continuous state spaces using perron frobenius operator theory building on our theoretical results we devise a nonparametric approach to empirically estimate the recovered pricing kernel and probability density in closed form using s p 500 index options we analyze recovered pricing kernels empirically and find evidence that ross recovery is misspecified

forecasting the distribution of economic variables in a data rich environment this article investigates the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable the baseline time series model is a semiparametric specification based on the quantile autoregressive qar model that assumes that the quantiles depend on the lagged values of the variable we then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset of variables selected by lasso we forecast the distribution of the h month growth rate for four economic variables from 1975 to 2011 and evaluate the forecast accuracy relative to a stochastic volatility model using the quantile score the results for the output and employment measures indicate that the multivariate models outperform the time series forecasts in particular at long horizons and in tails of the distribution while for the inflation variables the improved performance occurs mostly at the 6 month horizon we also illustra

real time forecasting with a mixed frequency var this article develops a vector autoregression var for time series which are observed at mixed frequencies quarterly and monthly the model is cast in state space form and estimated with bayesian methods under a minnesota style prior we show how to evaluate the marginal data density to implement a data driven hyperparameter selection using a real time dataset we evaluate forecasts from the mixed frequency var and compare them to standard quarterly frequency var and to forecasts from midas regressions we document the extent to which information that becomes available within the quarter improves the forecasts in real time this article has online supplementary materials

how do crude oil prices co move a copula approach abstract this paper examines the dependence structure between crude oil benchmark prices using copulas by considering several copula models with different conditional dependence structures and time varying dependence parameters we find evidence of significant symmetric upper and lower tail dependence between crude oil prices these findings suggest that crude oil prices are linked with the same intensity during bull and bear markets thus supporting the hypothesis that the oil market is one great pool in contrast with the hypothesis that states that the oil market is regionalized our findings on crude oil price co movements also have implications for risk management hedging strategies and asset pricing

moment based copula tests for financial returns we propose a class of moment based tests for copulas in a parametric multivariate dynamic context for financial returns the proposed method takes into account the effect of estimation uncertainty this effect is quite important but often is ignored in related studies our method can be applied to generate various tests to detect copula misspecification in different directions in particular on the basis of the conditional probabilities of quantile exceedances kendall s tau it generates the tail dependence tests the concordance test that can be used to investigate whether the copula being tested is suitable for characterizing the true tail dependence concordance structure such tests may be useful for exploring the cross dependence structures of financial returns that are essential for risk management and other purposes the monte carlo simulation supports the validity of our method as a demonstrative application we also apply these tests to an empirical study of stock market relationships

recovery rates default probabilities and the credit cycle in recessions the number of defaulting firms rises on top of this the average amount recovered on the bonds of defaulting firms tends to decrease this paper proposes an econometric model in which this joint time variation in default rates and recovery rate distributions is driven by an unobserved markov chain which we interpret as the credit cycle this model is shown to fit better than models in which this joint time variation is driven by observed macroeconomic variables we use the model to quantitatively assess the importance of allowing for systematic time variation in recovery rates which is often ignored in risk management and pricing models

combining var and dsge forecast densities a popular macroeconomic forecasting strategy utilizes many models to hedge against instabilities of unknown timing see among others stock and watson 2004 clark and mccracken 2010 and jore et al 2010 existing studies of this forecasting strategy exclude dynamic stochastic general equilibrium dsge models despite the widespread use of these models by monetary policymakers in this paper we use the linear opinion pool to combine inflation forecast densities from many vector autoregressions vars and a policymaking dsge model the dsge receives a substantial weight in the pool at short horizons provided the var components exclude structural breaks in this case the inflation forecast densities exhibit calibration failure allowing for structural breaks in the vars reduces the weight on the dsge considerably but produces well calibrated forecast densities for inflation

bridging the gap between the distribution of realized ecu volatility and arch modelling of the euro the garch nig model summary this paper bridges the gap between traditional arch modelling and recent advances on realized volatilities based on a ten year sample of five minute returns for the ecu basket currencies versus the us dollar we find that the realized volatilities constructed from the summation of the high frequency intraday squared returns conditional on the lagged squared daily returns are approximately inverse gaussian ig distributed while the distribution of the daily returns standardized by their realized volatilities is approximately normal moreover the implied daily garch model with normal inverse gaussian nig errors estimated for the ecu returns results in very accurate out of sample predictions for the three years of actual daily euro us dollar exchange rates copyright 2002 john wiley sons ltd

multinomial var backtests a simple implicit approach to backtesting expected shortfall under the fundamental review of the trading book frtb capital charges for the trading book are based on the coherent expected shortfall es risk measure which show greater sensitivity to tail risk in this paper it is argued that backtesting of expected shortfall or the trading book model from which it is calculated can be based on a simultaneous multinomial test of value at risk var exceptions at different levels an idea supported by an approximation of es in terms of multiple quantiles of a distribution proposed in emmer et al 2015 by comparing pearson nass and likelihood ratio tests lrts for different numbers of var levels n it is shown in a series of simulation experiments that multinomial tests with n 4 are much more powerful at detecting misspecifications of trading book loss models than standard bi nomial exception tests corresponding to the case n 1 each test has its merits pearson offers simplicity nass is robust in its size properties to the choice of n the lrt is very powerful though slightly over sized in small samples and more computationally burdensome a traffic light system for trading book models based on the multinomial test is proposed and the recommended procedure is applied to a real data example spanning the 2008 financial crisis

estimation and testing of forecast rationality under flexible loss in situations where a sequence of forecasts is observed a common strategy is to examine rationality conditional on a given loss function we examine this from a different perspective supposing that we have a family of loss functions indexed by unknown shape parameters then given the forecasts can we back out the loss function parameters consistent with the forecasts being rational even when we do not observe the underlying forecasting model we establish identification of the parameters of a general class of loss functions that nest popular loss functions as special cases and provide estimation methods and asymptotic distributional results for these parameters this allows us to construct new tests of forecast rationality that allow for asymmetric loss the methods are applied in an empirical analysis of imf and oecd forecasts of budget deficits for the g7 countries we find that allowing for asymmetric loss can significantly change the outcome of empirical tests of forecast rationality