post keynesianism meets feminist economics this article explores the relationships between post keynesian economics and feminist economics it distinguishes three key concepts in each tradition that recommend serious attention in the other tradition gender the household and unpaid work and caring as key concepts in feminist economics uncertainty market power and endogenous dynamics as core concepts in post keynesian economics this article will show with reference to the literature in which such cross fertilisation has been explored already how both traditions can be enriched from a stronger mutual engagement

dynamic expected shortfall a spectral decomposition of tail risk across time horizons abstract the tail risk of financial institutions is traditionally measured by expected shortfall es that does not characterize risk changes over investment horizons using wavelet analysis we propose a new method to capture the dynamics of es across time horizons the new method decomposes the stock return of financial institutions into different frequency e g short mid and long run components and then models the dynamics of these components separately to produce an aggregated es forecast we provide numerical and empirical examples to illustrate the new method we also study the relevance of each frequency component to out of sample es forecasts over different predictive horizons our empirical results confirm that the different frequency components of stock returns exhibit different persistence explicitly considering this distinction when modeling es significantly improves the out of sample forecasting performance in addition excluding the long run e g yearly return component can largely reduce short run e g weekly or monthly es forecasts without impacting the regulatory quality of the risk assessment

evaluating interval forecasts 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 i set out to build a consistent framework for conditional interval forecast evaluation which is crucial when higher order moment dynamics are present the new methodology is demonstrated in an application to the exchange rate forecasting procedures advocated in risk management

unexpected tails in risk measurement some international evidence risk management critically depends on the assumptions made about the distribution of stock returns this paper applies extreme value methods to investigate the limiting distribution of the extreme returns of the nikkei225 ftse100 and s p500 indices as well as the indices of some of largest sectors in japan uk and us the results indicate that the much celebrated generalised extreme value distribution does not provide the most accurate description of the minima since the generalised logistic distribution performs better due to its ability to better capture the fat tails of returns the time varying nature of extremes is also confirmed while a simulation exercise adds to the robustness of our results it is also shown that the findings may have important implications for risk models such as var and expected shortfall since risk measures which cannot capture the fatness of tails of the empirical distribution function of returns may lead to serious underestimation of downside risk

risk models at risk the experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risks a key reason for this is that risk measures are subject to a model risk due e g to specification and estimation uncertainty while regulators have proposed that financial institutions assess the model risk there is no accepted approach for computing such a risk we propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting the imperfect risk forecasts by outcomes from backtesting frameworks considering the desirable quality of var models such as the frequency independence and magnitude of violations we also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections

exploring the sources of default clustering abstract we study the sources of corporate default clustering in the united states we reject the hypothesis that firms default times are correlated only because their conditional default rates depend on observable and latent systematic factors by contrast we find strong evidence that contagion through which the default by one firm has a direct impact on the health of other firms is a significant clustering source the amount of clustering that cannot be explained by contagion and firms exposure to observable and latent systematic factors is insignificant our results have important implications for the pricing and management of correlated default risk

charter value risk taking and systemic risk in banking before and after the global financial crisis of 2007 2008 we investigate how bank charter value affects risk for a sample of oecd banks by using standalone and systemic risk measures before during and after the global financial crisis of 2007 2008 prio

value at risk and expected shortfall in cryptocurrencies portfolio a vine copula based approach risk management is an important and helpful process for investors hedge funds traders and market makers one of its key points is the appropriate estimation of risk measures which can improve the

take it to the limit innovative cvar applications to extreme credit risk measurement abstract the global financial crisis gfc demonstrated the devastating impact of extreme credit risk on global economic stability we develop four credit models to better measure credit risk in extreme economic circumstances by applying innovative conditional value at risk cvar techniques to structural models called xtreme s transition models xtreme t quantile regression models xtreme q and the author s unique itransition model xtreme i which incorporates industry factors into transition matrices we find the xtreme s and xtreme q models to be the most responsive to changing market conditions the paper also demonstrates how the models can be used to determine capital buffers required to deal with extreme credit risk

forecasting quantiles of day ahead electricity load accurate load forecasting plays a crucial role in the decision making process of many market participants but probably is most important for the dispatch planning of an electricity market operator despite the competitive forecast accuracy achieved by existing point forecast models point forecasts can only provide limited information relating to the expected level of future load to account for the uncertainty of future load and provide a more complete picture of the future load conditions for dispatch planning purposes quantile forecasts can be useful this paper proposes a computationally efficient approach to forecasting the quantiles of electricity load which is then applied to forecasting in the national electricity market of australia the proposed model performs competitively in comparison with one industry standard and two recently proposed quantile forecasting methods one of the main advantages of the proposed approach is the ease with the number of covariates can be expanded this is a particularly important feature in the context of load forecasting where large numbers of important drivers are usually necessary to provide accurate load forecasts

bayesian time varying quantile forecasting for value at risk in financial markets recently advances in time varying quantile modeling have proven effective in financial value at risk forecasting some well known dynamic conditional autoregressive quantile models are generalized to a fully nonlinear family the bayesian solution to the general quantile regression problem via the skewed laplace distribution is adapted and designed for parameter estimation in this model family via an adaptive markov chain monte carlo sampling scheme a simulation study illustrates favorable precision in estimation compared to the standard numerical optimization method the proposed model family is clearly favored in an empirical study of 10 major stock markets the results that show the proposed model is more accurate at value at risk forecasting over a two year period when compared to a range of existing alternative models and methods

fractional integration in agricultural futures price volatilities revisited jin and frechette 2004 examined the degree to which agricultural price volatilities exhibited evidence of fractional integration and concluded it was important to consider both long run and short run memory when modeling conditional variances the purpose of this note is to revisit the issue using new methods and techniques which generally reaffirm the view that return volatilities are fractionally integrated and conditionally heteroskedastic with many exhibiting significant leverage effects a result not previously reported

a note on the importance of overnight information in risk management models this paper examines the economic value of overnight information to users of risk management models in addition to the information revealed by overseas markets that trade during the domestic overnight period this paper exploits information generated via recent innovations in the structure of financial markets in particular certain securities and associated derivative products can now be traded at any time over a 24 h period as such it is now possible to make use of information generated by trading in almost identical securities during the overnight period of the securities that are available over such time periods s p 500 related products are by far the most actively traded and are therefore the subject of this paper using a variety of conditional volatility models that allow time dependent information flow within and across three different s p 500 markets the results show that overnight information flow has a significant impact on the conditional volatility of daytime traded s p 500 securities moreover time consistent forecasts from models that incorporate overnight information are shown to have economic value to risk managers in particular value at risk var models based on these conditional volatility models are shown to be more accurate than var models that ignore overnight information

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

the rise of europe atlantic trade institutional change and economic growth the rise of western europe after 1500 is due largely to growth in countries with access to the atlantic ocean and with substantial trade with the new world africa and asia via the atlantic this trade and the associatedcolonialism affected europe not only directly but also indirectly by inducing institutional change where initial political institutions those established before 1500 placed significant checks on the monarchy the growth of atlantic trade strengthened merchant groups by constraining the power of the monarchy and helped merchants obtain changes in institutions to protect property rights these changes were central to subsequent economic growth

currency downside risk liquidity and financial stability abstract we estimate volatility and quantile depreciation based spillovers across 20 global currencies against the us dollar in so doing we reveal significant asymmetries in the propagation of risk across global currency markets the quantile based statistic reacts more significantly to events that have a sizable impact on fx markets e g brexit vote and the fx crash following the subprime crisis which are missed by the volatility based statistic as such our tail spillover estimates constitute a new financial stability index for the fx market this index has the advantages of being easy to build of not requiring intraday data and of being more informative about currency crises and pressures than traditional spillover statistics based on volatilities finally we also document differences in the relation between liquidity and volatility quantile spillovers

modeling the dependence of conditional correlations on volatility several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns but few studies have investigated the determinants of the correlation dynamics a common opinion is that the market volatility is a major determinant of the correlations we extend some models to capture explicitly the dependence of the correlations on the volatility of the market of interest the models differ in the way by which the volatility influences the correlations which can be transmitted through linear or nonlinear and direct or indirect effects they are applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations

caviar conditional autoregressive value at risk by regression quantiles value at risk var has become the standard measure of market risk employed by financial institutions for both internal and regulatory purposes var is defined as the value that a portfolio will lose with a given probability over a certain time horizon usually one or ten days despite its conceptual simplicity its measurement is a very challenging statistical problem and none of the methodologies developed so far give satisfactory solutions interpreting the var as the quantile of future portfolio values conditional on current information we propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies such as normality or i i d returns the conditional autoregressive value at risk or caviar model moves the focus of attention from the distribution of returns directly to the behavior of the quantile we specify the evolution of the quantile over time using a special type of autoregressive process and use the regression quantile framework introduced by koenker and bassett to determine the unknown parameters since the objective function is not differentiable we use a differential evolutionary genetic algorithm for the numerical optimization utilizing the criterion that each period the probability of exceeding the var must be independent of all the past information we introduce a new test of model adequacy the dynamic quantile test applications to simulated and real data provide empirical support to this methodology and illustrate the ability of these algorithms to adapt to new risk environments

monetary policy and food inflation in south africa a quantile regression analysis abstract although optimal monetary policy stabilizes food inflation theoretically empirical studies remain limited not only in the context of volumes and the estimation approaches but are focused on selected advanced and emerging countries to the neglect of africa where poverty and dominance of food in the consumption basket are more pronounced we provide empirical evidence in the context of south africa using quantile regressions rising food prices are destabilized even further by restrictive monetary policy a finding that has ramifications for inflation targeting especially given that a quarter of the country s population is food poor

modeling and forecasting extreme commodity prices a markov switching based extreme value model abstract we propose a markov switching multifractal peaks over threshold msm pot model to capture the dynamic behavior of the random occurrences of extreme events exceeding a high threshold in time series of returns this approach allows introducing changes of regimes in the conditional mean function of the inter exceedance times i e the time between two consecutive extreme events in order to admit the presence of short and long term memory patterns further through its multifractal structure the msm pot approach is able to capture the typical stylized facts of extreme events observed in financial time series such as temporal clustering of the size of exceedances and temporal behavior of tail thickness we compare the performance of the msm pot model with competing self exciting models and a garch evt approach in an in and out of sample var forecasting exercise based on the extreme returns of six daily commodity futures prices i e brent and wti crude oil cocoa cotton copper and gold empirical results suggest that the var estimates generated by the msm pot model for the returns analyzed produce the most accurate forecasts

occupational segregation and the gender wage gap in a dynamic east asian economy labor markets in the east asian miracle economies have undergone profound changes in recent decades as their comparative advantage in low wage labor diminished and jobs shifted toward higher skill manufacturing and services this study uses an occupational decomposition technique to examine how such shifts in east asia s occupational structures have affected trends in their gender wage gaps the wage gap is decomposed into across occupation and within occupation factors that are each further separated into wage and employment components results based on a comprehensive labor force data set for taiwan show that within occupation pay discrepancies account for the bulk of gender wage inequity

dependence and extreme dependence of crude oil and natural gas prices with applications to risk management abstract in this article we show how the copula garch approach can be appropriately used to investigate the conditional dependence structure between the crude oil and natural gas markets as well as to derive implications for portfolio risk management in extreme economic conditions using daily price data from january 1997 to october 2011 our in sample results show evidence of asymmetric dependence between the two markets the crude oil and gas markets tend to comove closely together during bullish periods but not at all during bearish periods moreover taking the extreme comovement into account leads to an improvement in the accuracy of the out of sample value at risk forecasts

causality and predictability in distribution the ethanol food price relation revisited abstract this paper examines the relationship between biofuels field crops and cattle prices in the u s from a new perspective we focus on predictability in distribution by asking whether ethanol returns can be used to forecast different parts of field crops and cattle returns distribution or vice versa density forecasts are constructed using conditional autoregressive expectile models estimated with asymmetric least squares forecast evaluation relies on quantile weighed scoring rules which identify regions of the distribution of interest to the analyst results show that both the centre and the left tail of the ethanol returns distribution can be predicted by using field crops returns on the contrary there is no evidence that ethanol can be used to forecast any region of the field crops or cattle returns distributions

value at risk estimation of energy commodities a long memory garch evt approach abstract in this paper we evaluate value at risk var and expected shortfall es for crude oil and gasoline market we adopt three long memory models including figarch hygarch and fiaparch to forecast energy commodity volatility by capturing some volatility stylized fact such as long range memory heteroscedasticity asymmetry and fat tails then we consider extreme value theory which concentrates on the tail distribution rather than the entire distribution evt is considered as a potential framework for the separate treatment of tails of distributions which allows for asymmetry our results show that the fiaparch model with extreme value theory performs better in predicting the one day ahead var using the fitted long memory garch model and a simulation approach to estimate var for horizons over than one day backtesting results show that our approach still performs for lower estimation frequencies overall our findings confirm that taking into account long range memory asymmetry and fat tails in the behavior of energy commodity prices returns combined with filtering process such as evt are important in improving risk management assessments and hedging strategies in the high volatile energy market

risk transmission mechanism between energy markets a var for var approach abstract the global financial crisis gfc of 2007 2009 that originated in the us has revealed the need for measuring and monitoring the transmission of extreme downside market risk this paper investigates the risk transmission mechanism between the oil and natural gas markets we apply the recently introduced test statistics based on cross quantilogram function and the multivariate quantile regression model var for var to the us oil and natural gas prices which are independently formed our results show two asymmetric patterns first the shocks in the oil market substantially increase the value at risk var in the natural gas market but the reverse impact does not exist second we highlight the significant asymmetric response of gains and losses transmission in energy markets cautioning about the underlying weakness of adopting volatility to measure risk in the energy market moreover an extreme market risk is more easily transmitted across markets than moderate risks our results are in general robust in application to other regional energy markets such as europe and asia but the heterogeneities in responses are underpinned by the different role of natural gas in regions the findings in this paper have important implications for academic researchers policy makers in gas dependent economies and business practitioners in light of projected increases in the use of natural gas worldwide as well as the development of independent gas on gas competitive hub prices in asia and europe

relationship between the oil price volatility and sectoral stock markets in oil exporting economies evidence from wavelet nonlinear denoised based quantile and granger causality analysis abstract this paper examines the extent of volatility between oil price and sectoral indices in the gulf cooperation council gcc countries by using quantile regression analysis qra for the return s series and denoised series over the period 2006 2017 four sectors are found to offer diversification opportunities during a high market i e 90th quantile all the sectors are found interdependent of oil price volatility however the bank and insurance sectors are insusceptible to oil price volatility during the 10th 25th and 75th quantiles in addition qra results for wavelet nonlinear denoising with a soft thresholding series indicate that all the sectors are interdependent of oil price volatility but that the aggregate market index transport and telecommunication sectors are insensitive to oil price volatility during the 75th and 90th quantiles this highlights the usefulness of denoising the financial returns series when applying regression tools moreover the contagion and interdependence between the oil price and stock returns sectors are estimated by frequency domain causality

forecasting daily supermarket sales using exponentially weighted quantile regression inventory control systems typically require the frequent updating of forecasts for many different products in addition to point predictions interval forecasts are needed to set appropriate levels of safety stock the series considered in this paper are characterised by high volatility and skewness which are both time varying these features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions the widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting in this paper we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression the approach amounts to exponential smoothing of the cumulative distribution function and can be viewed as an extension of generalised exponential smoothing to quantile forecasting empirical results are encouraging with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting

the long term effects of africa s slave trades can part of africa s current underdevelopment be explained by its slave trades to explore this question i use data from shipping records and historical documents reporting slave ethnicities to construct estimates of the number of slaves exported from each country during africa s slave trades i find a robust negative relationship between the number of slaves exported from a country and current economic performance to better understand if the relationship is causal i examine the historical evidence on selection into the slave trades and use instrumental variables together the evidence suggests that the slave trades have had an adverse effect on economic development

market capitalization and value at risk abstract the potential of economic variables for financial risk measurement is an open field for research this article studies the role of market capitalization in the estimation of value at risk var we test the performance of different var methodologies for portfolios with different market capitalization we perform the analysis considering separately financial crisis periods and non crisis periods we find that var methods perform differently for portfolios with different market capitalization for portfolios with stocks of different sizes we obtain better var estimates when taking market capitalization into account we also find that it is important to consider crisis and non crisis periods separately when estimating var across different sizes this study provides evidence that market fundamentals are relevant for risk measurement

improving daily value at risk forecasts the relevance of short run volatility for regulatory quality assessment abstract in this paper we present a novel perspective on data filtering and present an innovative wavelet based approach that leads to improved value at risk var forecasts a separation of financial conditional volatility into short mid and long run components allows us to study the relevance of these frequency components with respect to a regulatory quality assessment for daily var forecasts a simulation study and an analysis of daily market prices suggest that short and mid run information components cover the relevant information that is necessary for estimating adequate daily var excluding long run information components reduces daily var forecasts by up to 4 and does not impact the quality of regulatory back testing

forecasting value at risk and expected shortfall using a semiparametric approach based on the asymmetric laplace distribution value at risk var forecasts can be produced from conditional autoregressive var models estimated using quantile regression quantile modeling avoids a distributional assumption and allows the dynamics of the quantiles to differ for each probability level however by focusing on a quantile these models provide no information regarding expected shortfall es which is the expectation of the exceedances beyond the quantile we introduce a method for predicting es corresponding to var forecasts produced by quantile regression models it is well known that quantile regression is equivalent to maximum likelihood based on an asymmetric laplace al density we allow the density s scale to be time varying and show that it can be used to estimate conditional es this enables a joint model of conditional var and es to be estimated by maximizing an al log likelihood although this estimation framework uses an al density it does not rely on an assumption for the returns distribution we also use the al log l

a linearized value at risk model with transaction costs and short selling abstract though value at risk var has been widely applied by finance industry a possibility of multiple optimums that is caused by the nonlinearity in modeling can challenge its application to deal with this technical issue we propose a linearized var model that employs the mixed 0 1 programing in lin 2009 p4 model we further advance the previous models with considering various transaction costs and the optimization of short selling weights we compare the performance of buy and hold bh strategy the mean variance mv model the original p4 model and our linearized p4 lp4 model by rebalancing a wide scale of international and alternative investments during a period between 2001 and 2012 the results of numerical tests show the superior performance of the var models to the bh and the mv portfolios the lp4 model yields the global optimum and outperforms the corresponding p4 model in both return and risk the stability of portfolio value generated from the lp4 model supports its higher effectiveness in risk management than the p4 model

forecasting the joint distribution of australian electricity prices using dynamic vine copulae abstract we consider the problem of modelling and forecasting the distribution of a vector of prices from interconnected electricity markets using a flexible class of drawable vine copula models where we allow the dependence parameters of the constituting bivariate copulae to be time varying we undertake in sample and out of sample tests using daily electricity prices and evidence that our model provides accurate forecasts of the underlying distribution and outperforms a set of competing models in their abilities to forecast one day ahead conditional quantiles of a portfolio of electricity prices our study is conducted in the australian national electricity market nem which is the most efficient power auction in the world electricity prices exhibit highly stylised features such as extreme price spikes price dependency between regional markets correlation asymmetry and non linear dependency the developed approach can be used as a risk management tool in the electricity retail industry which plays an integral role in the apparatus of modern energy markets electricity retailers are responsible for the efficient distribution of electricity while being exposed to market risk with extreme magnitudes

the impact of liquidity on portfolio value at risk forecasts abstracthistorical crisis events have highlighted the insufficiency of value at risk var as a measure of market risk because such metric does not take liquidity into account unlike previous stud

how does germany s green energy policy affect electricity market volatility an application of conditional autoregressive range models abstract based on a dynamic model for the high low range of electricity prices this article analyses the effects of germany s green energy policy on the volatility of the electricity market using european energy exchange data from 2000 to 2015 we find rather high volatility in the years 2000 2009 but also that the weekly price range has significantly declined in the period following the year 2009 this period is characterised by active regulation under the energy industry law enwg the eu emissions trading directive etd and the renewable energy law eeg in contrast to the preceding period price jumps are smaller and less frequent especially for day time hours implying that current policy measures are effective in promoting renewable energies while simultaneously upholding electricity market stability this is because the regulations strive towards a more and more flexible and market oriented structure which allows better integration of renewable energies and supports an efficient alignment of renewable electricity supply with demand

modelling interregional links in electricity price spikes abnormally high price spikes in spot electricity markets represent a significant risk to market participants as such a literature has developed that focuses on forecasting the probability of such spike events moving beyond simply forecasting the level of price many univariate time series models have been proposed to dealwith spikes within an individual market region this paper is the first to develop a multivariate self exciting point process model for dealing with price spikes across connected regions in the australian national electricity market the importance of the physical infrastructure connecting the regions on the transmission of spikes is examined it is found that spikes are transmitted between the regions and the size of spikes is influenced by the available transmission capacity it is also found that improved risk estimates are obtained when inter regional linkages are taken into account

a dynamic autoregressive expectile for time invariant portfolio protection strategies constant proportion portfolio insurance cppi is nowadays one of the most popular techniques for portfolio insurance strategies it simply consists of reallocating the risky part of a portfolio with respect to market conditions via a leverage parameter called the multiple guaranteeing a predetermined floor we propose to introduce a conditional time varying multiple as an alternative to the standard unconditional cppi method directly linked to actual risk management problematics this ex ante approach for the conditional multiple aims to diversify the risk model associated for example with the expected shortfall es or extreme risk measure estimations first we recall the portfolio insurance principles and main properties of the cppi strategy including the time invariant portfolio protection tipp strategy as introduced by estep and kritzman 1988 we emphasize the existence of an upper bound on the multiple for example to hedge against sudden drops in the market then we provide the main properties of the conditional multiples for well known financial models including the discrete time portfolio rebalancing case and levy processes to describe the risky asset dynamics for this purpose we precisely define and evaluate different gap risks in both conditional and unconditional frameworks as a by product the introduction of discrete or random time portfolio rebalancing allows us to determine and or estimate the density of durations between rebalancements finally from a more practical and statistical point of view due to trading restrictions we present the class of dynamic autoregressive expectile dare models for estimating the conditional multiple this latter approach provides useful complementary information about the risk and performance associated with probabilistic approaches to the conditional multiple

granular institutional investors and global market interdependence we study the propagation of global investment risk across markets through the granular view of institutional investors applying the conditional value at risk estimation to micro level weekly observations of international mutual funds between 2003 and 2011 we find that idiosyncratic shocks to large institutional investors explain both aggregate market risk and cross market risk interdependence conditional on the us capital markets being in financial distress idiosyncratic shocks to the top 10 largest funds investing in the us explain about 40 of the risk fluctuations in other non us markets the findings are also economically and statistically significant for the top largest funds investing in non us markets with the effects becoming especially large during the global financial crisis of 2007 09 these results are robust after controlling for common risk factors and applying alternative measures of idiosyncratic shocks

confidence intervals for conditional tail risk measures in arma garch models abstractarma garch models are widely used to model the conditional mean and conditional variance dynamics of returns on risky assets empirical results suggest heavy tailed innovations with positive extreme value index for these models hence one may use extreme value theory to estimate extreme quantiles of residuals using weak convergence of the weighted sequential tail empirical process of the residuals we derive the limiting distribution of extreme conditional value at risk cvar and conditional expected shortfall ces estimates for a wide range of extreme value index estimators to construct confidence intervals we propose to use self normalization this leads to improved coverage vis a vis the normal approximation while delivering slightly wider confidence intervals a data driven choice of the number of upper order statistics in the estimation is suggested and shown to work well in simulations an application to stock index returns documents the improvements of cvar and ces forecasts

var seductive but dangerous value at risk var has gained rapid acceptance as a valuable approach to risk management not all vars are equal however a study of var techniques used by dealers and end users reveals that var calculations differ significantly for the same portfolio vars are extremely dependent on parameters data assumptions and methodology calculation of eight common vars for three hypothetical portfolios demonstrates the potentially seductive but dangerous nature of any single approach to risk management in sum although var and other quantitative techniques are necessary aspects of an effective risk management program they are not sufficient to control risk

capturing deep tail risk via sequential learning of quantile dynamics abstract this paper develops a conditional quantile model that can learn long term and short term memories of sequential data it builds on sequential neural networks and yet outputs interpretable dynamics we apply the model to asset return time series across eleven asset classes using historical data from the 1960s to 2018 our results reveal that it extracts not only the serial dependence structure in conditional volatility but also the memories buried deep in the tails of historical prices we further evaluate its value at risk forecasts against a wide range of prevailing models our model outperforms the garch family as well as models using filtered historical simulation conditional extreme value theory and dynamic quantile regression these studies indicate that conditional quantiles of asset return have persistent sources of risk that are not coming from those responsible for volatility clustering these findings could have important implications for risk management in general and tail risk forecasts in particular

recent advances in electricity price forecasting a review of probabilistic forecasting abstract since the inception of competitive power markets two decades ago electricity price forecasting epf has gradually become a fundamental process for energy companies decision making mechanisms over the years the bulk of research has concerned point predictions however the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply demand and prices academics and practitioners alike have come to understand that probabilistic electricity price and load forecasting is now more important for energy systems planning and operations than ever before with this paper we offer a tutorial review of probabilistic epf and present much needed guidelines for the rigorous use of methods measures and tests in line with the paradigm of maximizing sharpness subject to reliability the paper can be treated as an update and a further extension of the otherwise comprehensive epf review of weron 1 or as a standalone treatment of a fascinating and underdeveloped topic that has a much broader reach than epf itself

historical origins of firm ownership structure the persistent effects of the african slave trade this paper uses evidence from the historical african slave trade to extend prior theory linking modern firm ownership structure to institutions and social capital we argue that institutions and so

the pernicious effects of contaminated data in risk management 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 profit and loss 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 market risk induced regulatory capital

energy risk management through self exciting marked point process abstract crude oil is a dynamically traded commodity that affects many economies we propose a collection of marked self exciting point processes with dependent arrival rates for extreme events in oil markets and related risk measures the models treat the time among extreme events in oil markets as a stochastic process the main advantage of this approach is its capability to capture the short medium and long term behavior of extremes without involving an arbitrary stochastic volatility model or a prefiltration of the data as is common in extreme value theory applications we make use of the proposed model in order to obtain an improved estimate for the value at risk in oil markets empirical findings suggest that the reliability and stability of value at risk estimates improve as a result of finer modeling approach this is supported by an empirical application in the representative west texas intermediate wti and brent crude oil markets

the columbian exchange a history of disease food and ideas this paper provides an overview of the long term impacts of the columbian exchange that is the exchange of diseases ideas food crops technologies populations and cultures between the new world and the old world after christopher columbus voyage to the americas in 1492 we focus on the aspects of the exchange that have been most neglected by economic studies namely the transfer of diseases food crops and knowledge between the two worlds we pay particular attention to the effects of the exchange on the old world

volatility forecasting by quantile regression quantile regression allows one to predict the volatility of time series without assuming an explicit form for the underlying distribution financial assets are known to have irregular return patterns not only the volatility but also the distribution functions themselves may vary with time so traditional time series models are often unreliable this study presents a new approach to volatility forecasting by quantile regression utilizing a uniformly spaced series of estimated quantiles the proposed method provides much more complete information on the underlying distribution without recourse to an assumed functional form based on an empirical study of seven stock indices using 16 years of daily return data the proposed approach produces better volatility forecasts for six of the seven indices

forecasting the comovements of spot interest rates abstract time varying covariance models are compared in the french and german interest rate markets of the pre euro period a bivariate asymmetric dynamic covariance model with level effect best characterizes the in sample variance covariance dynamics model comparison using economic loss functions raises some doubts with alternative models performing similarly out of sample results show that the variance covariance matrix is best forecasted using a vech model with level effect but no volatility spillover not entirely confirming the in sample evidence simple models using exponentially weighted moving averages of past observations perform similarly to the best bivariate model thus some features required to obtain a good in sample fit do not have additional out of sample forecasting power due to overfitting

tail granger causalities and where to find them extreme risk spillovers vs spurious linkages abstract identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management granger causality in tail or in risk tests whether past extreme events of a time series help predicting future extreme events of another time series the topology and connectedness of networks built with granger causality in tail can be used to measure systemic risk and to identify risk transmitters here we introduce a novel test of granger causality in tail which adopts the likelihood ratio statistic and is based on the multivariate generalization of a discrete autoregressive process for binary time series describing the sequence of extreme events of the underlying price dynamics the proposed test has very good size and power in finite samples especially for large sample size allows inferring the correct time scale at which the causal interaction takes place and it is flexible enough for multivariate extension when more than two time series are considered in order to decrease false detections as spurious effect of neglected variables an extensive simulation study shows the performances of the proposed method with a large variety of data generating processes and it introduces also the comparison with the test of granger causality in tail by hong et al 2009 we report both advantages and drawbacks of the different approaches pointing out some crucial aspects related to the false detections of granger causality for tail events an empirical application to high frequency data of a portfolio of us stocks highlights the merits of our novel approach

uncertainty persistence and heterogeneity a panel data perspective the purpose of this paper is to review newly developed identification and estimation tools that are relevant for the analysis of dynamic dependence structures of income risk i present an application to nonlinear permanent transitory models of household income using data from the panel study of income dynamics psid but the empirical approach is more generally applicable household income processes are of interest because the size of shocks the nature of their persistence and crosshousehold heterogeneity are all important to understand how income inequality varies with age and cohort and how it translates into consumption inequality i argue that going from an econometrics of autocovariances to an econometrics of flexible distributions is feasible and has the potential to reveal richer aspects of risk for example nonlinear persistence of unusual shocks jel c23 d31 d12

forecasting the var of crude oil market do alternative distributions help abstract accurate modeling of the empirical distribution of crude oil market returns is extremely important in estimating risk measures in addition to several commonly used distributions alternative distributions are explored in this study some of which account for the asymmetry and heavy tails simultaneously found in distributions and contain more tail parameters to separately depict the right and left tails when forecasting the value at risk var of crude oil markets during highly volatile periods seven backtests are also conducted to compare the var forecasting accuracy among different distributions the empirical results indicate that a highly volatile environment challenges the commonly used distributions and the four risk models based on commonly used distributions are rejected about 27 to 38 of the time the alternative distributions i e skewed general error distributions sged generalized hyperbolic skewed student t distributions ghst and generalized asymmetric student t gast distributions generally produce more accurate var measurement and gast gives the best measurement accuracy the empirical results imply that risk managers or policymakers should further consider more flexible distributions such as sged ghst or gast in particular when quantifying or managing the risk in turbulent market times

achieving fair trade through a social tariff regime a policy thought experiment this paper pursues a fair trade policy thought experiment that i presents the social index tariff structure sits regime and ii simulates a multilateral sits regime using bilateral trade flow data a sits regime seeks to promote strong labour and environmental standards and other initiatives that engender human development equality and sustainability it does this by institutionalising both the incentives and means for countries to enhance their performance in these areas we demonstrate that a multilateral sits regime would generate substantial and stable flows of development funds while incentivising an international race to the top in labour and environmental standards and more broadly in human development equality and sustainability

time varying quantile association regression model with applications to financial contagion and var abstract this paper develops a quantile association regression model which is able to capture the dynamic quantile dependence in the tails of conditional distributions the association measure the quantile specific odds ratio qor captures the tendency of two random variables being simultaneously below specific quantiles it is independent of marginal distributions and invariant to monotonic transformation and enjoys methodological advantages over popular alternatives such as the copula the ability of the qor measure to capture and forecast a range of different dependence structures is first shown via simulations in the financial application we implement the model and compute the qor on a daily basis to assess contagion for 10 stock markets during two recent crises our empirical results show that contagion exists during the us banking crisis between the us and all tested markets and between greece and the tested european markets during the euro crisis hence the model is able to capture the changes in quantile dependence between stock markets and offer a vivid description of market events in addition the model provides an accurate valuation of daily value at risk var

micro economic determinants of tourist expenditure a quantile regression approach abstract we assess the effect of the main determinants of tourist expenditure by applying both linear and quantile regression models to individual micro data collected by a survey addressed to non resident tourists who spent their holidays in sardinia during the period april october 2012 we find that in addition to income and foreign nationality tourist expenditure is crucially driven by trip related party size stay length accommodation sea and sun typology and transport modality and psychographic characteristics repeated visits and holiday motivations moreover our results indicate that the effects vary with respect to the expenditure component and the level of spending thus confirming the high complexity of the tourism product higher positive effects were found for heavy spenders in the case of foreign nationality previous visits and notoriety motivated holidays while party size and the number of visited sites contribute to reducing the level of expenditure for light spending tourists

a cge analysis of the impact of foreign direct investment and tariff reform on female and male workers in tanzania summary this study analyzes the impact on female and male workers of tariff reform and the reduction of regulatory barriers faced by domestic and foreign firms operating in business services it develops a data set that distinguishes labor by gender for 52 sectors and four skill categories the model is the first to incorporate modern trade theory to assess its gender implications the dixit stiglitz framework results in productivity gains which increase remunerations across all worker categories however this is less beneficial for women because the less skilled workers and the ones that are less involved in business services gain less

stocks and bonds flight to safety for ever abstract this paper gives new insights about flight to safety from stocks to bonds asking whether the strength of this phenomenon remains the same in the current environment of low yields the motivations lie in the conjecture that when yields are low the traditional motives of flight to safety wealth protection liquidity could not be sufficient inducing weaker flight to safety events empirical applications using data for u s government bonds and the s p 500 index show indeed that when yields are low the strength of flight to safety from stocks to bonds weakens this result holds even when controlling for the effects of traditional flight to safety factors including the vix the ted spreads and the overall level of illiquidity in the stock market moreover we develop a bivariate model of flight to safety transfers that measures to what extent the strength of flight to safety from stocks to bonds is related to the strength of flight to safety from stocks to other safe haven assets gold and currencies results show that when the strength of flight to safety from stocks to bonds decreases the strength of flight to safety from stocks to these safe haven assets increases this result holds only in the low yield environment suggesting a kind of substitution effect of save haven assets similar to the reaching for yield behavior

caviar based forecast for oil price risk abstract as a benchmark for measuring market risk value at risk var reduces the risk associated with any kind of asset to just a number amount in terms of a currency which can be well understood by regulators board members and other interested parties this paper employs a new var approach due to engle and manganelli engle r f manganelli s 2004 caviar conditional autoregressive value at risk by regression quantiles journal of business and economic statistics 22 367 381 to forecasting oil price risk in doing so we provide two original contributions by introducing a new exponentially weighted moving average caviar model and developing a mixed data regression model for multi period var prediction

are benefits from oil stocks diversification gone new evidence from a dynamic copula and high frequency data abstract oil is perceived as a good diversification tool for stock markets to fully understand this potential we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time varying joint distribution of the oil stocks pair accurately our realized garch with time varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models employing a recently proposed conditional diversification benefits measure that considers higher order moments and nonlinear dependence from tail events we document decreasing benefits from diversification over the past ten years the diversification benefits implied by our empirical model are moreover strongly varied over time these findings have important implications for asset allocation as the benefits of including oil in stock portfolios may not be as large as perceived

the dark side of global integration increasing tail dependence abstract we measure stock market coexceedances using the methodology of cappiello gerard and manganelli 2005 ecb working paper 501 this method enables us to measure comovement at each point of the return distribution first we construct annual coexceedance probabilities for both lower and upper tail return quantiles using daily data from 1974 2006 next we explain these probabilities in a panel gravity model framework results show that macroeconomic variables asymmetrically impact stock market comovement across the return distribution financial liberalization significantly increases left tail comovement whereas trade integration significantly increases comovement across all quantiles decreasing exchange rate volatility results in increasing lower tail comovement the introduction of the euro increases comovement across the entire return distribution thereby significantly reducing the benefits of portfolio diversification within the euro area

improving the value at risk forecasts theory and evidence from the financial crisis abstract the recent financial crisis has raised numerous questions about the accuracy of value at risk var as a tool to quantify extreme losses in this paper we develop data driven var approaches that are based on the principle of optimal combination and that provide robust and precise var forecasts for periods when they are needed most such as the recent financial crisis within a comprehensive comparative study we provide the latest piece of empirical evidence on the performance of a wide range of standard var approaches and highlight the overall outperformance of the newly developed methods

a nonparametric test for granger causality in distribution with application to financial contagion this article introduces a kernel based nonparametric inferential procedure to test for granger causality in distribution this test is a multivariate extension of the kernel based granger causality test in tail event the main advantage of this test is its ability to examine a large number of lags with higher order lags discounted in addition our test is highly flexible because it can be used to identify granger causality in specific regions on the distribution supports such as the center or tails we prove that the test converges asymptotically to a standard gaussian distribution under the null hypothesis and thus is free of parameter estimation uncertainty monte carlo simulations illustrate the excellent small sample size and power properties of the test this new test is applied to a set of european stock markets to analyze spillovers during the recent european crisis and to distinguish contagion from interdependence effects

point process models for extreme returns harnessing implied volatility forecasting the risk of extreme losses is an important issue in the management of financial risk there has been a great deal of research examining how option implied volatilities iv can be used to forecast asset return volatility however the role of iv in the context of predicting extreme risk has received relatively little attention the potential benefit of iv in forecasting extreme risk is considered within a range of models beginning with the traditional garch based approach along with a number of novel point process models univariate models where iv is included as an exogenous variable are considered along with a novel bivariate approach where extreme movements in iv are treated as another point process it is found that in the context of forecasting value at risk the bivariate models produce the most accurate forecasts across a wide range of scenarios

globalization and gender wage inequality in china summary this paper uses an enterprise population level dataset to investigate the link between globalization and gender inequality in the chinese labor market we find that foreign and exporting firms employ more female workers than domestic nonexporters foreign participation and export orientation within the same region and industry significantly encourage female employment and reduce the gender wage gap furthermore we show that while a large gender wage gap exists for foreign and exporting firms it mainly reflects the difference in gender productivity gender wage discrimination is observed only among private nonexporting firms overall our results highlight the importance of globalization in encouraging female employment and reducing gender discrimination

nonparametric correlation models for portfolio allocation abstract this article proposes time varying nonparametric and semiparametric estimators of the conditional cross correlation matrix in the context of portfolio allocation simulations results show that the nonparametric and semiparametric models are best in dgps with substantial variability or structural breaks in correlations only when correlations are constant does the parametric dcc model deliver the best outcome the methodologies are illustrated by evaluating two interesting portfolios the first portfolio consists of the equity sector spdrs and the s p 500 while the second one contains major currencies results show the nonparametric model generally dominates the others when evaluating in sample however the semiparametric model is best for out of sample analysis

large shocks in the volatility of the dow jones industrial average index 1928 2013 we determine the events that cause large shocks in volatility of the djia index over the period 1928 2013 using a new semi parametric test based on conditional heteroscedasticity models we find that these large shocks can be associated with particular events financial crashes elections wars monetary policies etc we show that some shocks are not identified as extraordinary movements by the investors due to their occurring during high volatility episodes especially the 1929 1934 1937 1938 and 2007 2011 periods

the effects of structural change and economic liberalisation on gender wage differentials in south korea and taiwan this paper investigates the sources of divergent trends in gender wage differentials in two important newly industrialised economies nies south korean and taiwan as these economies have entered the post industrial phase of development gender wage differentials in taiwan s manufacturing sector have widened while in korea they have narrowed decomposition analysis is used to broadly identify sources of change in gender wage differentials multivariate regression analysis is relied on to differentiate the impact on the gender wage gap of 1 macro levels policies 2 institutional factors and 3 shifts in labour demand and supply in addition to the predictable effects of several standard supply side variables in taiwan physical capital mobility is found to have contributed to a wider gender earnings gap women s greater concentration in industries where capital is mobile may explain this result the effects of capital mobility in korea appears to differ which may be due to the dissimilar characters of outward fdi from that country copyright 2000 by oxford university press

generating volatility forecasts from value at risk estimates statistical volatility models rely on the assumption that the shape of the conditional distribution is fixed over time and that it is only the volatility that varies the recently proposed conditional autoregressive value at risk caviar models require no such assumption and allow quantiles to be modeled directly in an autoregressive framework although useful for risk management caviar models do not provide volatility forecasts such forecasts are needed for several other important applications such as option pricing and portfolio management it has been found that for a variety of probability distributions there is a surprising constancy of the ratio of the standard deviation to the interval between symmetric quantiles in the tails of the distribution such as the 0 025 and 0 975 quantiles this result has been used in decision and risk analysis to provide an approximation of the standard deviation in terms of quantile estimates provided by experts drawing on the same result we construct financial volatility forecasts as simple functions of the interval between caviar forecasts of symmetric quantiles forecast comparison using five stock indices and 20 individual stocks shows that the method is able to outperform generalized autoregressive conditional heteroskedasticity garch models and moving average methods

gender wage inequality and export led growth in south korea this article investigates the relationship between gender wage inequality and export led growth in south korea the persistent gender wage gap in korea s manufacturing sector is found to be linked to women s segregation in the country s major export industries where real wage growth has lagged productivity growth despite favorable market conditions that might drive up women s wages relative to those of men the interaction of state and firm level hiring training and promotion practices that structure women s and men s employment opportunities differently appear to have resulted in a relatively weaker fall back position for women in labour markets econometric results are consistent with the hypothesis that women s weaker fall back position limits their ability to bargain for wage increases commensurate with productivity growth further evidence is presented which links gender wage inequality to the growth of korean exports