Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation

This study provides a comprehensive analysis of the possible influences of jump dynamics, heavy-tails, and skewness with regard to VaR estimates through the assessment of both accuracy and efficiency. To this end, the ARJI model, and its degenerative GARCH model with normal, GED, and skewed normal (SN) distributions were adopted to capture the properties of time-varying volatility, time-varying jump intensity, heavy-tails and skewness, for a range of stock indices across international stock markets during the period of the U.S. subprime mortgage crisis. Empirical results show that, with regard to the evaluation of accuracy, the role of jump dynamics is more substantial than heavy-tails or skewness as it pertains to VaR accuracy at the 90% and 95% levels, while heavy-tails become more important at the 99% level for a long position. However, the influence of the abovementioned properties on VaR estimation does not appear substantial for a short position. In addition, the properties of jump dynamics and skewness appear to be beneficial for the improvement of efficiency.     

Empirical determination of aggregate demand and supply curves: The example of the RWI Business Cycle Model

Macroeconometric models are often criticised for being too complex and difficult to read in theoretical terms. To overcome these difficulties, Hickman suggested the calculation of a model's implicit aggregate demand/supply (AD/AS) structure. The method helps to understand models and their main properties in theoretical terms and facilitates detailed model comparisons. This paper uses the AD/AS–IS/LM apparatus to analyse the simulation properties of the RWI (Rheinisch-Westfälisches Institut für Wirtschaftsforschung) Business Cycle Model, a medium-sized short-term macroeconometric model for Germany. The results confirm theoretical expectations for AD and AS elasticities and reveal particular reactions linked to peculiar model specifications such as an endogenous government sector. The results are also much in line with a previous study in a multi-country model context.     

South African stock return predictability in the context data mining: The role of financial variables and international stock returns

In this paper, we examine the predictive ability, both in-sample and the out-of-sample, for South African stock returns using a number of financial variables, based on monthly data with an in-sample period covering 1990:01 to 1996:12 and the out-of-sample period of 1997:01 to 2010:04. We use the t-statistic corresponding to the slope coefficient in a predictive regression model for in-sample predictions, while for the out-of-sample, the MSE-F and the ENC-NEW tests statistics with good power properties were utilised. To guard against data mining, a bootstrap procedure was employed for calculating the critical values of both the in-sample and out-of-sample test statistics. Furthermore, we use a procedure that combines in-sample general-to-specific model selection with out-of-sample tests of predictive ability to further analyse the predictive power of each financial variable. Our results show that, for the in-sample test statistic, only the stock returns for our major trading partners have predictive power at certain short and long run horizons. For the out-of-sample tests, the Treasury bill rate and the term spread together with the stock returns for our major trading partners show predictive power both at short and long run horizons. When accounting for data mining, the maximal out-of-sample test statistics become insignificant from 6-months onward suggesting that the evidence of the out-of-sample predictability at longer horizons is due to data mining. The general-to-specific model shows that valuation ratios contain very useful information that explains the behaviour of stock returns, despite their inability to predict stock return at any horizon. The model also highlights the role of multiple variables in predicting stock returns at medium- to long run horizons.     

Modelling conditional heteroskedasticity: Application to the “IBEX-35” stock-return index

This paper compares alternative time-varying volatility models for daily stock-returns using data from Spanish equity index IBEX-35. Specifically, we estimate a parametric family of models of generalized autoregressive heteroskedasticity (which nests the most popular symmetric and asymmetric GARCH models), a semiparametric GARCH model, the generalized quadratic ARCH model, the stochastic volatility model, the Poisson Jump Diffusion model and, finally, a nonparametric model. Those models which use conditional standard deviation (specifically, TGARCH and AGARCH models) produce better fits than all other GARCH models. We also compare the within sample predictive power of all models using a standard efficiency test. Our results show that the asymmetric behaviour of responses is a statistically significant characteristic of these data. Moreover, we observe that specifications with a distribution which allows for fatter tails than a normal distribution do not necessarily outperform specifications with a normal distribution.     

Predictions of growth in U.S. corporate profits: Asymmetric vs. symmetric loss

This study evaluates the Federal Reserve and private forecasts of growth in corporate profits for 1984-2004. These forecasts are both rational and directionally accurate but suggest different loss structures. The Federal Reserve forecasts tend to significantly under-predict and imply asymmetric loss. The private forecasts, however, are free of such bias, suggesting symmetric loss. Given that the Federal Reserve forecasts are made to help with policymaking, our findings point to the Fed's cautiousness not to incorrectly predict the downward moves in growth in corporate profits. The private forecasts are made by experts who (with a strong profit-motivated interest) attempt to generate financial gain and thus predict the upward moves as accurately as the downward moves.     

Real-time data revisions and the PCE measure of inflation

This paper tracks data revisions in the Personal Consumption Expenditure using the exclusions-from-core inflation persistence model. Keeping the number of observations the same, the regression parameters of earlier vintages of real-time data, beginning with vintage 1996:Q1, are tested for coincidence against the regression parameters of the last vintage of real-time data, used in this paper, which is vintage 2008:Q2 in a parametric and two nonparametric frameworks. The effects of data revisions are not detectable in the vast majority of cases in the parametric model, but the flexibility of the two nonparametric models is able to utilize the data revisions.     

Volatility forecasting using related markets’ information for the Tokyo stock exchange

Due to lack of information, volatility cannot be estimated via a high-frequency approach when markets are non-trading. In this paper, we focus on volatility forecasting for the Tokyo Stock Exchange (TSE) using high-frequency data of related assets traded in international markets when TSE is closed. We use the heterogenous autoregressive model to identify an optimal approach of this additional information for the ten largest TSE-listed stocks, TOPIX and Nikkei 225. The usefulness of harnessing global and neighbour market information in forecasting the TSE market volatility is confirmed through in-depth empirical analysis. Our findings have important implications for investors and policy makers.     

Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach

Abstract Quantifying the probability of U.S. recessions has become increasingly important since August 2007. In a data‐rich environment, this paper is the first to apply a Probit model to common factors extracted from a large set of explanatory variables to model and forecast recession probability. The results show the advantages of the proposed approach over many existing models. Simulated real‐time analysis captures all recessions since 1980. The proposed model also detects a significant jump in the next six‐month recession probability based on data up to November 2007, one year before the formal declaration of the recent recession by the NBER.     

Forecast content and content horizons for some important macroeconomic time series

Abstract.  The information content of statistical forecasts of approximately stationary quantities tends to decline as the forecast horizon increases, and there exists a maximum horizon beyond which forecasts cannot provide discernibly more information about the variable than is present in the unconditional mean (the content horizon ). The pattern of decay of forecast content (or skill) with increasing horizon is well known for many types of meteorological forecasts; by contrast, little generally accepted information about these patterns or content horizons is available for economic variables. In this paper we estimate content horizons for a variety of macroeconomic quantities; more generally, we characterize the pattern of decay of forecast content as we project farther into the future. We find a wide variety of results for the different macroeconomic quantities, with models for some quantities providing useful content several years into the future, for other quantities providing negligible content beyond one or two months or quarters.     

General equilibrium, wariness and efficient bubbles

Wary consumers overlook gains but not losses in remote sets of dates or states. As preferences are upper but not lower Mackey semi-continuous, Bewley?s (1972) [4] result on existence of equilibrium whose prices are not necessarily countably additive holds. Wariness is related to lack of myopia and to ambiguity aversion (and, therefore, to Bewley?s (1986) [6] work on Knightian uncertainty). Wary infinite lived agents have weaker transversality conditions allowing them to be creditors at infinity and for bubbles to occur in positive net supply assets completing the markets. There are efficient allocations that can only be implemented with asset bubbles.     

Conditional volatility and correlations of weekly returns and the VaR analysis of 2008 stock market crash

Modelling of conditional volatilities and correlations across asset returns is an integral part of portfolio decision making and risk management. Over the past three decades there has been a trend towards increased asset return correlations across markets, a trend which has been accentuated during the recent financial crisis. We shall examine the nature of asset return correlations using weekly returns on futures markets and investigate the extent to which multivariate volatility models proposed in the literature can be used to formally characterize and quantify market risk. In particular, we ask how adequate these models are for modelling market risk at times of financial crisis. In doing so we consider a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and show that the t-DCC model passes the usual diagnostic tests based on probability integral transforms, but fails the value at risk (VaR) based diagnostics when applied to the post 2007 period that includes the recent financial crisis.     

Multi-horizon inflation forecasts using disaggregated data

In this paper we use multi-horizon evaluation techniques to produce monthly inflation forecasts for up to twelve months ahead. The forecasts are based on individual seasonal time series models that consider both, deterministic and stochastic seasonality, and on disaggregated Consumer Price Index (CPI) data. After selecting the best forecasting model for each index, we compare the individual forecasts to forecasts produced using two methods that aggregate hierarchical time series, the bottom-up method and an optimal combination approach. Applying these techniques to 16 indices of the Mexican CPI, we find that the best forecasts for headline inflation are able to compete with those taken from surveys of experts.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.     

Default and liquidity regimes in the bond market during the 2002–2012 period

Using a real‐time random regime shift technique, we identify and discuss two different regimes in the dynamics of credit spreads during 2002–2012: a liquidity regime and a default regime. Both regimes contribute to the patterns observed in credit spreads. The liquidity regime seems to explain the predictive power of credit risk on the 2007–2009 NBER recession, whereas the default regime drives the persistence of credit spreads over the same recession. Our results complement the recent dynamic structural models as well as monetary and credit supply effects models by empirically supporting two important patterns in credit spreads: the persistence and the predictive ability toward economic downturns.