Out Of Sample Forecasts Research Articles

Financial and real sector Leading indicators of Recessions in Brazil using Probabilistic Models

We examine the usefulness of various financial and real sector variables to forecast recessions in Brazil between one and eight quarters ahead. We estimate probabilistic models of recession and select models based on their outof-sample forecasts, using the Receiver Operating Characteristic (ROC) function. We find that the predictive out-of-sample ability of several models vary depending on the numbers of quarters ahead to forecast and on the number of regressors used in the model specification. The models selected seem to be relevant to give early warnings of recessions in Brazil.

Modeling Long Memory in Volatility for Spot Price of Lentil with Multi-step Ahead Out-of-sample Forecast Using AR-FIGARCH Model

The potential presence of long memory (LM) properties in return and volatility of the spot price of lentil in Indore market has been investigated. Geweke and Porter-Hudak (1983) (GPH) method has been applied to test for presence of long range dependence in the volatility processes for the series. Stationarity of the series has been tested using Augmented Dickey-Fuller (ADF) unit root test and Philips-Peron (PP) unit root test. It is observed that both the log returns as well as squared log returns series are stationary at level but there is a significant presence of long memory in squared log return series. Accordingly, AR-FIGARCH model was applied for forecasting the volatility of lentil price. To this end, window based evaluation of forecasting is carried out with the help of Mean squares prediction error (MSPE), Root MSPE (RMSPE), Mean absolute prediction error (MAPE) and Relative MAPE (RMAPE). The residuals of the fitted models were used for diagnostic checking. Out-of sample forecast of volatility has been computed for 1st June-31st July, 2015 along with the percentage deviation from the actual price. The maximum deviation has been found to be 2.55%. The R software package has been used for data analysis.

APLIKASI ALGORITMA GENETIKA UNTUK MERAMALKAN KONSUMSI PREMIUM KOTA DENPASAR

This research aimed to forecast the gasoline demand at Denpasar using genetic algorithm method. This algorithm was selected because of easy to implement and its ability to find acceptable solution quickly. This algorithm works by searching the best individu according to fitness function defined. The series data used in the research were 60 observations of monthly gasoline demand at Denpasar for period January 2009 through December 2013. By observing the Partial Autocorrelation Function (PACF) plot, we found the last lag before the series become stationer was sixth lag. Based on this finding, we decided the best individu was represented by six genes. This individu, in addition, was used to make in-sample forecasting. The forecasted data had mean absolute error (MAE) as much as 553,27 kiloliters. For one semester out-of sample forecast, we found gasoline consumption fluctuated with lowest and highest consumption were for February 2014 and June 2014, respectively.

Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets

Volatility forecasting is an imperative research field in financial markets and crucial component in most financial decisions. Nevertheless, which model should be used to assess volatility remains a complex issue as different volatility models result in different volatility approximations. The concern becomes more complicated when one tries to use the forecasting for asset distribution and risk management purposes in the linked regional markets. This paper aims at observing the effectiveness of the contending models of statistical and econometric volatility forecasting in the three South-east Asian prominent capital markets, i.e. STI, KLSE, and JKSE. In this paper, we evaluate eleven different models based on two classes of evaluation measures, i.e. symmetric and asymmetric error statistics, following Kumar's (2006) framework. We employ 10-year data as in sample and 6-month data as out of sample to construct and test the models, consecutively. The resulting superior methods, which are selected based on the out of sample forecasts and some evaluation measures in the respective markets, are then used to assess the markets cointegration. We find that the best volatility forecasting models for JKSE, KLSE, and STI are GARCH (2,1), GARCH(3,1), and GARCH (1,1), respectively. We also find that international portfolio investors cannot benefit from diversification among these three equity markets as they are cointegrated.

Nowcasting and Forecasting Economic Growth in the Euro Area Using Principal Components

Many empirical studies have shown that factor models produce relatively accurate forecasts compared to alternative short-term forecasting models. These empirical ndings have been established for dierent macroeconomic data sets and dierent forecast horizons. However, various specications of the factor model exist and it is a topic of debate which specication is most eective in its forecasting performance. Furthermore, the forecast performances of the dierent specications during the recent nancial crisis are also not well documented. In this study we investigate these two issues in depth. We empirically verify the forecast performance of three factor model approaches and report our ndings in an extended empirical out-ofsample forecasting competition for quarterly growth of gross domestic product in the euro area and its ve largest countries over the period 1992-2012. We also introduce two extensions of existing factor models to make them more suitable for real-time forecasting. We show that the factor models have been able to systematically beat the benchmark autoregressive model, both before as well as during the nancial crisis. The recently proposed collapsed dynamic factor model shows the highest forecast accuracy for the euro area and the majority of countries that we have analyzed. The forecast precision improvements against the benchmark model can range up to 77% in mean square error reduction, depending on the country and forecast horizon.

An occurrence based regime switching model to improve forecasting

Purpose – The purpose of this paper is to propose an occurrence-based model to improve the forecasting of regime switches so as to assist decision making. Design/methodology/approach – This paper proposes a novel model where occurrences of relationships are taken into account when forecasting. Taiwan Stock Exchange Capitalization Weighted Stock Index is taken as the forecasting target. Findings – Due to the consideration of occurrences of relationships in forecasting, the out of sample forecasting is improved. Practical implications – The proposed model can be applied to forecast other time series for regime switches. In addition, it can be integrated with other time series models to improve forecasting performance. Originality/value – The empirical results show that the proposed model can improve the forecasting performance.

Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach

Financial asset prices occasionally exhibit large changes. To deal with their occurrence, observed return series are assumed to consist of a conditionally Gaussian ARMA–GARCH type model contaminated by an additive jump component. In this framework, a new test for additive jumps is proposed. The test is based on standardized returns, where the first two conditional moments of the non-contaminated observations are estimated in a robust way. Simulation results indicate that the test has very good finite sample properties, i.e. correct size and high proportion of correct jump detection. The test is applied to daily returns and detects less than 1% of jumps for three exchange rates and between 1% and 3% of jumps for about 50 large capitalization stock returns from the NYSE. Once jumps have been filtered out, all series are found to be conditionally Gaussian. It is also found that simple GARCH-type models estimated using filtered returns deliver more accurate out-of sample forecasts of the conditional variance than GARCH and Generalized Autoregressive Score (GAS) models estimated from raw data.

Can a simple structural time series model beat the random walk?

This paper tries to address the question that if the long run PPP holds, then there should exist a structural model which can outperform the random walk in out of sample forecasting. We propose an ARFIMA based model with log of the independent variable as an explanatory variable and make a comparison study of this structural model with the benchmark random walk model. Then, we compare our results with that as obtained by Engel and Hamilton, and by Clarida, Sarno, Taylor and Valente. We present the standard ARFIMA model and show how can make an extension of it so that it becomes a variant of ARFIMA and name it as YQ-ARFIMA, then construct a bivariate model relating the dependent variable yt and ln yt , and with that, perform an impulse response function analysis of the predictive ability of ln yt . We also transform the YQ-ARFIMA into a moving average representation, and thereafter perform the impulse response function analysis again, then make a comparison study between the standard ARFIMA and the YQARFIMA by comparing the out of sample forecasting ability of each one of them with the benchmark random walk model. After that, compare the performance of YQ-ARFIMA with that of the Markov switching model put forward by Engel and Hamilton, and the MSIH(3)-VECM as put forward by CSTV. Last, we test the robustness of the YQ-ARFIMA by fitting it into different exchange rate series spanning the five continents of the globe, then, test the consistency of the forecast by YQ-ARFIMA by a cointegration technique. By using the loss functions RMSE and MAPE, cointegration consistency in forecasts and impulse response function analysis, we have shown beyond doubt that theYQ-ARFIMA model is very much superior in forecasting ability.

Essay on Wavelet analysis and the European term structure of interest rates

We analyse the generalized, dynamic Nelson-Siegel approach including five factors to ensures the absence of arbitrage. In contrast to previous empirical analyses we define our risk factors so that they are observable and determine their significance using a series of cross sectional regressions. We then decompose the risk factors and test whether they are significant on each time scale. The results allow us to distinguish expected and unexpected components which are used in out of sample forecasts. We find good forecasting abilities of this approach; the one month forecast remains high even during times of financial market distress.

Forecasting of Nonlinear DSGE Model

A medium-scale nonlinear DSGE model is estimated (54 variables, 29 state variables, 7 observed variables). The model includes stock market. RMSE of in sample and out of sample forecasts are calculated. The nonlinear DSGE model with measurement errors outperform AR(1), VAR(1), linearized DSGE in terms of out of sample forecasts quality. The nonlinear DSGE model without measurement errors has actually equal quality with linearized DSGE model.

Forecasting Wholesale Price of Pigeon Pea Using Long Memory Time-Series Models

The fractional integration is a generalization of integer integration, under which time-series are usually presumed to be integrated of order zero or one. In this regard, the autoregressive fractionally integrated moving-average (ARFIMA) model along with its estimation procedure is investigated. ARFIMA model searches for a non-integer differencing parameter d to difference the data to capture long memory. The model has been applied for modelling and forecasting of daily wholesale price of pigeon pea (Cajanas cajan) in the Amritsar and Bhatinda markets and the all-India maximum, minimum and modal prices of pigeon pea. Augmented Dickey-Fuller (ADF) test and Philips Perron (PP) test have been used for testing the stationarity of the series. Autocorrelation (ACF) and partial autocorrelation (PACF) functions have shown a slow hyperbolic decay indicating the presence of long memory. In all the five price series, long memory parameters are found to be significant. On the basis of minimum AIC values, the best model was identified for each series. To this end, evaluation of forecasting was carried out with root mean squares prediction error (RMSPE), mean absolute prediction error (MAPE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models have been used for diagnostic checking. Out-of sample forecast of wholesale prices of pigeon pea has been computed up to February, 2014. The R software package has been used for data analysis.

Do Economies Stall? The International Evidence

A “stalling” economy has been defined as one that experiences a discrete deterioration in economic performance following a decline in its growth rate to below some threshold level. Previous efforts to identify stalls have focused primarily on the US economy, with the threshold level being chosen endogenously, and have suggested that the concept of a stall may be useful for macroeconomic forecasting. We examine the international evidence for stalling in a panel of 51 economies using two different definitions of a stall threshold (time-invariant and related to lagged average growth rates) and two complementary empirical approaches (insample statistical significance and out-of-sample forecast performance). We find that the evidence for stalling based on time-invariant thresholds is limited: only 12 of the 51 economies in our sample experience statistically significant stalls, and including a stall threshold generally results in only modest improvements to out-ofsample forecast performance. When we instead model the stall threshold as varying with average growth rates, the number of economies with statistically-significant stalls actually declines (to nine), but in 71% of the cases we examine, including a stall threshold results in an improvement in out-of-sample forecast performance.

Research on Credit Risk Measurement Based on Uncertain KMV Model

Regarding KMV model identification credit risk profile of small and medium-sized listed companies, at present, domestic scholars has made some achievements in the process of the KMV model combined with China’s national conditions. In this paper, we will amend the model by using uncertain interest rate instead of fixed rate on the basis of existing research. Comparing the uncertain KMV model to traditional KMV model with ST-listed companies and non-ST-listed companies in Shanghai and Shenzhen stock exchange, we find that it performs slightly better as a predictor in uncertain KMV model and in out of sample forecasts.

Statistical Modelling of Temperature Risk

Recently the topic of global warming has become very popular. The literature has concentrated its attention on the evidence of such eect, either by detecting regime shifts or change points in time series. The majority of these methods are designed to nd shifts in mean, but only few can do this for the variance. In this paper we attempt to investigate the statistical evidence of global warming by identi- fying shifts in seasonal mean of daily average temperatures over time and in seasonal variance of temperature residuals. We present a time series approach for modelling temperature dynamics. A seasonal mean Lasso-type technique based with a multi- plicative structure of Fourier and GARCH terms in volatility is proposed. The model describes well the stylised facts of temperature: seasonality, intertemporal correla- tions and the heteroscedastic behaviour of residuals. The application to European temperature data indicates that the multiplicative model for the seasonal variance performs better in terms of out of sample forecast than other models proposed in the literature for modelling temperature dynamics. We study the dynamics of the seasonal variance by implementing quantile and expectile functions with condence corridor to detrended and deseasonalized residuals. We show that shifts in seasonal mean and variance vary from location to location, indicating that all sources of trends other than mean and variance would rise trends over spatial scales. The local eects of temperature risk support the existence of global warming.

Chinese saving dynamics: the impact of GDP growth and the dependent share

China's national saving rate rose rapidly in the 2000s after declining through the late 1990s. These dynamics are not readily explained by the precautionary motive, the institutional distribution of income, or reform related processes in general. Rather, we find a compelling explanation lies with GDP growth fluctuations and demographic shifts. We estimate a vector autoregressive model for the period 1978-2008, then generate in-sample simulations that successfully replicate the 2000s run-up in the saving rate. Our out of sample forecasts show the saving rate dropping in the 2010s as the dependent share falls and GDP growth moderates.

Leverage as a predictor for real activity and volatility

This paper explores the link between the leverage of the US financial sector, of households and of non-financial businesses, and real activity. We document that leverage is negatively correlated with the future growth of real activity, and positively linked to the conditional volatility of future real activity and of equity returns. The joint information in sectoral leverage series is more relevant for predicting future real activity than the information contained in any individual leverage series. Using in-sample regressions and out-of sample forecasts, we show that the predictive power of leverage is roughly comparable to that of macro and financial predictors commonly used by forecasters. Leverage information would not have allowed to predict the ‘Great Recession’ of 2008–2009 any better than conventional macro/financial predictors.

Banking Systemic Vulnerabilities: A Tail-Risk Dynamic CIMDO Approach

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology of Segoviano, and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks? default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in three forms: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks and; (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust outof-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.

The Euro Area Yield Curve: An Analysis with Nelson-Siegel Type Models

We present Nelson-Siegel-type yield curves for the Euro Area, by studying the in-sample fit and the out-of sample forecasting properties. Moreover, we add macro variables (output gap, EONIA and HICP or alternatively Eurocoin) to estimate the interactions with yield curve factors (level, slope and curvature), following the rationale of Diebold et al (2006). We use a two step procedure as in Diebold and Li (2006) and we find three interesting results. The Svensson model has better in-sample fit properties with respect to the Nelson-Siegel model. There exist interactions among both the level and the curvature with macroeconomic variables, while on the other hand, the slope interacts with all macroeconomic variables. The addiction of a business cycle indicator like Eurocoin gives better results with respect to both yields-only and alterative yields-macro models, confirming the success of parsimonious models for out-of sample forecasting.

A Medium Scale Forecasting Model for Monetary Policy

This paper presents a 16-variable Bayesian VAR forecasting model of the U.S. economy for use in a monetary policy setting. The variables that comprise the model are selected not only for their effectiveness in forecasting the primary variables of interest, but also for their relevance to the monetary policy process. In particular, the variables largely coincide with those of an augmented New-Keynesian DSGE model. We provide out-of sample forecast evaluations and illustrate the computation and use of predictive densities and fan charts. Although the reduced form model is the focus of the paper, we also provide an example of structural analysis to illustrate the macroeconomic response of a monetary policy shock.

The impact of overnight returns on realized volatility

We obtain intraday data on three stock indices listed on the Taiwan Stock Exchange (TWSE), and then analyse the data by incorporating an overnight returns indicator into the ‘Heterogeneous Auto-Regressive’ (HAR) model of realized volatility. Our overall aim is to enhance the forecasting of future volatility. Our findings demonstrate that the modified model significantly improves the forecasting performance of future realized volatility, with our results also being found to continue to hold for both in sample and out of sample forecasts.