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on Forecasting |
| By: | Jon Danielsson; Kevin R. James; Marcela Valenzuela; Ilknur Zer |
| Abstract: | This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with and caused by market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings, hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, with a no obvious way to identify which method is the best. Finally, we discuss the main problems in risk forecasting for macro prudential purposes and propose an evaluation criteria for such models. |
| Keywords: | Value-at-Risk; expected shortfall; systemic risk; model risk; CoVaR; MES;financial stability; risk management; Basel III |
| JEL: | J1 |
| Date: | 2014–04–30 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:59296&r=for |
| By: | Domenico Giannone; Francesca Monti; Lucrezia Reichlin |
| Abstract: | This paper shows how and when it is possible to obtain a mapping from a quarterly DSGE model to amonthly specification thatmaintains the same economic restrictions and has real coefficients. We use this technique to derive the monthly counterpart of the Gali et al (2011) model. We then augment it with auxiliary macro indicators which, because of their timeliness, can be used to obtain a now-cast of the structural model. We show empirical results for the quarterly growth rate of GDP, the monthly unemployment rate and the welfare relevant output gap defined in Gali, Smets andWouters (2011). Results show that the augmented monthly model does best for now-casting. |
| Keywords: | DSGE models; forecasting; temporal aggregation; mixed frequency data; large datasets |
| JEL: | C33 C53 E30 |
| Date: | 2014–06–03 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:57998&r=for |
| By: | Audrino, Francesco; Camponovo, Lorenzo; Roth, Constantin |
| Abstract: | A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine efficient parameter estimation, variable selection, and valid inference for time series processes. In an application to several constituents of the S&P 500 index it is shown that (i) the optimal significant lag structure is time-varying and subject to drastic regime shifts that seem to happen across assets simultaneously; (ii) in many cases the relevant information for prediction is included in the first 22 lags, corroborating previous results concerning the accuracy and the difficulty of outperforming out-of-sample the heterogeneous autoregressive (HAR) model; and (iii) some common features of the optimal lag structure can be identified across assets belonging to the same market segment or showing a similar beta with respect to the market index. |
| Keywords: | Realized volatility; Adaptive lasso; HAR model; Test for false positives; Lag structure |
| JEL: | C12 C58 C63 |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2015:01&r=for |
| By: | Vivek Ghosal; Yang Ye |
| Abstract: | We examine the impact of uncertainty on employment dynamics. Alternative measures of uncertainty are constructed based on the survey of professional forecasters, and regressionbased forecasting models for GDP growth, inflation, S&P500 stock price index, and fuel prices. Our results indicate that greater uncertainty has a negative impact on growth of employment, and the effects are primarily felt by the relatively smaller businesses; the impact on large businesses are generally non-existent or weaker. Our results suggest that to truly understand the effects of uncertainty on employment dynamics, we need to focus on the relatively smaller and entrepreneurial businesses. We discuss implications for the framing of economic policy. |
| Keywords: | Employment;Business enterprises;Entrepreneurship;Forecasting models;Regression analysis;uncertainty, employment, small businesses, entrepreneurship, real-options, financing constraints. |
| Date: | 2015–01–14 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:15/4&r=for |
| By: | Sebastien Lleo; William T. Ziemba |
| Abstract: | In this paper, we extend the literature on crash prediction models in three main respects. First, we relate explicitly crash prediction measures and asset pricing models. Second, we present a simple, effective statistical significance test for crash prediction models. Finally, we propose a definition and a measure of robustness for crash prediction models. We apply the statistical test and measure the robustness of selected model specifications of the Price-Earnings (P/E) ratio and Bond Stock Earning Yield Differential (BSEYD) measures. This analysis suggests that the BSEYD, the logarithmic BSEYD model, and to a lesser extent the P/E ratio, are statistically significant robust predictors of equity market crashes. |
| Keywords: | stock market crashes; bond-stock earnings yield mode; Fed model; priceearnings-ratio |
| JEL: | J1 |
| Date: | 2014–03–15 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:59290&r=for |
| By: | J. E. Wesen; V. VV. Vermehren; H. M. de Oliveira |
| Abstract: | This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares (Petrobras ON) traded in the Brazilian Stock Market. The first-rate choices of the window length and the number of filter coefficient are evaluated. This is done by observing the correlation between the predictor signal and the actual course performed by the market in terms of both the window prevision length and filter coefficient values. It is shown that such adaptive predictors furnish, on the average, very substantial profit on the invested amount. |
| Date: | 2015–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1501.07504&r=for |
| By: | Ron Peretz; Gilad Bavly |
| Abstract: | A decision maker observes the evolving state of the world while constantly trying to predict the next state given the history of past states. The ability to benefit from such predictions depends not only on the ability to recognize patters in history, but also on the range of actions available to the decision maker. We assume there are two possible states of the world. The decision maker is a gambler who has to bet a certain amount of money on the bits of an announced binary sequence of states. If he makes a correct prediction he wins his wager, otherwise he loses it. We compare the power of betting strategies (aka martingales) whose wagers take values in different sets of reals. A martingale whose wagers take values in a set A is called an A-martingale. A set of reals B anticipates a set A, if for every A-martingale there is a countable set of B-martingales, such that on every binary sequence on which the A- martingale gains an infinite amount at least one of the B-martingales gains an infinite amount, too. We show that for two important classes of pairs of sets A and B, B anticipates A if and only if the closure of B contains r A, for some positive r. One class is when A is bounded and B is bounded away from zero; the other class is when B is well ordered (has no left-accumulation points). Our results generalize several recent results in algorithmic randomness and answer a question posed by Chalcraft et al. (2012). |
| Keywords: | repeated games; gambling; algorithmic randomness; pseudo-randomness; predictability |
| JEL: | C72 C73 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:59542&r=for |
| By: | Massimiliano Caporin (University of Padova); Eduardo Rossi (University of Pavia); Paolo Santucci De Magistris (University of Aahrus) |
| Abstract: | The realized volatility of financial returns is characterized by persistence and occurrence of unpredictable large increments. To capture those features, we introduce the Multiplicative Error Model with jumps (MEM-J). When a jump component is included in the multiplicative specification, the conditional density of the realized measure is shown to be a countably infinite mixture of Gamma and K distributions. Strict stationarity conditions are derived. A Monte Carlo simulation experiment shows that maximum likelihood estimates of the model parameters are reliable even when jumps are rare events. We estimate alternative specifications of the model using a set of daily bipower measures for 7 stock indexes and 16 individual NYSE stocks. The estimates of the jump component confirm that the probability of jumps dramatically increases during the financial crises. Compared to other realized volatility models, the introduction of the jump component provides a sensible improvement in the fit, as well as for in-sample and out-of-sample volatility tail forecasts. |
| Keywords: | Multiplicative Error Model with Jumps, Jumps in volatility, Realized measures, Volatility-at-Risk. |
| JEL: | C22 C58 G10 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:pad:wpaper:0186&r=for |