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on Forecasting |
| By: | Degiannakis, Stavros |
| Abstract: | Two volatility forecasting evaluation measures are considered; the squared one-day-ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error standardized with its volatility. The statistical properties of the forecast errors point the standardized version as a more appropriate metric for evaluating volatility forecasts. We highlight the importance of standardizing the forecast errors with their volatility. The predictive accuracy of the models is investigated for the FTSE100, DAX30 and CAC40 European stock indices and the exchange rates of Euro to British Pound, US Dollar and Japanese Yen. Additionally, a trading strategy defined by the standardized forecast errors provides higher returns compared to the strategy based on the simple forecast errors. The exploration of forecast errors is paving the way for rethinking the evaluation of ultra-high frequency realized volatility models. |
| Keywords: | ARFIMA model, HAR model, intra-day data, predictive ability, realized volatility, ultra-high frequency modelling. |
| JEL: | C14 C32 C50 G11 G15 |
| Date: | 2016–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:80163&r=for |
| By: | Siem Jan S.J. Koopman (VU Amsterdam, The Netherlands; CREATES, Aarhus University, Denmark; Tinbergen Institute, The Netherlands); Rutger Lit (VU Amsterdam, The Netherlands) |
| Abstract: | We develop a new dynamic multivariate model for the analysis and the forecasting of football match results in national league competitions. The proposed dynamic model is based on the score of the predictive observation mass function for a high-dimensional panel of weekly match results. Our main interest is to forecast whether the match result is a win, a loss or a draw for each team. To deliver such forecasts, the dynamic model can be based on three different dependent variables: the pairwise count of the number of goals, the difference between the number of goals, or the category of the match result (win, loss, draw). The different dependent variables require different distributional assumptions. Furthermore, different dynamic model specifications can be considered for generating the forecasts. We empirically investigate which dependent variable and which dynamic model specification yield the best forecasting results. In an extensive forecasting study, we consider match results from six large European football competitions and we validate the precision of the forecasts for a period of seven years for each competition. We conclude that our preferred dynamic model for pairwise counts delivers the most precise forecasts and outperforms benchmark and other competing models. |
| Keywords: | Football; Forecasting; Score-driven models; Bivariate Poisson; Skellam; Ordered probit; Probabilistic loss function |
| JEL: | C32 |
| Date: | 2017–07–05 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20170062&r=for |
| By: | Menden, Christian; Proaño, Christian R. |
| Abstract: | The analysis of the financial cycle and its interaction with the macroeconomy has become a central issue for the design of macroprudential policy since the 2007-08 financial crisis. This paper proposes the construction of financial cycle measures for the US based on a large data set of macroeconomic and financial variables. More specifically, we estimate three synthetic financial cycle components that account for the majority of the variation in the data set using a dynamic factor model. We investigate whether these financial cycle components have significant predictive power for economic activity, inflation and short-term interest rates by means of Granger causality tests in a factor-augmented VAR set-up. Further, we analyze if the synthetic financial cycle components have significant forecasting power for the prediction of economic recessions using dynamic probit models. Our main findings indicate that all financial cycle measures improve the quality of recession forecasts significantly. In particular, the factor related to financial market participants' uncertainty and risk aversion - related to Rey's (2013) global financial cycle - seems to serve as an appropriate early warning indicator for policymakers. |
| Keywords: | financial cycle,dynamic factor model,Granger causality,recession forecasting,dynamic probit models,early warning systems |
| JEL: | C35 C38 C52 C53 E32 E47 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bamber:126&r=for |
| By: | Korbinian Dress; Stefan Lessmann; Hans-J\"org von Mettenheim |
| Abstract: | Leasing is a popular channel to market new cars. Pricing a leasing contract is complicated because the leasing rate embodies an expectation of the residual value of the car after contract expiration. To aid lessors in their pricing decisions, the paper develops resale price forecasting models. A peculiarity of the leasing business is that forecast errors entail different costs. Identifying effective ways to address this characteristic is the main objective of the paper. More specifically, the paper contributes to the literature through i) consolidating and integrating previous work in forecasting with asymmetric cost of error functions, ii) systematically evaluating previous approaches and comparing them to a new approach, and iii) demonstrating that forecasting with asymmetric cost of error functions enhances the quality of decision support in car leasing. For example, under the assumption that the costs of overestimating resale prices is twice that of the opposite error, incorporating corresponding cost asymmetry into forecast model development reduces decision costs by about eight percent, compared to a standard forecasting model. Higher asymmetry produces even larger improvements. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1707.02736&r=for |
| By: | Michael Funke; Julius Loermann; Richhild Moessner |
| Abstract: | We derive risk-neutral probability densities for future euro/Swiss franc exchange rates as implied by option prices. We find that the credibility of the Swiss franc floor somewhat decreased as the spot exchange rate approached the lower bound of 1.20 CHF per euro. We also compare the forecasting performance of a random walk benchmark model with an error-correction model (ECM) augmented with option-implied break probabilities of breaching the currency floor. We find some evidence that the augmented ECM has an informational advantage over the random walk when using one-month break probabilities. But we find that one-month option-implied densities cannot predict the entire range of exchange rate realizations. |
| Keywords: | Swiss franc, forecasting, options, risk-neutral probability densities |
| JEL: | C53 F31 F37 |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:bis:biswps:652&r=for |
| By: | Fabian Goessling; Martina Danielova Zaharieva |
| Abstract: | We propose a new and highly exible Bayesian sampling algorithm for non-linear state space models under non-parametric distributions. The estimation framework combines a particle filtering and smoothing algorithm for the latent process with a Dirichlet process mixture model for the error term of the observable variables. In particular, we overcome the problem of constraining the models by transformations or the need for conjugate distributions. We use the Chinese restaurant representation of the Dirichlet process mixture, which allows for a parsimonious and generally applicable sampling algorithm. Thus, our estimation algorithm combines a pseudo marginal Metropolis Hastings scheme with a marginalized hierarchical semi-parametric model. We test our approach for several nested model specifications using simulated data and provide density forecasts. Furthermore, we carry out a real data example using S&P 500 returns. |
| Keywords: | Bayesian Nonparametrics, Particle Filtering, Stochastic Volatility, MCMC, Forecasting |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:6417&r=for |
| By: | Wilson Ye Chen; Gareth W. Peters; Richard H. Gerlach; Scott A. Sisson |
| Abstract: | We offer a novel way of thinking about the modelling of the time-varying distributions of financial asset returns. Borrowing ideas from symbolic data analysis, we consider data representations beyond scalars and vectors. Specifically, we consider a quantile function as an observation, and develop a new class of dynamic models for quantile-function-valued (QF-valued) time series. In order to make statistical inferences and account for parameter uncertainty, we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution. Compared to modelling realised measures, modelling the entire quantile functions of intra-daily returns allows one to gain more insight into the dynamic structure of price movements. Via simulations, we show that the proposed MCMC algorithm is effective in recovering the posterior distribution, and that the posterior means are reasonable point estimates of the model parameters. For empirical studies, the new model is applied to analysing one-minute returns of major international stock indices. Through quantile scaling, we further demonstrate the usefulness of our method by forecasting one-step-ahead the Value-at-Risk of daily returns. |
| Date: | 2017–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1707.02587&r=for |