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on Forecasting |
| By: | Laura Garcia-Jorcano (Department of Economic Analysis and Finance (Area of Financial Economics), Facultad de Ciencias Jurídicas y Sociales Universidad de Castilla-La Mancha, Toledo, Spain.); Alfonso Novales (Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad Complutense, 28223 Madrid, Spain.) |
| Abstract: | We introduce three dominance criteria to compare the performance of alternative VaR forecasting models. The three criteria use the information provided by a battery of VaR validation tests based on the frequency and size of exceedances, offering the possibility of efficiently summarizing a large amount of statistical information. They do not require the use of any loss function defined on the difference between VaR forecasts and observed returns, and two of the criteria are not conditioned on any significance level for the VaR tests. We use them to explore the potential for 1-day ahead VaR forecasting of some recently proposed asymmetric probability distributions for return innovations, as well as to compare the APARCH and FGARCH volatility specifications with more standard alternatives. Using 19 assets of different nature, the three criteria lead to similar conclusions, suggesting that the unbounded Johnson SU, the skewed Student-t and the skewed Generalized-t distributions seem to produce the best VaR forecasts. The added flexibility of a free power parameter in the conditional volatility in the APARCH and FGARCH models leads to a better fit to return data, but it does not improve upon the VaR forecasts provided by GARCH and GJR-GARCH volatilities. |
| Keywords: | Value at risk; Backtesting; Forecast evaluation; Dominance; Conditional volatility models; Asymmetric distributions. |
| JEL: | C52 C58 G17 G32 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1923&r=all |
| By: | Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Konstantinos Gkillas (Department of Business Administration, University of Patras – University Campus, Rio, P.O. Box 1391, 26500 Patras, Greece); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We analyze the predictive power of time-varying risk aversion for the realized volatility of crude oil returns based on high-frequency data. While the popular linear heterogeneous autoregressive realized volatility (HAR-RV) model fails to recognize the predictive power of risk aversion over crude oil volatility, we find that risk aversion indeed improves forecast accuracy at all forecast horizons when we compute forecasts by means of random forests. The predictive power of risk aversion is robust to various covariates including realized skewness and realized kurtosis, various measures of jump intensity and leverage. The findings highlight the importance of accounting for nonlinearity in the data-generating process for forecast accuracy as well as the predictive power of non-cashflow factors over commodity-market uncertainty with significant implications for the pricing and forecasting in these markets. |
| Keywords: | Oil price, Realized volatility, Risk aversion, Random forests |
| JEL: | G17 Q02 Q47 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201972&r=all |
| By: | Oguzhan Cepni (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara,Turkey); I. Ethem Guney (Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara,Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA; School of Business and Economics, Loughborough University, Leicestershire, LE11 3TU, UK) |
| Abstract: | In this paper, we develop a new investor sentiment index that is aligned with the purpose of predicting the excess returns on government bonds of the United States (US) of maturities of 2-, 3-, 4-, 5-year. By eliminating a common noise component in underlying sentiment proxies using the partial least squares (PLS) approach, the new index is shown to have much greater predictive power than the original principal component analysis (PCA)-based sentiment index both in- and out-of-sample, with the predictability being statistically significant, especially for bond premia with shorter maturities, even after controlling for a large number of financial and macro factors, as well as investor attention and manager sentiment indexes. Given the role of Treasury securities in forecasting of output and inflation, and portfolio allocation decisions, our findings have significant implications for investors, policymakers and researchers interested in accurately forecasting return dynamics for these assets. |
| Keywords: | Bond premia, Investor attention, Investor sentiment, Predictability, Out-of-sample forecasts |
| JEL: | C22 C53 G12 G17 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201973&r=all |
| By: | Robert Lehmann |
| Abstract: | In this study, we systematically evaluate the potential of a bunch of survey-based indicators from different economic branches to forecasting export growth across a multitude of European countries. Our pseudo out-of-sample analyses reveal that the best-performing indicators beat a well-specified benchmark model in terms of forecast accuracy. It turns out that four indicators are superior: the Export Climate, the Production Expectations of domestic manufacturing firms, the Industrial Confidence Indicator, and the Economic Sentiment Indicator. Two robustness checks confirm these results. As exports are highly volatile and turn out to be a large demand-side component of gross domestic product, our results can be used by applied forecasters in order to choose the best-performing indicators and thus increasing the accuracy of export forecasts. |
| Keywords: | export forecasting, export expectations, export climate, Europe |
| JEL: | F01 F10 F17 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7846&r=all |
| By: | Fearghal Kearney; Han Lin Shang; Lisa Sheenan |
| Abstract: | Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in the implied volatility surfaces of the most actively traded commodity options between 2006 and 2016. Adopting a rolling out-of-sample forecasting framework that addresses the common multiple comparisons problem, we establish that, for energy and precious metals options, explicitly modeling the term structure of implied volatility using the Nelson-Siegel factors produces the most accurate forecasts. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.11009&r=all |
| By: | Alfonso Novales (Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad Complutense, 28223 Madrid, Spain.); Laura Garcia-Jorcano (Department of Economic Analysis and Finance (Area of Financial Economics), Facultad de Ciencias Jurídicas y Sociales Universidad de Castilla-La Mancha, Toledo, Spain.) |
| Abstract: | We use stock market data to analyze the quality of alternative models and procedures for fore- casting expected shortfall (ES) at different significance levels. We compute ES forecasts from conditional models applied to the full distribution of returns as well as from models that focus on tail events using extreme value theory (EVT). We also apply the semiparametric filtered historical simulation (FHS) approach to ES forecasting to obtain 10-day ES forecasts. At the 10-day hori- zon we also combine FHS with EVT. The performance of the different models is assessed using six different ES backtests recently proposed in the literature. Our results suggest that conditional EVT-based models produce more accurate 1-day and 10-day ES forecasts than do non-EVT based models. Under either approach, asymmetric probability distributions for return innovations tend to produce better forecasts. Incorporating EVT in parametric or semiparametric approaches also improves ES forecasting performance. These qualitative results are also valid for the recent crisis period, even though all models then underestimate the level of risk. FHS narrows the range of numerical forecasts obtained from alternative models, thereby reducing model risk. Combining EVT and FHS seems to be best approach for obtaining accurate ES forecasts. |
| Keywords: | Extreme value theory; Skewed distributions; Expected shortfall; Backtesting; Filtered historical simulation. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1924&r=all |
| By: | Laura Coroneo; Fabrizio Iacone; Fabio Profumo |
| Abstract: | We evaluate the real-time predictive ability of density forecasts from the European Central Bank’s Survey of Professional Forecasters (ECB SPF) using the Diebold and Mariano (1995) and West (1996) test. As the sample size for the ECB SPF is fairly small, we use fixed-b and fixed-m asymptotics to alleviate size distortions. We verify in an original Monte Carlo design that fixed-smoothing asymptotics delivers correctly sized tests in this framework. Empirical results indicate that ECB SPF density forecasts for unemployment and real GDP growth beat simple benchmarks at one-year horizon. ECB SPF density forecasts for inflation instead do not easily outperform simple benchmarks, as up to 2008 ECB SPF inflation expectations are close to the target. After 2008, we find that the predictive ability of the ECB SPF is more conspicuous for all variables, even though inflation expectations are still loosely anchored to the target. |
| Keywords: | real-time density forecast evaluation, ECB SPF, Diebold-Mariano-West test, fixed-smoothing asymptotics |
| JEL: | C12 C22 E17 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:yor:yorken:19/14&r=all |
| By: | Patrick McAllister; Ilir Nase |
| Abstract: | Building on previous research evaluating the IPF Consensus Forecasts, this study updates and expands upon the existing body of work. The paper evaluates forecasting accuracy at the sector level and assesses the extent to which the consensus forecasts were able to predict the relative performance of each sector. It also evaluates the performance of the implied yield forecasts and concludes that it is failure in yield forecasting that is the main source of failure in forecasts of capital growth and total returns. The ability of the consensus forecasts to identify the best and worst performing sectors was relatively good with a high level of agreement between the actual and forecasted sector rankings. |
| Keywords: | Forecasting performance accuracy. Consensus. Commercial real estate. |
| JEL: | R3 |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2019_374&r=all |
| By: | Laura Garcia-Jorcano (Department of Economic Analysis and Finance (Area of Financial Economics), Facultad de Ciencias Jurídicas y Sociales, Universidad de Castilla-La Mancha, Toledo, Spain.); Alfonso Novales (Instituto Complutense de Análisis Económico (ICAE), and Department of Economic Analysis, Facultad de Ciencias Económicas y Empresariales, Universidad Complutense, 28223 Madrid, Spain.) |
| Abstract: | We provide evidence suggesting that the assumption on the probability distribution for return in- novations is more influential for Value at Risk (VaR) performance than the conditional volatility specification. We also show that some recently proposed asymmetric probability distributions and the APARCH and FGARCH volatility specifications beat more standard alternatives for VaR fore- casting, and they should be preferred when estimating tail risk. The flexibility of the free power parameter in conditional volatility in the APARCH and FGARCH models explains their better performance. Indeed, our estimates suggest that for a number of financial assets, the dynamics of volatility should be specified in terms of the conditional standard deviation. We draw our results on VaR forecasting performance from i) a variety of backtesting approaches, ii) the Model Confi- dence Set approach, as well as iii) establishing a ranking among alternative VaR models using a precedence criterion that we introduce in this paper. |
| Keywords: | Value-at-risk; Backtesting; Evaluating forecasts; Precedence; APARCH model; Asym- metric distributions. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:ucm:doicae:1926&r=all |
| By: | Norbert Pfeifer |
| Abstract: | The validation of house price value remains a critical task for scientific research as well as for practitioners. The following paper investigates this challenge by integrating textual-based information contained in real estate descriptions. More specifically, we show different approaches surrounding how to integrate verbal descriptions from real estate advertisements in an automated valuation model. By using Airbnb listing data, we address the proposed methods against a traditional hedonic-based approach, where we show that a neural network-based prediction model—featuring only information from verbal descriptions—are able to outperform a traditional hedonic-based model estimated with physical attributes, such as bathrooms or/and bedrooms. We also draw attention to techniques that allow for interrelations between physical, locational, and qualitative, text-based attributes. The results strongly suggest the integration of textual information, specifically modelled in a 2-stage model architecture in which the first model (recurrent long short-term memory network) outputs a probability distribution over price classifications, which is then used along with quantitative measurements in a stacked feed-forward neural network. |
| Keywords: | AVM; housing; Neural Network; NLP |
| JEL: | R3 |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2019_329&r=all |
| By: | Marcelo Cajias |
| Abstract: | In the era of internet and digitalization real estate prices of dwellings are predominantly collected live by multiple listing services and merged with supporting data such as spatio-temporal geo-information. Despite the computational requirements for analyzing such large datasets, the methods for analyzing big data have evolved substantially and go much far beyond the traditional regression. In this context, the usage of machine learning technologies for analyzing prices in the real estate industry is not commonplace. This paper applies machine learnings algorithms on a data set of more than 3 Mio. observations in the German residential market to explore the predicting accuracy of methods such as the random forests regressions, XGboost and the stacked regression among others. The results show a significant reduction in the forecasting variance and confirm that artificial intelligence understands real estate prices much deeper. |
| Keywords: | Big Data in real estate; German housing; Machine learning Algorithms; Random forest; XGBoost |
| JEL: | R3 |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2019_232&r=all |
| By: | Andrea Berardi (Ca Foscari University of Venice - Dipartimento di Economia); Michael Markovich (Investment Strategy - Private Banking Wealth Management; Vienna Institute of Finance); Alberto Plazzi (Swiss Finance Institute; USI Lugano); Andrea Tamoni (Rutgers, The State University of New Jersey - Rutgers Business School at Newark & New Brunswick; London School of Economics & Political Science (LSE)) |
| Abstract: | We show that the difference between the natural rate of interest and the current level of monetary policy stance, dubbed Convergence Gap (CG), contains information that is valuable for bond predictability. Adding CG in forecasting regressions of bond excess returns significantly raises the R-squared, and restores countercyclical variation in bond risk premia that is otherwise missed by forward rates. The convergence gap also predicts changes in future yields, and consistently plays the role of an unspanned variable within an affine term structure framework. The importance of the gap remains robust out-of-sample, and in countries other than the U.S. Furthermore, its inclusion brings significant economic gains in the context of dynamic conditional asset allocation. |
| Keywords: | Bond Risk Premia, Forward Rates, Monetary Policy, Natural Rate of Interest, Bond Predictability |
| JEL: | E0 E43 G0 G12 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp1952&r=all |
| By: | Marcelo Cajias; Jonas Willwersch; Felix Lorenz |
| Abstract: | Real estate transactions can be seen as a spatial point pattern over space and time. That means, that real estate transactions occur in places where at a certain point of time certain characteristics are given that lead to an investment decision. While the decision-making process by investors is impossible to capture, this paper applies new methods for capturing the conditions under which real estate transactions are made over space and time. In other words, we explain and forecast real estate transactions with machine learning methods including both real estate transactions, geographical information and most importantly microeconomic data. |
| Keywords: | Machine Learning; Point pattern analysis; Real estate transactions; Spatial-temporal analysis; Surveillance analysis |
| JEL: | R3 |
| Date: | 2019–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2019_171&r=all |
| By: | Daiki Matsunaga; Toyotaro Suzumura; Toshihiro Takahashi |
| Abstract: | Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and thus limited to certain experimental setups to obtain good prediction results. On the other hand, professional investors additionally use their rich knowledge of inter-market and inter-company relations to map the connectivity of companies and events, and use this map to make better market predictions. For instance, they would predict the movement of a certain company's stock price based not only on its former stock price trends but also on the performance of its suppliers or customers, the overall industry, macroeconomic factors and trade policies. This paper investigates the effectiveness of work at the intersection of market predictions and graph neural networks, which hold the potential to mimic the ways in which investors make decisions by incorporating company knowledge graphs directly into the predictive model. The main goal of this work is to test the validity of this approach across different markets and longer time horizons for backtesting using rolling window analysis.In this work, we concentrate on the prediction of individual stock prices in the Japanese Nikkei 225 market over a period of roughly 20 years. For the knowledge graph, we use the Nikkei Value Search data, which is a rich dataset showing mainly supplier relations among Japanese and foreign companies. Our preliminary results show a 29.5% increase and a 2.2-fold increase in the return ratio and Sharpe ratio, respectively, when compared to the market benchmark, as well as a 6.32% increase and 1.3-fold increase, respectively, compared to the baseline LSTM model. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.10660&r=all |