nep-for New Economics Papers
on Forecasting
Issue of 2015‒09‒05
23 papers chosen by
Rob J Hyndman
Monash University

  1. Supervision in Factor Models Using a Large Number of Predictors By Lorenzo Boldrini; Eric Hillebrand
  2. Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions By Teixeira de Carvalho Guillén, Osmani; Hecq, Alain; Victor Issler, João; Saraiva, Diogo
  3. The impact of oil price on South African GDP growth: A Bayesian Markov Switching-VAR analysis By Mehmet Balcilar; Reneé van Eyden; Josine Uwilingiye; Rangan Gupta
  4. The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach By Lorenzo Boldrini; Eric Hillebrand
  5. Forecasting Exchange Rates Using Time Series Analysis: The sample of the currency of Kazakhstan By Daniya Tlegenova
  6. The Role of Economic Policy Uncertainty in Predicting U.S. Recessions: A Mixed-Frequency Markov-Switching Vector Autoregressive Approach By Mehmet Balcilar; Rangan Gupta; Mawuli Segnon
  7. Automatic model selection for forecasting Brazilian stock returns By CUNHA, Ronan; VALLS PEREIRA, Pedro L.
  8. Electricity Price Forecasting using Sale and Purchase Curves: The X-Model By Florian Ziel; Rick Steinert
  9. Consumption-Wealth Ratio and Expected Stock Returns: Evidence from Panel Data on G7 Countries By Castro, Andressa Monteiro de; Issler, João Victor
  10. Microfounded Forecasting By Gaglianone, Wagner Piazza; Issler, João Victor
  11. Do Swedish Consumer Confidence Indicators Do What They Are Intended to Do? By Assarsson, Bengt; Österholm, Pär
  12. Forecast comparison with nonlinear methods for Brazilian industrial production By ROCHA, Jordano Vieira; VALLS PEREIRA, Pedro L.
  13. Contemporary Airport Demand Forecasting: Choice Models and Air Transport Forecasting By Benedikt Mandel
  14. Forecasting stock market returns over multiple time horizons By Kroujiline, Dimitri; Gusev, Maxim; Ushanov, Dmitry; Sharov, Sergey V.; Govorkov, Boris
  15. An Extrapolative Model of House Price Dynamics By Glaeser, Edward L.; Nathanson, Charles G.
  16. Forecasting the Global Mean Sea Level, a Continuous-Time State-Space Approach By Lorenzo Boldrini
  17. A Microsimulation Model for Educational Forecasting By Niels Erik Kaaber Rasmussen; Peter Stephensen
  18. Explaining and forecasting bank loans. Good times and crisis (in french) By G.Levieuge
  19. Data-driven warehouse optimization By Matusiak, M.; de Koster, M.B.M.; Saarinen, J.
  20. En the coherence and the predictive content of the French Bank Lending Survey’s indicators (in French) By G.Levieuge
  21. Inflation-Forecast Targeting: Applying the Principle of Transparency By Kevin Clinton; Charles Freedman; Michel Juillard; Ondra Kamenik; Douglas Laxton; Hou Wang
  22. Quantitative Analysis of Technology Futures: A review of Techniques, Uses and Characteristics By Tommaso Ciarli; Alex Coad; Ismael Rafols
  23. Early warning of large volatilities based on recurrence interval analysis in Chinese stock markets By Zhi-Qiang Jiang; Askery A. Canabarro; Boris Podobnik; H. Eugene Stanley; Wei-Xing Zhou

  1. By: Lorenzo Boldrini (Aarhus University and CREATES); Eric Hillebrand (Aarhus University and CREATES)
    Abstract: In this paper we investigate the forecasting performance of a particular factor model (FM) in which the factors are extracted from a large number of predictors. We use a semi-parametric state-space representation of the FM in which the forecast objective, as well as the factors, is included in the state vector. The factors are informed of the forecast target (supervised) through the state equation dynamics. We propose a way to assess the contribution of the forecast objective on the extracted factors that exploits the Kalman filter recursions. We forecast one target at a time based on the filtered states and estimated parameters of the state-space system. We assess the out-of-sample forecast performance of the proposed method in a simulation study and in an empirical application, comparing its forecasts to the ones delivered by other popular multivariate and univariate approaches, e.g. a standard dynamic factor model with separate forecast and state equations.
    Keywords: state-space system, Kalman filter, factor model, supervision, forecasting JEL classification: C32, C38, C55
    Date: 2015–08–24
  2. By: Teixeira de Carvalho Guillén, Osmani; Hecq, Alain; Victor Issler, João; Saraiva, Diogo
    Abstract: Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.
    Date: 2015–02–26
  3. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Famagusta, Northern Cyprus , via Mersin 10, Turkey; Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Reneé van Eyden (Department of Economics, University of Pretoria); Josine Uwilingiye (Department of Economics, University of Pretoria); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: One characteristic of many macroeconomic and financial time series is their asymmetric behaviour during different phases of a business cycle. Oil price shocks have been amongst those economic variables that have been identified in theoretical and empirical literature to predict the phases of business cycles. However, the role of oil price shocks to determine business cycle fluctuations has received less attention in emerging and developing economies. The aim of this study is to investigate the role of oil price shocks in predicting the phases of the South African business cycle associated with higher and lower growth regimes. By adopting a regime dependent analysis, we investigate the impact of oil price shocks under two phases of the business cycle, namely high and low growth regimes. As a net importer of oil, South Africa is expected to be vulnerable to oil price shocks irrespective of the phase of the business cycle. Using a Bayesian Markov switching vector autoregressive (MS-VAR) model and data for the period 1960Q2 to 2013Q3, we found the oil price to have predictive content for real output growth under the low growth regime. The results also show the low growth state to be shorter-lived compared to the higher growth state. against standard forecasting models. U.S. inflation forecasts improve when controlling for persistence and economic policy uncertainty (EPU). Importantly, the VARFIMA model, comprising of inflation and EPU, outperforms commonly used inflation forecast models.
    Keywords: Macroeconomic fluctuations; oil price shocks; Bayesian Markov switching VAR;
    JEL: C32 E32 Q43
    Date: 2014
  4. By: Lorenzo Boldrini (Aarhus University and CREATES); Eric Hillebrand (Aarhus University and CREATES)
    Abstract: We study the forecast power of the yield curve for macroeconomic time series, such as consumer price index, personal consumption expenditures, producer price index, real disposable income, unemployment rate, and industrial production. We employ a state-space model in which the forecasting objective is included in the state vector. This amounts to an augmented dynamic factor model in which the factors (level, slope, and curvature of the yield curve) are supervised for the macroeconomic forecast target. In other words, the factors are informed about the dynamics of the forecast objective. The factor loadings have the Nelson and Siegel (1987) structure and we consider one forecast target at a time. We compare the forecasting performance of our specification to benchmark models such as principal components regression, partial least squares, and ARMA(p,q) processes. We use the yield curve data from G¨urkaynak, Sack, and Wright (2006) and Diebold and Li (2006) and macroeconomic data from FRED. We compare the models by means of the conditional predictive ability test of Giacomini and White (2006). We find that the yield curve has more forecast power for real variables compared to inflation measures and that supervising the factor extraction for the forecast target can improve forecast performance.
    Keywords: state-space system, Kalman filter, factor model, supervision, forecasting, yield curve. JEL classification: C32, C38, E43
    Date: 2015–08–24
  5. By: Daniya Tlegenova
    Abstract: This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper is to apply the ARIMA model for forecasting of yearly exchange rates of USD/KZT, EUR/KZT and SGD/KZT. The accuracy of the forecast is compared with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE).
    Date: 2015–08
  6. By: Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Turkey; Department of Economics, University of Pretoria, South Africa); Rangan Gupta (Department of Economics, University of Pretoria); Mawuli Segnon (Department of Economics, University of Kiel, Germany)
    Abstract: This paper analyzes the performance of the monthly economic policy uncertainty (EPU) index in predicting recessionary regimes of the (quarterly) U.S. GDP. In this regard, we apply a mixed-frequency Markov-switching vector autoregressive (MF-MSVAR) model, and compare its in-sample and out-of-sample forecasting performances to those of a Markov-switching vector autoregressive model (MS-VAR, where the EPU is averaged over the months to produce quarterly values) and a Markov-switching autoregressive (MS-AR) model. Our results show that the MF-MS-VAR fits the different recession regimes, and provides out-of-sample forecasts of recession probabilities which are more accurate than those derived from the MS-VAR and MS-AR models. Our results highlight the importance of using high-frequency values of the EPU, and not averaging them to obtain quarterly values, when forecasting recessionary regimes for the U.S. economy.
    Keywords: Business cycles, Economic policy uncertainty, Mixed frequency, Markovswitching VAR models
    JEL: E32 E37 C32
    Date: 2015–08
  7. By: CUNHA, Ronan; VALLS PEREIRA, Pedro L.
    Abstract: This study aims to contribute on the forecasting literature in stock return for emerging markets. We use Autometrics to select relevant predictors among macroeconomic, microeconomic and technical variables. We develop predictive models for the Brazilian market premium, measured as the excess return over Selic interest rate, Itaú SA, Itaú-Unibanco and Bradesco stock returns. We find that for the market premium, an ADL with error correction is able to outperform the benchmarks in terms of economic performance. For individual stock returns, there is a trade o between statistical properties and out-of-sample performance of the model.
    Date: 2015–08–07
  8. By: Florian Ziel; Rick Steinert
    Abstract: Our paper aims to model and forecast the electricity price in a completely new and promising style. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model as X-Model, as almost every deregulated electricity price is simply the result of the intersection of the electricity supply and demand curve at a certain auction. Therefore we show an approach to deal with a tremendous amount of auction data, using a subtle data processing technique as well as dimension reduction and lasso based estimation methods. We incorporate not only several known features, such as seasonal behavior or the impact of other processes like renewable energy, but also completely new elaborated stylized facts of the bidding structure. Our model is able to capture the non-linear behavior of the electricity price, which is especially useful for predicting huge price spikes. Using simulation methods we show how to derive prediction intervals. We describe and show the proposed methods for the day-ahead EPEX spot price of Germany and Austria.
    Date: 2015–09
  9. By: Castro, Andressa Monteiro de; Issler, João Victor
    Abstract: Using the theoretical framework of Lettau and Ludvigson (2001), we perform an empirical investigation on how widespread is the predictability of cay { a modi ed consumption-wealth ratio { once we consider a set of important countries from a global perspective. We chose to work with the set of G7 countries, which represent more than 64% of net global wealth and 46% of global GDP at market exchange rates. We evaluate the forecasting performance of cay using a panel-data approach, since applying cointegration and other time-series techniques is now standard prac- tice in the panel-data literature. Hence, we generalize Lettau and Ludvigson's tests for a panel of important countries. We employ macroeconomic and nancial quarterly data for the group of G7 countries, forming an unbalanced panel. For most countries, data is available from the early 1990s until 2014Q1, but for the U.S. economy it is available from 1981Q1 through 2014Q1. Results of an exhaustive empirical investigation are overwhelmingly in favor of the predictive power of cay in forecasting future stock returns and excess returns.
    Date: 2015–07
  10. By: Gaglianone, Wagner Piazza; Issler, João Victor
    Abstract: Our focus is on information in expectation surveys that can now be built on thousands (or millions) of respondents on an almost continuous-time basis (big data) and in continuous macroeconomic surveys with a limited number of respondents. We show that, under standard microeconomic and econometric techniques, survey forecasts are an affine function of the conditional expectation of the target variable. This is true whether or not the survey respondent knows the data-generating process (DGP) of the target variable or the econometrician knows the respondent s individual loss function. If the econometrician has a mean-squared-error risk function, we show that asymptotically efficient forecasts of the target variable can be built using Hansen s (Econometrica, 1982) generalized method of moments in a panel-data context, when N and T diverge or when T diverges with N xed. Sequential asymptotic results are obtained using Phillips and Moon s (Econometrica, 1999) framework. Possible extensions are also discussed.
    Date: 2015–05
  11. By: Assarsson, Bengt (National Institute of Economic Research); Österholm, Pär (National Institute of Economic Research)
    Abstract: In this paper, we investigate whether the two main consumer confidence indicators available for Sweden – that of the National Institute of Economic Research and that of the European Commission – can nowcast Swedish household consumption expenditure. In a simulated out-of sample nowcast exercise, we find that the consumer confidence indicator of the National Institute of Economic Research appears most useful for this purpose. The root mean square error of the nowcast from the model employing this indicator is the lowest of all the studied models which rely on survey data. The nowcasting performance of the model using the consumer confidence indicator of the European Commission is less impressive; while it outperforms the simplest possible benchmark model, its root mean square error is considerably higher than that of the model relying on the consumer confidence indicator of the National Institute of Economic Research. An implication of our findings is that while the European Commission’s survey programme may have been successful in creating a set of harmonised data for the member countries of the European Union, it is not obvious that the harmonised indicators are the most relevant ones for analysis, nowcasting or forecasting in each country.
    Keywords: Household consumption; Nowcasting
    JEL: E21 E27
    Date: 2015–09–01
  12. By: ROCHA, Jordano Vieira; VALLS PEREIRA, Pedro L.
    Abstract: This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.
    Date: 2015–07–27
  13. By: Benedikt Mandel
    Abstract: This paper describes the econometric system approach developed by MKmetric to perform short and long-term air transport demand forecasts while considering various determinants such as socio-economy, policy, infrastructure and land use. The necessities for modelling air transport evoking from a transport system point of view and the changes of the aviation world occurred during the last decade are investigated. Based on these findings the mathematical framework is outlined considering shortfalls of traditional models used in aviation forecasting and restrictions caused by classical functional forms. The increasing gap of information for modelling is described and alternative data sources used for the development of the system approach are listed. As all models are imperfect describing just a part of real life, it sheds a light on the necessity to validate models and the prerequisite of complexity needed to cope with multi-sector scenario simulations for strategic, tactical and operational developments as well as political decisions. Finally some analysis examples demonstrate the power of the approach used focusing on the choice modelling reflecting consumers’ behaviour.
    Keywords: consumer behaviour
    Date: 2014–04
  14. By: Kroujiline, Dimitri; Gusev, Maxim; Ushanov, Dmitry; Sharov, Sergey V.; Govorkov, Boris
    Abstract: In this paper we seek to demonstrate the predictability of stock market returns and explain the nature of this return predictability. To this end, we further develop the news-driven analytic model of the stock market derived in Gusev et al. (2015). This enables us to capture market dynamics at various timescales and shed light on mechanisms underlying certain market behaviors such as transitions between bull- and bear markets and the self-similar behavior of price changes. We investigate the model and show that the market is nearly efficient on timescales shorter than one day, adjusting quickly to incoming news, but is inefficient on longer timescales, where news may have a long-lasting nonlinear impact on dynamics attributable to a feedback mechanism acting over these horizons. Using the model, we design the prototypes of algorithmic strategies that utilize news flow, quantified and measured, as the only input to trade on market return forecasts over multiple horizons, from days to months. The backtested results suggest that the return is predictable to the extent that successful trading strategies can be constructed to harness this predictability.
    Keywords: stock market dynamics, return predictability, price feedback, market efficiency, news analytics, sentiment evolution, agent-based modeling, Ising, dynamical systems, synchronization, self-similar behavior, regime transitions, news-based strategies, algorithmic trading
    JEL: G02 G12 G14 G17
    Date: 2015–08–18
  15. By: Glaeser, Edward L. (Harvard University); Nathanson, Charles G. (Northwestern University)
    Abstract: A modest approximation by homebuyers leads house prices to display three features that are present in the data but usually missing from perfectly rational models: momentum at one-year horizons, mean reversion at five-year horizons, and excess longer-term volatility relative to fundamentals. Valuing a house involves forecasting the current and future demand to live in the surrounding area. Buyers forecast using past transaction prices. Approximating buyers do not adjust for the expectations of past buyers, and instead assume that past prices reflect only contemporaneous demand, as with a capitalization rate formula. Consistent with survey evidence, this approximation leads buyers to expect increases in the market value of their homes after recent house price increases, to fail to anticipate the price busts that follow booms, and to be overconfident in their assessments of the housing market.
    Date: 2015–03
  16. By: Lorenzo Boldrini (Aarhus University and CREATES)
    Abstract: In this paper we propose a continuous-time, Gaussian, linear, state-space system to model the relation between global mean sea level (GMSL) and the global mean temperature (GMT), with the aim of making long-term projections for the GMSL. We provide a justification for the model specification based on popular semi-empirical methods present in the literature and on zero-dimensional energy balance models. We show that some of the models developed in the literature on semi-empirical models can be analysed within this framework. We use the sea-level data reconstruction developed in Church and White (2011) and the temperature reconstruction from Hansen et al. (2010). We compare the forecasting performance of the proposed specification to the procedures developed in Rahmstorf (2007b) and Vermeer and Rahmstorf (2009). Finally, we compute projections for the sea-level rise conditional on the 21st century SRES temperature scenarios of the IPCC fourth assessment report. Furthermore, we propose a bootstrap procedure to compute confidence intervals for the projections, based on the method introduced in Rodriguez and Ruiz (2009).
    Keywords: energy balance model, semi-empirical model, state-space system, Kalman filter, forecasting, temperature, sea level, bootstrap JEL classification: C32
    Date: 2015–08–24
  17. By: Niels Erik Kaaber Rasmussen; Peter Stephensen (Danish Rational Economic Agents Model, DREAM)
    Abstract: A dynamic microsimulation model for forecasting educational patterns is presented. At the level of individuals the model simulates lifetime educational behavior, resulting in a long term forecast of the general educational level in Denmark. The model is a light-weight, dynamic, multithreaded and closed microsimulation model using discrete time. Data on the full Danish population is used as the initial population. Each individual is characterized by age, gender, origin, educational attainment and current educational status. Future demographic events such as births, deaths, immigration and emigration are projected in a separate group-based model and given as input. In the model individuals lives their life?s independently to decrease time-complexity and to utilize the potential of the multithreaded environment. Transition probabilities are calculated from historical educational behavior using Danish register data. The historical observations are linked to a range of background variables (such as gender, age, origin, current participation in education, study length and educational attainment). Prior to running the model, transition probabilities are computed using conditional inference trees. This data-mining approach groups together observations with similar characteristics and responses based on statistical tests. This paper describes the features of the model, briefly presents some results and points to the potential of the model in terms of policy analysis and already planned extensions to the model.
    Keywords: microsimulation model, education, forecasting, education projection
    Date: 2014–10
  18. By: G.Levieuge
    Abstract: This paper aims to develop a parsimonious model to explain and forecast bank loans to non-financial companies during calm periods as well as in situations of financial turmoil. In doing so, we are led to gauge the marginal informational content of simple leading indicators, and to investigate potential non-linearity in credit dynamics. This framework is applied to the French context, over a period including financial, banking and sovereign debt crises. In accordance with firms and banks’ balance sheets effects, the growth rate of equity prices appears to be one of the most interesting leading indicator as well as a significant threshold variable for explaining regime switching. However, our results highlight the difficulties to accurately predict the right credit dynamics regimes. A simple VAR model finally performs better.
    Keywords: Credit ; Forecast ; VECM ; Threshold VAR ; leading indicators.
    JEL: E51 E47 C22
    Date: 2015
  19. By: Matusiak, M.; de Koster, M.B.M.; Saarinen, J.
    Abstract: Batching orders and routing order pickers is a commonly studied problem in many picker-to-parts warehouses. The impact of individual differences in picking skills on performance has received little attention. In this paper, we show that taking into account differences in the skills of individual pickers when assigning work has a substantial effect on total batch execution time and picker productivity. We demonstrate this for the case of a Finnish retailer. First, using time-stamped picking data, multilevel modeling is used to forecast batch execution times for individual pickers by modeling individual skills of pickers. Next, these forecasts are used to minimize total batch execution time, by assigning the right picker to the right order batch. We formulate the problem as a joint order batching and generalized assignment model, and solve it with an Adaptive Large Neighborhood Search algorithm. For the sample company, we are able to improve state-of-the-art batching and routing methods by almost 10% taking skill differences among pickers into account and minimizing the sum of total order processing time. Compared to assigning order batches to pickers only based on individual picker productivity, savings of 6% in total time are achieved.
    Keywords: logistics, order picking, analytics, combinatorial optimization, data driven modelling
    Date: 2015–06–29
  20. By: G.Levieuge
    Abstract: The objective of this paper is to assess the relevance and the predictive content of the indicators stemming from the ten-year-old quarterly Bank Lending Survey (BLS) on credit distribution. First, we validate the coherence of the BLS indicators by cross-checking indicators that are supposed to deliver similar information, and by comparing them to a broad set of actual macroeconomic and financial variables. Second, econometric tests reveal that the demand-side indicators of the BLS, but not the supply-side ones, are particularly relevant for explaining and forecasting loans to non-financial companies in France.
    Keywords: Credit ; Forecast ; VECM ; Threshold VAR ; leading indicators.
    JEL: E51 E47 C22
    Date: 2015
  21. By: Kevin Clinton; Charles Freedman; Michel Juillard; Ondra Kamenik; Douglas Laxton; Hou Wang
    Abstract: Many central banks in emerging and advanced economies have adopted an inflation-forecast targeting (IFT) approach to monetary policy, in order to successfully establish a stable, low-inflation environment. To support policy making, each has developed a structured system of forecasting and policy analysis appropriate to its needs. A common component is a model-based forecast with an endogenous policy interest rate path. The approach is characterized, among other things, by transparent communications—some IFT central banks go so far as to publish their policy interest rate projection. Some elements of this regime, although a work still in progress, are worthy of consideration by central banks that have not yet officially adopted full-fledged inflation targeting.
    Keywords: Central banks and their policies;Inflation targeting;Monetary policy;Optimal Control, inflation, interest, interest rate, central bank, General,
    Date: 2015–06–24
  22. By: Tommaso Ciarli (SPRU, University of Sussex, UK); Alex Coad (JRC-IPTS, European Commission, Seville, SP); Ismael Rafols (Ingenio (CSIC-UPV), Universitat Politècnica València, València, SP; SPRU, University of Sussex, UK)
    Abstract: A variety of quantitative techniques have been used in the past in Future-Oriented Technology Analysis (FTA). In recent years, increased computational power and algorithms, web-based searching, and data availability have led to the emergence of new techniques that are potentially useful for foresight and forecasting. As a result, there is now a wide palette of techniques that might be used in FTA exercises. However, it is often unclear how they differ, when the use of a techniques is appropriate, what type of insights it may yield, and how they can be combined. This article reviews and qualifies quantitative methods for FTA in order to help users to make choices among alternative techniques, including new techniques that have not been integrated yet in the FTA literature and practice. We first provide a working definition of Future- Oriented Technology Analysis (FTA) and discuss its role, uses, and popularity over recent decades. Second, we select 22 FTA techniques identified as the most important quantitative FTA techniques and then we review these techniques, discuss their main contexts and uses, and classify them into groups with common characteristics, positioning them along four key dimensions: descriptive/prescriptive; extrapolative/normative; data gathering/inference; and forecasting/foresight.
    Keywords: Future-oriented Technology Analysis (FTA); Quantitative Techniques; Foresight; Forecasting
    JEL: O3
    Date: 2015–08
  23. By: Zhi-Qiang Jiang (ECUST, BU); Askery A. Canabarro (UFAL, BU); Boris Podobnik (UR); H. Eugene Stanley (BU); Wei-Xing Zhou (ECUST)
    Abstract: Being able to forcast extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between volatilities exceeding a certain threshold $Q$ for a fixed expected recurrence time $\tau_Q$. We find that the recurrence intervals are well approximated by the $q$-exponential distribution for all stocks and all $\tau_Q$ values. Thus a analytical formula for determining the hazard probability $W(\Delta t |t)$ that a volatility above $Q$ will occur within a short interval $\Delta t$ if the last volatility exceeding $Q$ happened $t$ periods ago can be directly derived from the $q$-exponential distribution, which is found to be in good agreement with the empirical hazard probability from real stock data. Using these results, we adopt a decision-making algorithm for triggering the alarm of the occurrence of the next volatility above $Q$ based on the hazard probability. Using a "receiver operator characteristic" (ROC) analysis, we find that this predicting method efficiently forecasts the occurrance of large volatility events in real stock data. Our analysis may help us better understand reoccurring large volatilities and more accurately quantify financial risks in stock markets.
    Date: 2015–08

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