scholarly journals Better the Devil You Know: Improved Forecasts from Imperfect Models

2021 ◽  
Vol 2021 (070) ◽  
pp. 1-45
Author(s):  
Dong Hwan Oh ◽  
◽  
Andrew J. Patton ◽  
Keyword(s):  
Risk Management ◽  
Yield Curve ◽  
Forecasting Model ◽  
Volatility Forecasting ◽  
Standard Methods ◽  
State Variable ◽  
Economic Decisions ◽  
Out Of Sample ◽  
M Estimation ◽  
The Devil

Many important economic decisions are based on a parametric forecasting model that is known to be good but imperfect. We propose methods to improve out-of-sample forecasts from a mis-specified model by estimating its parameters using a form of local M estimation (thereby nesting local OLS and local MLE), drawing on information from a state variable that is correlated with the misspecification of the model. We theoretically consider the forecast environments in which our approach is likely to o¤er improvements over standard methods, and we find significant fore- cast improvements from applying the proposed method across distinct empirical analyses including volatility forecasting, risk management, and yield curve forecasting.

Coastal risk management: How to motivate individual economic decisions to lower flood risk?

2011 ◽  
Vol 54 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Tatiana Filatova ◽  
Jan P.M. Mulder ◽  
Anne van der Veen
Keyword(s):  
Risk Management ◽  
Flood Risk ◽  
Economic Decisions ◽  
Coastal Risk

scholarly journals Forecasting Wheat Production in Pakistan

2008 ◽  
Vol 13 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Falak Sher ◽  
Eatzaz Ahmad
Keyword(s):  
Production Function ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Forecasting Model ◽  
Future Prospects ◽  
Wheat Production ◽  
Steady Growth ◽  
Forecasting Performance ◽  
The Future ◽  
Out Of Sample

This study analyzes the future prospects of wheat production in Pakistan. Parameters of the forecasting model are obtained by estimating a Cobb-Douglas production function for wheat, while future values of various inputs are obtained as dynamic forecasts on the basis of separate ARIMA estimates for each input and for each province. Input forecasts and parameters of the wheat production function are then used to generate wheat forecasts. The results of the study show that the most important variables for predicting wheat production per hectare (in order of importance) are: lagged output, labor force, use of tractors, and sum of the rainfall in the months of November to March. The null hypotheses of common coefficients across provinces for most of the variables cannot be rejected, implying that all variables play the same role in wheat production in all the four provinces. Forecasting performance of the model based on out-of-sample forecasts for the period 2005-06 is highly satisfactory with 1.81% mean absolute error. The future forecasts for the period of 2007-15 show steady growth of 1.6%, indicating that Pakistan will face a slight shortage of wheat output in the future.

Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index

2020 ◽  
Vol 24 (2) ◽  
pp. 217-227
Author(s):  
Kelvin Mutum
Keyword(s):  
Decision Making ◽  
Investor Sentiment ◽  
Trading Strategies ◽  
Percentage Error ◽  
Forecasting Model ◽  
Decision Making Process ◽  
Options Market ◽  
Volatility Forecasting ◽  
Behavioural Finance ◽  
Options Trading

The present study was to examine whether the performance of options trading strategies can be improved if volatility forecasting incorporating investors’ sentiment was incorporated in the decision-making process at the Indian options market. The study adopted the multiple-factor model to build the Indian volatility forecasting model. The benchmark forecasting model (BMF) includes absolute daily returns (|RA|), daily high–low range (HLR) and daily realized volatility (RV). The proxies of investors’ sentiment considered in the study were India volatility index (IVIX), advance decline ratio (ADR), put-call open interest (PCOI) and their changes. The results of the causality and regression test indicate that investors’ sentiment and their changes should be included in the forecasting model. Mean absolute percentage error (MAPE) indicates that 15-day holding period shows the minimum error. Straddle strategies were simulated 15 days ahead before the options maturity date base on the direction of the forecast for different volatility forecasting models. The simulation result shows that the options trading performance might be improved if volatility forecasting incorporating investor sentiment, particularly IVIX, was incorporated in the decision-making process at the Indian options market. From the behavioural finance point of view, the study bridges the gap between options trading, volatility forecasting and information content of investors’ sentiment at the Indian financial market.

scholarly journals A Bayesian forecasting model of international conflict

2019 ◽  
Vol 17 (3) ◽  
pp. 235-242
Author(s):  
George W Williford ◽  
Douglas B Atkinson
Keyword(s):  
International Relations ◽  
Bayesian Methods ◽  
International Conflict ◽  
Rare Events ◽  
Forecasting Model ◽  
Bayesian Forecasting ◽  
Strong Interest ◽  
Out Of Sample ◽  
Cross National

Scholars and practitioners in international relations have a strong interest in forecasting international conflict. However, due to the complexity of forecasting rare events, existing attempts to predict the onset of international conflict in a cross-national setting have generally had low rates of success. In this paper, we apply Bayesian methods to develop a forecasting model designed to predict the onset of international conflict at the yearly level. We find that this model performs substantially better at producing accurate predictions both in and out of sample.

scholarly journals Using the Entire Yield Curve in Forecasting Output and Inflation

Econometrics ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 40
Author(s):  
Eric Hillebrand ◽  
Huiyu Huang ◽  
Tae-Hwy Lee ◽  
Canlin Li
Keyword(s):  
Monte Carlo Simulation ◽  
Yield Curve ◽  
Factor Model ◽  
Factor Models ◽  
Output Growth ◽  
Numerical Examples ◽  
Combining Forecasts ◽  
Out Of Sample ◽  
The Many ◽  
Combining Information

In forecasting a variable (forecast target) using many predictors, a factor model with principal components (PC) is often used. When the predictors are the yield curve (a set of many yields), the Nelson–Siegel (NS) factor model is used in place of the PC factors. These PC or NS factors are combining information (CI) in the predictors (yields). However, these CI factors are not “supervised” for a specific forecast target in that they are constructed by using only the predictors but not using a particular forecast target. In order to “supervise” factors for a forecast target, we follow Chan et al. (1999) and Stock and Watson (2004) to compute PC or NS factors of many forecasts (not of the predictors), with each of the many forecasts being computed using one predictor at a time. These PC or NS factors of forecasts are combining forecasts (CF). The CF factors are supervised for a specific forecast target. We demonstrate the advantage of the supervised CF factor models over the unsupervised CI factor models via simple numerical examples and Monte Carlo simulation. In out-of-sample forecasting of monthly US output growth and inflation, it is found that the CF factor models outperform the CI factor models especially at longer forecast horizons.

A credibility-based yield forecasting model for crop reinsurance pricing and weather risk management

2019 ◽  
Vol 79 (1) ◽  
pp. 2-26 ◽  
Author(s):  
Wenjun Zhu ◽  
Lysa Porth ◽  
Ken Seng Tan
Keyword(s):  
Risk Management ◽  
Closed Form ◽  
Crop Yield ◽  
Forecasting Model ◽  
Content Type ◽  
Credibility Estimator ◽  
Weather Risk ◽  
Yield Forecasting ◽  
Crop Yield Forecasting ◽  
Weather Risk Management

Purpose The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated. Design/methodology/approach The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection. Findings The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities. Research limitations/implications The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results. Practical implications This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events. Originality/value This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.

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