Random Walk Forecasts Research Articles

Quantitative easing effectiveness: Evidence from Euro private assets

AbstractProponents of quantitative easing (QE) unconventional policy have rather overstated some evidence that structural time series models do not predict long‐term asset prices and yields as well as naive random walk forecasts, implying that predictions of price reversals cannot be profitable and, therefore, that QE effects are not transitory. Indeed, in this work we present evidence that naive models do not outperform structural vector autoregressive and Markov switching models in out‐of‐sample forecasting of corporate bond yields purchased by the European Central Bank, when the information set includes base money growth. It turns out that structural time series models provide additional information regarding the likelihood of price reversals, thus motivating investors to offset the effects of QE interventions if they perceive unconventional monetary policy regimes as temporary.

Forecast combination puzzle in the HAR model

AbstractThe heterogeneous autoregressive (HAR) model has become the benchmark model for predicting realized volatility, given its simplicity and consistent empirical performance. Many modifications and extensions to the original model have been proposed that often only provide incremental forecast improvements. In this paper, we take a step back and view the HAR model as a forecast combination that combines three predictors: previous day realization (or random walk forecast), previous week average, and previous month average. When applying the ordinary least squares (OLS) to combine the predictors, the HAR model uses optimal weights that are known to be problematic in the forecast combination literature. In fact, an average of simpler HAR‐style models, and a simple average forecast often outperforms the optimal combination in many empirical applications. We investigate the performance of these simple combination forecasts for the realized volatility of the Dow Jones Industrial Average equity index, and a sample of individual constituent stocks, as well as across a range of other assets, commodities, exchange rates, and a range of global equity market indices. In all cases, we find dramatic improvements in forecast accuracy across all horizons and different time periods. The combinations also perform well relative to the logarithmic form of the HAR model and when using a volatility timing strategy in portfolio allocation. Thisis the first time the forecast combination puzzle is identified in this context.

Forecasts of the real price of oil revisited: Do they beat the random walk?

In macroeconomic forecasting, the real price of oil is traditionally computed as the monthly average price of oil deflated by the price index. Consequently, the no-change forecast used to benchmark forecasts of the real price of crude oil is a monthly average price. We demonstrate that an alternative no-change forecast which reflects the random walk forecast from daily oil prices – the end-of-month price – is significantly more accurate in predicting the real price of oil up to one year ahead. We find that at the one-step-ahead prediction, all existing forecasts that outperform the monthly average no-change forecast perform worse than the end-of-month no-change forecast. The results call into question the usefulness of existing forecasting approaches for the real price of crude oil relative to naive forecasts.

Market participants or the random walk – who forecasts better? Evidence from micro-level survey data

We analyse micro-level data concerning four financial variables in Sveriges Riksbank's Prospera Survey to evaluate the precision of forecasts provided by professionals active in the Swedish fixed-income market. Our results indicate that for the SEK/EUR and SEK/USD exchange rates, and the five-year government bond yield, none of the market participants that frequently participate in the survey manage to significantly outperform the random-walk forecast. For the central bank's policy rate, the market participants typically have a statistically significant higher forecast precision than the random-walk forecast at the three-month horizon; however, at the two- and five-year horizons, the random-walk forecast typically outperforms the market participants.

Discussion of “Multivariate dynamic modeling for Bayesian forecasting of business revenue”

Discussion of “Multivariate dynamic modeling for Bayesian forecasting of business revenue”

Analysts versus the random walk in financial forecasting: evidence from the Czech National Bank’s Financial Market Inflation Expectations survey

ABSTRACT We analyse how financial market analysts’ expectations in the Czech National Bank’s Financial Market Inflation Expectations survey perform relative to the random-walk forecast when it comes to predicting five financial variables. Using data from 2001 to 2022, our results indicate that the analysts are able to significantly outperform the random-walk forecast in terms of forecast precision for the repo rate and Prague Interbank Offered Rate at the one-month forecasting horizon. For the five- and ten-year interest rate swap rates, the random walk significantly outperforms the analysts at both the one-month and one-year forecasting horizons. For the CZK/EUR exchange rate, the random-walk forecast has a lower root mean squared forecast error than that of the analysts’ forecast at the one-month horizon whereas at the one-year horizon the opposite is found; however, none of these differences are statistically significant.

A monetary model of exchange rate beats the random walk forecast even at a short horizon: Evidence from the Serbian hyperinflation at daily frequency

This paper finds that an out-of-sample forecast of a monetary model of exchange rate (MMER) in hyperinflation decisively beats a random walk one particularly at the most challenging one step ahead forecast, thus outperforming standard results previously obtained for low inflation episodes. The findings refer to the Serbian hyperinflation at daily frequency, and are robust with respect to various tests. Fast adjustment of the exchange rate to its fundamental value and the low, well below one discount factor found in the Serbian episode, as opposed to low-inflation ones, can account for divergent results in the respective inflation environments. The low discount factor appears in other hyperinflation episodes, while fast adjustment is due to the absence of nominal rigidities in hyperinflation thus both suggesting that reported findings for one episode might generalize.

Random Walk Forecasts of Stationary Processes Have Low Bias

Random Walk Forecasts of Stationary Processes Have Low Bias

Searching for the Best Inflation Forecasters within an Employment Survey: Microdata Evidence from Chile

This article aims to evaluate quantitative inflation forecasts for the Chilean economy, taking advantage of a specific survey of consumer perceptions at the individual microdata level, which, at the same time, is linked to a survey of employment in Chile’s capital city. Thus, it is possible to link, with no error, consumer perceptions and 12-month-ahead inflation forecasts with personal characteristics such as gender, age, educational level, county of living, and the economic sector in which they are currently working. By using a sample ranging from 2005.III to 2018.IV, the results suggest that women aged between 35 and 65 years old, with a college degree, living in the North-eastern part of Santiago (the richest of the city), and working in the Community and Social Services sector are the best forecasters. Men aged between 35 and 65 years old, with a college degree, in a tie living in the North-eastern and South-eastern zones but working in Government and Financial Services and Retail sectors, respectively, come in second. Some econometric exercises reinforce and give greater support to the group of most accurate forecasters and reveal that another group of forecasters, different from the second-best in terms of forecast accuracy, displays the characteristics required of a forecasting variable. Remarkably, this group has the same specifications as the most accurate group, with the only difference being that it is composed of men instead of women. Thus, it looks promising for further consideration. Importantly, a forecast accuracy test reveals that no factor comes out as superior to the naïve random walk forecast used as a benchmark. These results are important because they help to identify the most accurate group when forecasting inflation and, thus, help refine the information provided by the survey for inflation forecasting purposes.

An evaluation of quantitative easing effectiveness based on out-of-sample forecasts

<abstract> <p>A line of defense of quantitative easing (QE) policies has been developed around empirical evidence that time series models do not predict long-term asset prices and yields as well as naive random walk forecasts, implying that predictions of price reversals cannot be profitable and, therefore, that QE effects are not reversed. However, in this work we present evidence that for the Eurozone, Sweden, and the UK, which have pursued QE interventions, a random walk does not beat a Markov switching regimes model in out-of-sample forecasting and, at the same time, the switching process provides additional information regarding the likelihood of price reversals, thus inducing market participants to offset the effects of QE interventions whenever they perceive unconventional monetary policy regimes as temporary.</p> </abstract>

Wheat Futures Prices Prediction in China: A Hybrid Approach

Stocks markets play their financial roles of price shocks and hedging just when they are proficient. The imperative highlights of productive market are that one cannot make extraordinary profit from the stocks markets. This research investigates whether China wheat futures price can be predicted by employing artificial intelligence neural network. This would add to our knowledge whether wheat futures market is resourceful and would enable traders, sellers, and investors to improve cost-effective trading strategy. We utilize the traditional financial model to forecast the wheat futures price and acquire out of sample point estimates. We additionally assess the robustness of our outcomes by applying several alternative forecasting techniques such as artificial intelligence with one hidden layer and autoregressive integrated moving average (ARIMA) model. Furthermore, the statistical significance of our point estimation was further tested through the Mariano and Diebold test. Considering random walk forecast as the bench mark, we used a number of economic indicators, trader’s expectation towards futures prices, and lagged value of futures price of wheat in order to forecast the evaluation of wheat futures price. The computable significance of out of sample estimations recommends that our ANN with one hidden layer has the best anticipating presentation among all the models considered in this exploration and has the estimating power in foreseeing wheat futures returns. Furthermore, this investigation discovers that the futures price of wheat can be predicted, and the wheat futures market of China is not productive.

Machine learning and oil price point and density forecasting

The purpose of this paper is to explore machine learning techniques to forecast the oil price. In the era of big data, we investigate whether new automated tools can improve over traditional approaches in terms of forecast accuracy. Oil price point and density forecasts are built from 23 methods, including regression trees (random forest, quantile regression forest, xgboost), regularization procedures (elastic net, lasso, ridge), standard econometric models and forecast combinations, besides the structural factor model of Schwartz and Smith (2000). The database contains 315 macroeconomic and financial variables, used to build high-dimensional models. To evaluate the predictive power of each method, an extensive pseudo out-of-sample forecasting exercise is built, in monthly and quarterly frequencies, with horizons from one month up to five years. Overall, the results indicate a good performance of the machine learning methods in the short-run. Up to six months, lasso-based models, oil future prices, VECM and the Schwartz–Smith model provide the best forecasts. At longer horizons, forecast combinations also become relevant. In several cases, the accuracy gains in respect to the random walk forecast are statistically significant and reach two-digit figures, in percentage terms, using the R2 out-of-sample statistic; an expressive achievement compared to the previous literature.

Testing the Forecasting Ability of Multi-Factor Models on Non-US Interbank Rates

This article examines the forecasting performance of continuous-time multi-factor models, in comparison with other parsimonious models, for the term structure of interbank rates in the UK, Europe, and Japan. The article employs two general dynamic frameworks with different factor structures: the generalized Chan-Karolyi-Longstaff-Sanders family of models and the arbitrage-free dynamic Nelson-Siegel family of models. Applying a battery of accuracy measures and a range of formal tests of forecasting superiority, this research provides evidence that extended multi-factor models demonstrate good out-of-sample forecasting performance for the short segment of the yield curve. However, for the euro and in part for the yen, random walk forecasts consistently pass various tests, indicating a higher level of market efficiency compared to the pound sterling interbank market. TOPICS:Factor-based models, currency, developed markets, performance measurement Key Findings ▪ For the term structure of interbank rates in the UK, Europe, and Japan, more complex continuous-time models that include more factors are superior in terms of predictive power to models with less factors or discrete-time models. ▪ Based on a battery of accuracy tests and a range of formal tests, extended multi-factor models demonstrate good out-of-sample forecasting performance for the short segment of the yield curve. ▪ For the euro and in part for the yen, random walk forecasts consistently pass various tests, indicating a higher level of market efficiency compared to the pound sterling interbank market.

Gasoline price predictability: insights from consumer vehicle‐buying assessments

AbstractThis study employs the random walk forecasts of US retail gasoline prices for 2001–2020 in order to investigate the potential usefulness of consumer vehicle‐buying assessments for predicting gasoline prices. We show that the random walk forecasts are free of systematic bias and imply symmetric loss, but they do not fully contain the information on consumer vehicle‐buying assessments. For instance, the random walk forecast errors fail to be orthogonal to the difference of opinion of consumers about whether the next 12 months or so will be a good time or a bad time to buy a new vehicle, and to the difference of opinion of consumers about vehicle prices (but not interest rates). In addition, the random walk forecast errors fail to be orthogonal to the change in consumer opinion about gasoline prices, fuel efficiency and poor selections (quality). Such findings remain unchanged when we use the ‘weakly’ rational forecasts of gasoline prices instead of the random walk benchmark. Put together, our results provide new insights into the potential usefulness of consumer vehicle‐buying assessments for predicting gasoline prices. We recommend forecasters pay special attention to such assessments when forecasting retail gasoline prices, since they can potentially help produce forecasts that are superior to both the random walk and ‘weakly’ rational forecasts.

Forecast combination puzzle in the HAR model

The Heterogeneous Autoregressive (HAR) model of Corsi (2009) has become the benchmark model for predicting realized volatility given its simplicity and consistent empirical performance. Many modifications and extensions to the original model have been proposed that often only provide incremental forecast improvements. In this paper, we take a step back and view the HAR model as a forecast combination that combines three predictors: previous day realization (or random walk forecast), previous week average, and previous month average. When apply- ing the Ordinary Least Squares (OLS) to combine the predictors, the HAR model uses optimal weights that are known to be problematic in the forecast combination literature. In fact, the simple average forecast often outperforms the optimal combination in many empirical applications. We investigate the performance of the simple average forecast for the realized volatility of the Dow Jones Industrial Average equity index. We find dramatic improvements in forecast accuracy across all horizons and different time periods. This is the first time the forecast combination puzzle is identified in this context.

Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States.

Goal: The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US’ diversity, geographic spread, and economic inequality, the COVID-19 pandemic in the US acts more as a series of diverse regional outbreaks rather than a synchronized homogeneous one. Method: In order to determine how to assess regional risk related to COVID-19, a two-phase modeling approach is developed while considering demographic and economic criteria. First, an unsupervised clustering technique, specifically n}{}k-means, is employed to group US counties based on demographic and economic similarities. Then, time series forecasting of each cluster of counties is developed to assess the short-run viral transmissibility risk. Results: To this end, we test ARIMA and Seasonal Trend Random Walk forecasts to determine which is more appropriate for modeling the spread and lethality of COVID-19. From our analysis, we then utilize the superior ARIMA models to forecast future COVID-19 trends in the clusters, and present the areas in the US which have the highest COVID-19 related risk heading into the winter of 2020. Conclusion: Including sub-national socioeconomic characteristics to data-driven COVID-19 infection and fatality forecasts may play a key role in assessing the risk associated with changes in infection patterns at the national level.

Oil prices and experts’ forecasts of seven exchange rates

ABSTRACT Research indicates that survey-based forecasts of exchange rates fail to be rational. In addition, the literature points to oil prices as a key factor in determining/predicting exchange rates. We investigate these two research topics by focusing on Blue Chip multi-month forecasts of seven exchange rates for 1999–2015. Our investigation utilizes the recent forecast evaluation method that allows for the possibility of asymmetric loss. We show that Blue Chip forecasts, while free of systematic bias, cannot beat the random walk benchmark, and, at some forecast horizons, the errors fail to be orthogonal to changes in oil prices. We further show that, for four out of seven exchange rates, the change in oil prices explains the information content difference between the Blue Chip and random walk forecasts. Put together, our findings suggest that the inaccuracy of Blue Chip forecasts may be in part due to an inefficient use of oil price information available at the time of the forecast and, thus, forecasters should pay special attention to oil price movements when forecasting exchange rates.

On the predictability of crude oil market: A hybrid multiscale wavelet approach

AbstractPast research indicates that forecasting is important in understanding price dynamics across assets. We explore the potentiality of multiscale forecasting in the crude oil market by employing a wavelet multiscale analysis on returns and volatilities of Brent and West Texas Intermediate crude oil indices between January 1, 2001, and May 1, 2015. The analysis is based on a shift‐invariant discrete wavelet transform, augmented by an entropy‐based methodology for determining the optimal timescale decomposition under different market regimes. The empirical results show that the five‐step‐ahead wavelet forecast that is based on volatilities outperforms the random walk forecast, relative to the wavelet forecast that is based on returns. Optimal wavelet causality forecasting for returns is suggested across all frequencies (i.e., daily–yearly), whereas for volatilities it is suggested only up to quarterly frequencies. These results may have important implications for market efficiency and predictability of prices on the crude oil markets.

The effects of conventional and unconventional monetary policy on forecasting the yield curve

We investigate how conventional and unconventional monetary policies affect the dynamics of the yield curve by assessing the performance of individual yield curve models and their mixtures. Out-of-sample forecasts for U.S. bond yields show that the arbitrage-free Nelson–Siegel model and its mixtures with other models perform well in the period of conventional monetary policy, whereas the random walk model outperforms all the other models in the period of unconventional monetary policy. The diminished role of the no-arbitrage restriction in forecasting the yield curve since 2009 can be attributed to unconventional monetary policy, which resulted in low correlations between short- and long-term bond yields and little variation in the short-term rates. During the period of the maturity extension program in 2011–2012, the superiority of the random walk forecasts is more pronounced, reinforcing our finding that the monetary policy framework affects yield curve forecast accuracy.

Forecasting bulk prices of Bordeaux wines using leading indicators

Agricultural price forecasting has been being abandoned progressively by researchers ever since the development of large-scale agricultural futures markets. However, as with many other agricultural goods, there is no futures market for wine. This paper draws on the agricultural prices forecasting literature to develop a forecasting model for bulk wine prices. The price data include annual and monthly series for various wine types that are produced in the Bordeaux region. The predictors include several leading economic indicators of supply and demand shifts. The stock levels and quantities produced are found to have the highest predictive power. The preferred annual and monthly forecasting models outperform naive random walk forecasts by 27.1% and 3.4% respectively; their mean absolute percentage errors are 2.7% and 3.4% respectively. A simple trading strategy based on monthly forecasts is estimated to increase profits by 3.3% relative to a blind strategy that consists of always selling at the spot price.