Out Of Sample Forecasts Research Articles

Media sentiment emotions and consumer energy prices

This paper analyzes how sentiment from different news sources affect[s] energy prices for consumers. We assess the impact of sentiments derived from newspaper and social media on prices of gasoline, heating oil and natural gas from 2006 until 2021. Having shown that sentiments derived from social media and newspapers differ significantly in the first step, we show that newspaper sentiments have significant effects on prices in times of high news coverage while the effects of social media news are negligible. Finally, we illustrate that strong emotions in newspaper coverage have additional price effects and analyze a potential factor structure for out-of-sample forecasts.

An adaptive long memory conditional correlation model

We propose a conditional correlation model with long memory dependence and smooth structural change. Previous literature has considered correlation and covariance models with structural change or long memory, but this is the first paper to jointly model both features. The correlation matrix is decomposed into long and short run components. Short run correlations converge hypergeometrically towards a slow moving long run correlation matrix that evolves according to one or more flexible Fourier forms. The model is applied to two data sets: a US equity portfolio; and a US equity, bond, gold and oil portfolio. Model fit and out of sample forecasts over 1 to 60 day horizons support the proposed approach.

Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey

Today, data accumulated during internet use have become an important source of information for people’s behaviour, issues, and needs, and due to real-time data acquisition, Google search data have become a focal point for researchers. As a result, it has been become more common to use GT data, which have been included in forecasting models for many economic indicators, including unemployment rate forecasting. Therefore, this study aims to determine whether including Google search data in forecasting models can improve the model’s performance in forecasting the unemployment rate in Turkey. In this context, out-ofsample forecasting was performed in this study using seasonally adjusted monthly unemployment rates for the period between January 2005 and August 2020 and monthly GT data about the topic of unemployment insurance. In addition, the forecasting performance of ARIMA and ARIMAX methods were compared.

The SARIMA model-based monthly rainfall forecasting for the Turksvygbult Station at the Magoebaskloof Dam in South Africa

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann–Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3) 12 model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3) 12, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.

Exchange Rate Forecasting with Advanced Machine Learning Methods

Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 to 2014 for ten currency pairs of OECD countries are used to make out-of sample forecasts with artificial neural networks and XGBoost models. Most approaches show significant and substantial predictive power in directional forecasts. Moreover, the evidence suggests that information regarding prediction timing is a key component in the forecasting performance.

Determinants of loan survival rates for small and medium‐sized enterprises: Evidence from an emerging economy

AbstractThis article investigates the determinants of bank loan survival time in an emerging economy for ‘micro’, ‘small’ and ‘medium’‐sized enterprises. We use proprietary data from the Central Bank of Mexico for approximately 1.5 million company loans between January 2010 and May 2017. Our results show that firm' characteristics at the time of loan origination, and lagged macroeconomic factors have a significant impact on the loan survival times. Further tests on model validation show that our fitted survival model performs reasonably well in out of sample forecasts.

Forecasting of Cotton Production in India using Advanced Time Series Models

Reliable and timely estimates of cotton production are important providing useful inputs to policymakers for proper foresighted and informed planning. So an attempt was made to forecast the production of cotton at all India level using a time series model. The annual data on production of cotton for the period 1951-52 to 2018-19 was processed. The data were transformed into logarithmic series to stabilize the variance of the series. The stationarity of the data was checked with the help of the Augmented Dickey-Fuller and Phillips-Perron tests. The results of ADF and PP tests confirmed the cotton production series was non-stationary at level, so stationarity in the data was brought by differencing the data series at a first lag. The pattern present in ACF and PACF and results of SCAN and ESACF provided guideline to select the order of non-seasonal ARIMA model. The best fit ARIMA model (ARIMA: 3 1 1) was selected based on AIC criteria and residual diagnostic. The performance of the model was judged based on the MAPE value. The out of sample forecast of cotton production at all India level was carried out for the period 2019-20 to 2021-22. The forecasted values indicated a slight increase in the production of cotton compared to 2018-19.

Maturity Structure of Banking Transactions and Its Role in Predicting Negative Net Worth of Banks

In this paper, we perform a microeconomic analysis of positive and negative imbalances in the maturity structure of Russian banks’ transactions. In particular, using Heckman selection models at the cross-section of Russian banks, we test the ability of such imbalances to predict the probability of the detection of banks’ negative net worth and its expected magnitude in advance (three months before negative worth detection). The estimation results show that, first, certain indicators of imbalances do offer ‘value added’ in predicting ‘holes’ in banks’ capital: taking into account these imbalances in banks’ short- and medium-term transactions with households and short-term transactions with enterprises improves the quality of out-ofsample forecasts. Second, the very division into positive and negative imbalances makes sense: the effects are in many cases found to be opposite with respect to the size and likelihood of negative net worth detection at banks. Third, a separate analysis of banking transactions with households and those with businesses is also of great importance: the effect of imbalances in transactions similar in maturity structure but with different types of economic agents is in many cases opposite in sign. The results may be useful for the Bank of Russia in identifying potentially fragile banks as part of its prudential policy.

Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models

This study is an attempt to examine empirically the best ARIMA model for forecasting. The monthly time series data routinely-collected at Orthopedic clinic from January 2013 until June 2018 have been used for this purpose. At first the stationarity condition of the data series is observed by ACF and PACF plots, then checked using the Ljung-Box-Pierce Q-statistic. It has been found that the monthly time series data of the Orthopedic clinic are stationary. The best ARIMA model has been selected by using the MAPE. To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of MAPE are smallest is considered as the best model. Hence, ARIMA (1, 0, 0) is found as the best model for forecasting the Orthopedic clinic data series. The out of sample forecast by using ARIMA (1, 0, 0) model indicated a fluctuation of monthly orthopedic patients demand, from lowest was 294 and the highest was 299 patients that could receive treatment from the clinic in a month.

Greek milk price decomposition

The aim of this paper is to analyse the Greek cow milk farm gate price, compare it with German and Irish prices and make an out of sample forecast for the year 2017. For this purpose, the milk price time series are modelled using the state space methods and they are decomposed into a trend, a seasonal and a cyclical component. The results show that there is an increase in the price fluctuations. The Greek milk price becomes more seasonal and cyclical while the duration of the cycles is relatively stable. Finally, conclusions are drawn about the connection of the cycles of the milk price time series and the milk production and quotas.

Forecasting Realized Stock-Market Volatility: Do Industry Returns Have Predictive Value?

Yes, they do. Utilizing a machine-learning technique known as random forests to compute forecasts of realized (good and bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not constant over time, industrials and materials play a dominant predictive role during the aftermath of the 2008 global financial crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show that incorporating lagged industry returns in aggregate level volatility forecasts benefits forecasters who are particularly concerned about under-predicting market volatility, yielding greater economic benefits for forecasters as the degree of risk aversion increases.

Application of Machine Learning to Mortality Modeling and Forecasting

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.

Tail Risk in a Retail Payments System

AbstractIn this paper, we study a credit risk (collateral) management scheme for the Canadian retail payment system designed to cover the exposure of a defaulting member. We estimate ex ante the size of a collateral pool large enough to cover exposure for a historical worst-case default scenario. The parameters of the distribution of the maxima are estimated using two main statistical approaches based on extreme value models: Block-Maxima for different window lengths (daily, weekly and monthly) and Peak-over-Threshold. Our statistical model implies that the largest daily net debit position across participants exceeds roughly $1.5 billion once a year. Despite relying on extreme-value theory, the out of sample forecasts may still underestimate an actual exposure given the absence of observed data on defaults and financial stress in Canada. Our results are informative for optimal collateral management and system design of pre-funded retail-payment schemes.

Is Idiosyncratic Volatility Related to Returns? Evidence from a Subset of Firms with Quality Idiosyncratic Volatility Estimates

Empirical studies investigating the relation between expected idiosyncratic volatility (IVOL) and returns find mixed results. Several papers report a negative relation using lagged realized IVOL to proxy for expectations, but Fu (2009) questions the validity of this proxy and proposes using forecasts from EGARCH models, resulting in a positive relation. However, recent studies show that this positive relation disappears when the forecasts are generated by out-of-sample models. We show that the proxies used in prior literature are noisy and propose using combinations of outof-sample IVOL forecasts that pass basic diagnostic tests. These high quality proxies uncover a significant positive relation.

Electricity consumption and economic growth: empirical evidence from a resource-rich landlocked economy

This paper unfolds the short run and the long run causality between per capita electric power consumption (LEPC) and per capita gross domestic product (LGDP) for Nepal during the 1971–2010. The conventional Augmented Dickey Fuller (ADF) unit root tests indicate that the series are integrated of order independently and Johansen cointegration test confirms the presence of one cointegration among the variables. A vector error correction model (VECM) is then employed. It is found that LGDP Granger causes LEPC in the long run and weakly Granger causes in the short run. While reverse causality is found not to be true. Furthermore, impulse responses are included, which estimate how each variable behave upon the policy shock and variance decompositions segregate the portion of each variable on total variation. Finally, with a tight 67% confidence interval, in sample forecast and out of sample forecasts were made from 2011 till 2020. The results indicate that total electricity consumption has no causal role as a component of economic growth in Nepal. Thus, the electricity consumption policy should be designed and implemented as a cohesion to growth but not as cohesive to growth.

Anticipating electricity prices for future needs – Implications for liberalised retail markets

Electricity price forecasting is a mature research area, with various techniques already developed in recent years to help both generators and retailers hedge against price and load associated risks. This paper aims to add on to the forecasting literature, with emphasis on the importance of making such forecasts transparent to facilitate countries' transitions towards more liberalised retail electricity markets.We conduct univariate forecasting of Singapore's weekly wholesale electricity prices with the Autoregressive Integrated Moving Average (ARIMA) models, complimented with the use of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models and their variants to account for volatility. Results show that our models reasonably emulate the price trends based on out of sample forecasts. The magnitude of expected future weekly price spikes may be estimated to a reasonable extent based on historical price outages, determined exogenously in the model. These forecasts can thus serve as possible references to retail players in a competitive market for all parties to make more informed decisions before participating in the open market. This is especially important for smaller consumers of electricity who are typically last to be exposed to retail choices. Adequate knowledge of prices will be necessary to increase desired switching rates.

Forecasting short-term carbon emission futures price volatility: information for hedging carbon emission futures risk

This paper aimed to illustrate how short-term carbon futures speculators might use short-term carbon emission futures data to predict and forecast carbon prices. The paper became apposite given ubiquitous research focussing on long-term carbon futures data, which has left out short-term carbon emission futures speculators with information. Therefore, this paper demonstrated that short-term speculators in carbon futures could indeed use short-term time series data on carbon futures to make a reliable prediction and forecasting of carbon emissions futures price volatility within a short term and thus decide on investment opportunity. The sample data results showed that short-term data could produce a dependable in-sample futures prediction since the in-sample prediction fell within the 95% confidence interval. The demonstration also showed that short-term carbon futures data could assist speculators to conduct a reliable short-term out of sample forecast of carbon futures prices within the closer period. The paper offers practical assistance to carbon futures speculators and is equally important for academic studies for business and economic students on discussions and research bordering on carbon emissions, carbon trading, environmental economics and sustainable development. More carbon short-term forecasting is encouraged – such research should compare short-term forecasting of carbon futures amongst different carbon markets.

Exchange Rates and Monetary Fundamentals: Evidence from Turkey

(ProQuest: ... denotes formulae omitted.)1. IntroductionExchange rates, which is simply the price of one currency in terms of another currency, are one of the key component of the macroeconomics discussions in the literature. If one want to understand why some currencies depreciate and others appreciate, the factors that cause the change in supply and demand of currencies must be investigated. These factors include various fundamentals such as real income, inflation rates, real interest rates, consumer preferences, and government trade policy. For example, Purchasing Power Parity (PPP) states that a country's currency will depreciate by an amount equal to the excess of domestic inflation over foreign inflation while Uncovered Interest Parity (UIP) suggests that the interest rate differentials change the exchange rates. Traditional Flow Approach, which is also called the balance-ofpayments view, states that exchange rate moves to keep the balance of payments in equilibrium under the assumption of no government interventions. On the other hand, nominal exchange rate fluctuations should reflect movements in a country's monetary fundamentals such as relative money, output, interest rates and prices, according to the Monetary Model of Exchange Rate Determination.One of the important debates centers on the international economics is the difficulty of predicting exchange rates by using monetary fundamentals such as money supplies, outputs, and interest rates. As stated above, some theories in the economic literature claim that the exchange rates might be determined by such fundamental variables. However, since Meese and Rogoff (1983), it has been considered that exchange rates are very difficult to predict using monetary fundamentals; in particular, a simple random walk without drift (RW), is found to produce better exchange rate forecasts than fundamental-based exchange rate models. In other words, fundamental variables do not help predict future changes in exchange ratesMeese and Rogoff's (1983) finding was a shock for economists since monetary fundamentals have long been considered key determinants of exchange rates. If their finding is true, then all exchange rate models based on the fundamentals are misleading. Therefore, large numbers of studies have attempted to refute Meese and Rogoff's findings and find positive results in favor of fundamentals-based models. One of the most well-known rebuttals to Meese and Rogoff's work is Mark's (1995) study. Mark concludes in his study that monetary fundamentals contain predictive power for exchange rate. He used the long horizon regression (or Error Correction Model (ECM)) derived from the VECM (Vector Error Correction Model). However, these two studies are not the only ones testing the predictive ability of exchange rate models. Several models such as the single equation linear models, single-equation ECM, multi-equation VECM models, time-varying parameter models, and panel models have been used in the literature in the attempt to predict exchange rates. Even though most of the authors are unable to document short-run exchange rate predictability, some of them find evidence of long horizon predictability. Nevertheless, the literature has remained pessimistic about the link between exchange rates and fundamentals.The main purpose of the study is to investigate the exchange rate predictability in Turkey by using the monetary fundamentals in the ECM. This study first examines the in-sample fit of the ECM model, and then evaluates the out-of sample forecast performance of the ECM against benchmark RW model. The fundamental value of exchange rate used in this study is obtained from Frankel-Bilson Monetary Model. The error correction term is constructed under the assumptions of zero/nonzero interest rate differentials and country specific money demand elasticities to satisfy the cointegration assumption between fundamentals and exchange rates. The in-sample analysis results present evidence that monetary fundamentals are useful to explain the long horizon changes in the logarithm of the exchange rates while the out-of sample analysis results suggest that whether the ECM or the RW explains the nature of exchange rate changes might be considered that time varying. …

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016.

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

On the predictability of model-free implied correlation

This paper investigates the existence of predictable patterns in the evolution of the implied correlation series. To this end, alternative time-series specifications are employed to model the correlation dynamics, and the statistical and economic significance of out-of sample forecasts is assessed. The statistical measures provide strong evidence in favor of predictable patterns in the S&P 100 options market. A trading strategy designed to exploit daily changes in the series can yield abnormal profits; however, these profits disappear when transaction costs are incorporated. We conclude that the efficient market hypothesis cannot be rejected.