Volatility Forecasting Performance of Smooth Transition Exponential Smoothing Method: Evidence from Mutual Fund Indices in Malaysia

This paper aims to empirically compare the performance of the smooth transition exponential smoothing (STES) method against the well-known generalized autoregressive conditional heteroskedasticity (GARCH) model in one-step-ahead volatility forecasting. While the GARCH model captured most of the stylized facts of the financial time series, threats of outliers in the leptokurtic distributed series remain unresolved. The study compared volatility forecasting performance of a total of 22 models and methods comprising STES, GARCH, and some ad-hoc forecasting. The daily returns of seven mutual fund indices (derived from 57 individual equity mutual funds) under two different economic conditions (sub-periods) were applied across all competing models. Findings revealed that the STES method with error and absolute error as transition variables emerged as the best post-sample volatility forecasting model in both sub-periods with and without financial crisis impact, as verified by model confidence set (MCS) procedure. The implications based on the results are: (1) both the sign and size of yesterday’s news shock have an impact on today’s volatility; (2) the STES method is resilient to outliers, and hence superior to GARCH and other volatility forecasting approaches examined. This study contributes an empirical approach in forecasting the risk of mutual funds investment for investors and fund managers, as well as extending the scope of volatility forecasting literature into the less explored mutual funds.

Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model

Previous studies have found that geopolitical risk (GPR) caused by geopolitical events such as terrorist attacks can affect the movements of asset prices. However, the studies on whether and how these influences can explain and predict the volatility of stock returns in emerging markets are scant and emerging. By using the data from China’s CSI 300 index, we provide some evidence on whether and how the GPR factors can explain and forecast the volatility of stock returns in emerging economies. We employed the GARCH-MIDAS model and the model confidence set (MCS) to investigate the mechanism of GPR’s impact on the China stock market, and we considered the GPR index, geopolitical action index, geopolitical threat index, and different country-specific GPR indices. The empirical results suggest that except for a few emerging economies such as Mexico, Argentina, Russia, India, South Africa, Thailand, Israel, and Ukraine, the global and most of the regional GPR have a significant impact on China’s stock market. This paper provides some evidence for the different effects of GPR from different countries on China’s stock market volatility. As for predictive potential, GPRAct (geopolitical action index) has the best predictive power among all six types of GPR indices. Considering that GPR is usually unanticipated, these findings shed light on the role of the GPR factors in explaining and forecasting the volatility of China’s market returns.

On the Elicitability and Risk Model Comparison of Emerging Markets Equities

The need for comparative backtesting in the Basel III framework presents the challenge for ranking of internal value-at-risk (VaR) and expected shortfall (ES) models. We use a joint loss function to score the elicitable joint VaR and ES models to select competing tail risk models for the top 9 emerging markets equities and the emerging markets composite index. We achieve this with the model confidence set (MCS) procedure. Our analysis span two sub-sample periods representing turbulent (Eurozone and Global Financial crises periods) and tranquil (post-Global Financial crisis period) market conditions. We find that many of the markets risk models are time-invariant and independent of market conditions. But for China and South Africa this is not true because their risk models are time-varying, market conditions-dependent, percentile-dependent and heterogeneous. Tail risk modelling may be difficult compared to other markets. The resemblance between China and South Africa can stem from the closeness between their equities composition. However, generally, there is evidence of more homogeneity than heterogeneity in risk models. This is indicated by a minimum of three models (out of six) per equity in most of the countries. This may ease the burden for risk managers to find the optimal set of models. Our study is important for internal risk modelling, regulatory oversight, reduce regulatory arbitrage and may bolster confidence in international investors with respect to emerging markets equities.

Forecasting Inflation in Latin American Countries Using a SARIMA-LSTM Combination

Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.

Automatic Indexation of the Pension Age to Life Expectancy: When Policy Design Matters

Increasing retirement ages in an automatic or scheduled way with increasing life expectancy at retirement is a popular pension policy response to continuous longevity improvements. The question addressed here is: to what extent is simply adopting this approach likely to fulfill the overall goals of policy? To shed some light on the answer, we examine the policies of four countries that have recently introduced automatic indexation of pension ages to life expectancy–The Netherlands, Denmark, Portugal and Slovakia. To this end, we forecast an alternative period and cohort life expectancy measures using a Bayesian Model Ensemble of heterogeneous stochastic mortality models comprised of parametric models, principal component methods, and smoothing approaches. The approach involves both the selection of the model confidence set and the determination of optimal weights. Model-averaged Bayesian credible prediction intervals are derived accounting for various stochastic process, model, and parameter risks. The results show that: (i) retirement ages are forecasted to increase substantially in the coming decades, particularly if a constant period in retirement is targeted; (ii) retirement age policy outcomes may substantially deviate from the policy goal(s) depending on the design adopted and its implementation; and (iii) the choice of a cohort over period life expectancy measure matters. In addition, the distributional issues arising with the increasing socio-economic gap in life expectancy remain largely unaddressed.

TD 2611 - Previsão de Homicídios no Brasil: proposta de variável antecedente

Neste estudo, investigamos a capacidade de variáveis antecedentes, entre elas internações por agressão, na previsão do número de homicídios no Brasil. O objetivo principal desta pesquisa é suprimir a lacuna referente à defasagem de informações na divulgação sobre homicídios no país, permitindo assim análises conjunturais atualizadas. Para tanto, por intermédio do esquema rolling window e da abordagem model confidence set (MCS), investigamos se modelos de variáveis antecedentes apresentam desempenho preditivo superior ao conjunto de modelos univariados. Ao aplicar a abordagem MCS, considerando diferentes estatísticas de avaliação, funções de perda e janelas de estimação, encontramos fortes evidências da capacidade das variáveis antecedentes utilizadas fornecerem conteúdo informacional adicional na previsão da dinâmica criminal brasileira, com modelos de variáveis antecedentes sistematicamente superando modelos univariados. Na média, os melhores modelos de variáveis antecedentes apresentam melhorias relativas ao benchmark random walk, de 60% em termos de raiz do erro quadrado médio (RMSE), erro absoluto médio (MAE) e desvio absoluto médio da média (MAD).

Synergy in fertility forecasting: improving forecast accuracy through model averaging

Accuracy in fertility forecasting has proved challenging and warrants renewed attention. One way to improve accuracy is to combine the strengths of a set of existing models through model averaging. The model-averaged forecast is derived using empirical model weights that optimise forecast accuracy at each forecast horizon based on historical data. We apply model averaging to fertility forecasting for the first time, using data for 17 countries and six models. Four model-averaging methods are compared: frequentist, Bayesian, model confidence set, and equal weights. We compute individual-model and model-averaged point and interval forecasts at horizons of one to 20 years. We demonstrate gains in average accuracy of 4–23% for point forecasts and 3–24% for interval forecasts, with greater gains from the frequentist and equal weights approaches at longer horizons. Data for England and Wales are used to illustrate model averaging in forecasting age-specific fertility to 2036. The advantages and further potential of model averaging for fertility forecasting are discussed. As the accuracy of model-averaged forecasts depends on the accuracy of the individual models, there is ongoing need to develop better models of fertility for use in forecasting and model averaging. We conclude that model averaging holds considerable promise for the improvement of fertility forecasting in a systematic way using existing models and warrants further investigation.

Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin

In this paper, we study the volatility forecasts in the Bitcoin market, which has become popular in the global market in recent years. Since the volatility forecasts help trading decisions of traders who want a profit, the volatility forecasting is an important task in the market. For the improvement of the forecasting accuracy of Bitcoin’s volatility, we develop the hybrid forecasting models combining the GARCH family models with the machine learning (ML) approach. Specifically, we adopt Artificial Neural Network (ANN) and Higher Order Neural Network (HONN) for the ML approach and construct the hybrid models using the outputs of the GARCH models and several relevant variables as input variables. We carry out many experiments based on the proposed models and compare the forecasting accuracy of the models. In addition, we provide the Model Confidence Set (MCS) test to find statistically the best model. The results show that the hybrid models based on HONN provide more accurate forecasts than the other models.

Comparing the Performances of GARCH-type Models in Capturing Cryptocurrencies Volatility

The analysis of cryptocurrencies market behaviour is receiving significant attention from researchers and practitioners in the last decades. This paper aims at contributes to volatility estimations of the cryptocurrencies helping to highlight the main stylized facts and characteristics. The performance of different specifications of volatility modelling, within the GARCH class, have been compared through the Model Confidence Set (MCS) over four of the most capitalised cryptocurrencies, namely Bitcoin, Ethereum, Stellar and Ripple. Our empirical findings give evidence of strong asymmetric effects in cryptocurrencies volatility leading to a better performance of asymmetric GARCH specifications..