Forecasting Stock Prices via Deep Learning During COVID-19: A Case Study from an Emerging Economy

In this study we apply a Deep Learning Technique to predict stock prices for the 30 stocks that compose the BIST30, Turkish Stock Market Index before and after the onset of Covid-19 crises. Specifically, we utilize the Bi-Directional Long-Short Term Memory (BiLSTM) model which is a variation of the Long-Short-Term Memory (LSTM) model to predict stock prices for the BIST30 stocks. We compare the performance of the model to other commonly used machine learning models like decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), artificial neural networks (ANN), and other deep Leaning models like recurrent neural network (RNN), and the Long-Short-Term Memory (LSTM) model. The BiLSTM model seems to have better performance compared to conventional models used for predicting stock prices and continues to have superior performance in the Covid19 period. The LSTM model seems to have a good overall performance and is the next best model.&nbsp

Volatility Distribution of the DJSTOXXE50 Index

In this paper using data from 1995-2005 on 5-minute intraday returns, we construct a model free estimate of the daily realized volatility for the DJSTOXXE50 index. We compute the unconditional volatility distribution of the DJSTOXXE50 index by a nonparametric kernel estimation method. Our results indicate that the unconditional volatility distribution of the DJSTOXXE50 returns are leptokurtic and highly skewed to the right. The logarithmic standard deviations seem to be approximately Gaussian. Our results are inline with previous research for individual DJIA equity return volatility and for Japanese index, Nikkei 22

Japanese Foreign Exchange Intervention and the Yen/Dollar Exchange Rate: A Simultaneous Equations Approach Using Realized Volatility

We use realized volatility to study the influence of central bank interventions on the yen/dollar exchange rate. Realized volatility is a technical innovation that allows specifying a system of equations for returns, realized volatility, and interventions without endogeneity bias. We find that during the period 1995 through 1999, interventions of the Japanese monetary authorities did not have the desired effect with respect to the exchange rate level and we measure an increase in volatility associated with interventions. During the period 1999 through 2004, the estimations are consistent with successful interventions, both in depreciating the yen and in reducing exchange rate volatility.

Japanese foreign exchange intervention and the Yen/Dollar exchange rate: a simultaneous equations approach using realized volatility

We use realized volatility to study the influence of central bank interventions on the yen/dollar exchange rate. Realized volatility is a technical innovation that allows specifying a system of equations for returns, realized volatility, and interventions without endogeneity bias. We find that during the period 1995 through 1999, interventions of the Japanese monetary authorities did not have the desired effect with respect to the exchange rate level and we measure an increase in volatility associated with interventions. During the period 1999 through 2004, the estimations are consistent with successful interventions, both in depreciating the yen and in reducing exchange rate volatility

Sampling properties of criteria for evaluating GARCH volatility forecasts

There is considerable evidence that GARCH models do not forecast financial volatility well out of sample when evaluated by the R2 from the Mincer and Zarnowitz (1969) regression. Andersen and Bollerslev (1998) argued that although the R2s tend to be small, they are consistent with the population value of the criterion for a correctly specified GARCH model. We extend the Andersen and Bollerslev result and derive the population moments of the mean squared error, the mean absolute error and a heteroscedasticity adjusted mean square error for the GARCH volatility forecasts. We state existence conditions for the moments. The criteria and their population values are illustrated with empirical examples. Using Monte Carlo simulation, we analyse the sampling properties of these criteria. When volatility is highly persistent, we find that the sampling distribution of the R2 is highly skewed to the right, which indicates that the majority of the realized R2s lie below the population R2. Among the accuracy criteria, we find the heteroscedasticity adjusted mean-squared error is preferable because it has the weakest existence condition and its sampling distribution is reflective of the population value. 'A Good Volatility Model Forecasts Volatility' Engle and Patton (2001)

A Comparison of the Runs Test for Volatility Forecastability and the LM Test for GARCH Using Aggregated Returns

Christoffersen and Diebold (2000) have introduced a runs test for forecastable volatility in aggregated returns. In this note, we compare the size and power of their runs test and the more conventional LM test for GARCH by Monte Carlo simulation. When the true daily process is GARCH, EGARCH, or stochastic volatility, the LM test has better power than the runs test for the moderate-horizon returns considered by Christoffersen and Diebold. For long-horizon returns, however, the tests have very similar power. We also consider a qualitative threshold GARCH model. For this process, we find that the runs test has greater power than the LM test. Theresults support the use of the runs test with aggregated returns.Aggregated returns, Forecast horizon, GARCH, LM test, Monte Carlo simulation, Runs test, Volatility forecastability,

Out-of-sample forecasting performance of the QGARCH model

The population value of the R 2 is derived from the Mincer-Zarnowitz volatility forecast regression for a QGARCH(1,1). The study shows that the population R 2 exceeds that of the standard GARCH(1,1). This indicates that accounting for asymmetry in the conditional variance process can increase the predictive power of volatility forecasts. As with the standard GARCH(1,1) model, however, the R 2 is still bounded by the reciprocal of the innovation kurtosis. As a result, small values of the R 2 should be anticipated when using the QGARCH(1,1) in empirical work.

The Turn-of-the-Month Effect: Evidence from Macedonian Stock Exchange

We examine turn of the month effect for the Macedonian Stock Exchange using daily return data utilizing OLS and pooled regression analysis for the 10 components of the MBI10 index. We find that for most of the individual stock returns the coefficients of the turn-of-the-month effect are all positive indicating the presence of the turn-of-the-month effect. When the data is pooled, we obtain even stronger results. The study confirms that the turn-of-the-month effect holds for Macedonian Stock Exchange which has not been examined before. Therefore, on average, the daily return over the turn-of-the-month effect period is significantly higher than the daily return over the rest-of-the-month period and hence providing room for more investment opportunities