Predicting the Benchmark Banking Indicator (IBR) Time Series through Neural Networks

En años recientes, la predicción del comportamiento del Indicador Bancario de Referencia (IBR) se ha hecho relevante debido a su importancia en el mercado monetario colombiano. El propósito de este trabajo es demostrar la eficiencia de las redes LSTM en la generación de predicciones de series de tiempo —a través de su memoria a corto y largo plazo— que sean comparables con el modelo predictivo ARIMA para estudios econométricos. Se analizó la incidencia de la tasa representativa de mercado (TRM) y la tasa de los bonos de la deuda pública (TES) a 10 años, comparando ambos indicadores con el IBR. Con lo anterior, se buscó determinar la correlación existente entre estas variables mediante el método de Pearson. Finalmente, la eficiencia del modelo fue evaluada con el error cuadrático medio (RMSE), utilizando una red LSTM multivariable con tres entradas (IBR, TES y TRM) y una salida.In recent years, predicting the behavior of the Benchmark Banking Indicator (IBR) has become relevant due to its importance in the Colombian money market. The purpose of this paper is to demonstrate the efficiency of LSTM networks for generating predictions of time series —through their long and short-term memory— that are comparable with the ARIMA predictive model for econometric studies. The incidence of the representative market rate (TRM) and the rate of 10-year public debt bonds (TES) was analyzed and compared to the IBR, seeking to determine its correlation through the Pearson method. Finally, the model efficiency was evaluated with the mean square error (RMSE), using a multivariable LSTM network with three inputs (IBR, TES, and TRM) and one output

Advanced neural networks : finance, forecast, and other applications

[no abstract

The Influence of COVID-19 on Sustainable Economy

This book examines applied studies contributing to the issues of financial markets and sustainable economy in conditions of COVID-19 pandemic. All studies in this book applied complex models with quantitative data in the areas of finance, macro and sustainable economy, as well as business and management, to express the main issues of the financial–economic universe during the pandemic crisis. Some of the studies offer possible solutions in the sustainable post-COVID era. This book is also of particular interest in relation to the green economy during the COVID-19 pandemic

Empirical studies of over-the-counter currency option contracts

 It is a well-established fact that the foreign exchange market is the largest financial market in the world1. However, it is relatively less well-known that currency options and other foreign exchange-related derivatives have become more popular and prominent in size since the mid-1980’s. Today, currency options are used by numerous players in the financial market, including portfolio managers, hedgers, speculators and even central bankers. Despite their popularity amongst market participants, research in currency options has received little attention in comparison with options on stocks and other underlying assets. This is not surprising as most of the currency option contracts are written by commercial and investment banks in the privately negotiated over-thecounter option markets rather than the exchange-traded markets. This thesis provides empirical investigations into the behaviour of implied volatility quotes for currency options on the British pound/U.S. dollar (GBP/USD), the euro/U.S. dollar (EUR/USD), the Australian dollar/U.S. dollar (AUD/USD) and the U.S. dollar/Japanese yen (USD/JPY). The analyses are performed using dealer-quoted implied volatility and spot exchange rate datasets collected from the over-the-counter currency option market. 1 According to the Triennial Central Bank Survey conducted by the Bank for International Settlements, global foreign exchange market recorded a daily turnover of USD3.21 trillion in April 2007 (See Table B.1 of the survey released in December 2007). viii Two main aspects of the implied volatility quotes are examined in this dissertation. First, the time series behaviour of implied volatility of various maturities is analysed. Second, analysis concerning the dynamics of implied volatility smiles for these four currency-pairs is undertaken. The first empirical chapter examines the random walk hypothesis using implied volatility quotes of various maturities. Conventional and nonparametric variance ratio tests are performed on the volatility levels and first-differences. The results provide evidence of random walk violations in the volatility series across all currency pairs examined. Specifically, strong rejections are found in the short-dated volatility of one week and one month. Further, out-of-sample robustness tests suggest that forecasting implied volatility changes using a random walk model produce significantly higher forecasting errors compared with two alternative models based on the artificial neural networks (ANNs) and autoregressive integrated moving average (ARIMA) frameworks. These findings suggest that short-dated implied volatility are better characterised as a mean-reverting process while the random walk process captures long-dated implied volatility more accurately. The analysis in the second chapter extends the key findings by examining the profitability of volatility trading using a simple technical trading strategy. This study concludes that the trading rules generated positive returns in the majority of the currency pairs even after allowing for volatility and exchange rate spreads. The buy straddle signals generate positive average holding-period returns for three of the four currency pairs examined. Further, the average holding-period return of the buy trade is statistically different from the average holding-period return of the sell trade. This is ix especially evident for the USD/JPY straddles. Conversely, risk reversal trades produced less compelling outcomes with lower winning trades and holding-period returns. Thus the overall results suggest that moving average trading rules are useful in volatility trading. In addition the profits from the option strategies are often large enough to offset the transaction costs. The third analysis chapter examines a well-known empirical anomaly in the currency option market. Specifically, the relation between the dynamics of the volatility smile and the anticipated volatility for the GBP/USD, EUR/USD, AUD/USD and USD/JPY currency pairs is investigated. The analysis uses a unique trader-quoted implied volatility dataset to construct the volatility smile over the sample period. To fully capture the time series dynamics of the volatility smile, different measures of volatility smile dynamics are employed, namely, (i) the slope coefficient of the call and put volatility curves, (ii) a measure of curvature, and (iii) the degree of skewness in the daily volatility smile. The Granger-causality tests show that the lagged coefficients for the recursive GARCH estimates are statistically different from zero over the optimal lag choice. This evidence of a unidirectional relationship is particularly strong when the tests are performed using put volatility curves. The results also reveal significant feedback between the curvature of the volatility smile and the quoted volatility. Further, tests are performed using a trivariate vector autoregressive model and impulse response functions to trace the impact of a volatility shock. A robustness test using probit regression suggests evidence of predictability of jumps using the smile curvature and out-of-money options. Consistent with recent literature, this study suggests that the behaviour of the volatility smile is driven by trading activities induced by the anticipated risk in the foreign exchange market. x The final analysis chapter extends earlier empirical work on volatility forecasting using information subsumed in the volatility smile dynamics. Specifically, it combines volatility smile dynamics with corresponding at-the-money implied volatility and GARCH(1,1) volatility estimates to forecast realised exchange rate volatility. The relative information content of the forecasting models is analysed using encompassing regression tests. The coefficients for smile curvature are both significant and negatively related to the level of implied volatility. The validity of the unbiasedness and efficiency hypothesis for the implied volatility forecasts is found to be related to the shape of the volatility smile. In particular, when the smile effect is more pronounced, the forecast performance of the implied volatility series deteriorates.