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on Econometric Time Series |
| By: | St\'ephane Cr\'epey (LPSM, UPCit\'e); Lehdili Noureddine (LPSM, UPCit\'e); Nisrine Madhar (LPSM, UPCit\'e); Maud Thomas (LPSM, SU) |
| Abstract: | We consider time series representing a wide variety of risk factors in the context of financial risk management. A major issue of these data is the presence of anomalies that induce a miscalibration of the models used to quantify and manage risk, whence potentially erroneous risk measures on their basis. Therefore, the detection of anomalies is of utmost importance in financial risk management. We propose an approach that aims at improving anomaly detection on financial time series, overcoming most of the inherent difficulties. One first concern is to extract from the time series valuable features that ease the anomaly detection task. This step is ensured through a compression and reconstruction of the data with the application of principal component analysis. We define an anomaly score using a feed-forward neural network. A time series is deemed contaminated when its anomaly score exceeds a given cutoff. This cutoff value is not a hand-set parameter, instead it is calibrated as a parameter of the neural network throughout the minimisation of a customized loss function. The efficiency of the proposed model with respect to several well-known anomaly detection algorithms is numerically demonstrated. We show on a practical case of value-at-risk estimation, that the estimation errors are reduced when the proposed anomaly detection model is used, together with a naive imputation approach to correct the anomaly. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.11686&r= |
| By: | Leonardo Marinho |
| Abstract: | I develop the concept of impulse response in a causal fashion, defining analytical tools suitable for different policy analysis. Applications of techniques presented to models containing features like confounders or nonlinearities through Monte Carlo experiments are given. I also apply some of these techniques to practical macroeconomic problems, computing impulse responses of GDP, interest rate, inflation and real exchange rate to monetary policy decisions of Banco Central do Brasil, the Brazilian Central Bank. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:570&r= |
| By: | Yaya, OlaOluwa A; Lukman, Adewale F.; Vo, Xuan Vinh |
| Abstract: | The paper investigated persistence, returns and volatility spill overs from the Bitcoin market to Gold and Silver markets using daily datasets from 2 January 2018 to 31 July 2020. We applied the fractional persistence framework to the price series, returns and volatility proxy series. The results showed that price persistence with Bitcoin posed the highest volatility, while Silver posed the lowest volatility. The results of multivariate GARCH modelling, using the CCC-VARMA-GARCH model and other lower variants indicated the impossibility of returns spill over between Bitcoin and Gold (or Silver) market, while there existed volatility spill overs and these were bi-directional in form of shocks and volatility transmissions. Appropriate portfolio management and hedging strategies rendered towards the end of the paper required more gold and silver investments in the portfolio of Bitcoin to fully have the diversification advantage and reduce risk to the minimum without reducing the portfolio return expectancy. |
| Keywords: | Bitcoin; Commodity markets; CCC-VARMA-GARCH model; Volatility spill overs; Portfolio management |
| JEL: | C22 |
| Date: | 2022–09–09 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114521&r= |
| By: | Yaya, OlaOluwa S.; Ogbonna, Ahamuefula E.; Adesina, Ayobami O.; Alobaloke, Kafayat; Vo, Xuan Vinh |
| Abstract: | Extant literature establishes co-movements among commodity (metal and oil) prices; whereas oil price/shocks aggregate, as a lone predictor, has relative predictability for most financial assets. We assess the predictability of Baumeister and Hamilton's (2019) decomposed oil shocks (economic activity shocks, oil consumption demand shocks, oil inventory demand shocks, and oil supply shocks) for conditional volatilities of prominently traded precious metals (gold, palladium, platinum, and silver) using GARCH-MIDAS-X framework. The asymmetric effect of decomposed oil shocks on precious metals’ volatilities is examined. The DCC-MIDAS framework allows to investigate the conditional correlations and volatility between oil and precious metal prices. Results show that precious metals exhibit hedging potentials against oil demand and supply shocks, with heterogeneity observed in the precious metal-oil shocks nexus. Asymmetry is evident in the responses of metals’ volatility to oil shocks. DCC-MIDAS results reveal significant dynamic correlations between oil prices and precious metals (except for platinum). Our results are robust (sensitive) to precious metals (oil shocks) proxies. The findings are insightful for commodity market stakeholders. |
| Keywords: | GARCH-MIDAS; DCC-MIDAS; Disaggregated oil shocks; Dynamic correlation; Platinum |
| JEL: | C22 |
| Date: | 2022–09–23 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:114689&r= |
| By: | Guglielmo Maria Caporale; José Javier de Dios Mazariegos; Luis A. Gil-Alana |
| Abstract: | This paper applies fractional integration and cointegration methods to examine respectively the univariate properties of the four main cryptocurrencies in terms of market capitalization (BTC, ETH, USDT, BNB) and of four US stock market indices (S&P500, NASDAQ, Dow Jones and MSCI for emerging markets) as well as the possible existence of long-run linkages between them. Daily data from 9 November 2017 to 28 June 2002 are used for the analysis. The results provide evidence of market efficiency in the case of the cryptocurrencies but not of the stock market indices considered. They also indicate that in most cases there are no long-run equilibrium relationships linking the assets in question, which implies that cryptocurrencies can be a useful tool for investors to diversify and hedge when required in the case of the US markets. |
| Keywords: | stock market prices, cryptocurrencies, persistence, fractional integration and cointegration |
| JEL: | C22 C58 G11 G15 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9950&r= |
| By: | B. Cooper Boniece; Jos\'e E. Figueroa-L\'opez; Yuchen Han |
| Abstract: | Statistical inference for stochastic processes based on high-frequency observations has been an active research area for more than two decades. One of the most well-known and widely studied problems is the estimation of the quadratic variation of the continuous component of an It\^o semimartingale with jumps. Several rate- and variance-efficient estimators have been proposed in the literature when the jump component is of bounded variation. However, to date, very few methods can deal with jumps of unbounded variation. By developing new high-order expansions of the truncated moments of a locally stable L\'evy process, we construct a new rate- and variance-efficient volatility estimator for a class of It\^o semimartingales whose jumps behave locally like those of a stable L\'evy process with Blumenthal-Getoor index $Y\in (1,8/5)$ (hence, of unbounded variation). The proposed method is based on a two-step debiasing procedure for the truncated realized quadratic variation of the process. Our Monte Carlo experiments indicate that the method outperforms other efficient alternatives in the literature in the setting covered by our theoretical framework. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.10128&r= |
| By: | Ruochen Xiao; Yingying Feng; Lei Yan; Yihan Ma |
| Abstract: | MAE, MSE and RMSE performance indicators are used to analyze the performance of different stocks predicted by LSTM and ARIMA models in this paper. 50 listed company stocks from finance.yahoo.com are selected as the research object in the experiments. The dataset used in this work consists of the highest price on transaction days, corresponding to the period from 01 January 2010 to 31 December 2018. For LSTM model, the data from 01 January 2010 to 31 December 2015 are selected as the training set, the data from 01 January 2016 to 31 December 2017 as the validation set and the data from 01 January 2018 to 31 December 2018 as the test set. In term of ARIMA model, the data from 01 January 2016 to 31 December 2017 are selected as the training set, and the data from 01 January 2018 to 31 December 2018 as the test set. For both models, 60 days of data are used to predict the next day. After analysis, it is suggested that both ARIMA and LSTM models can predict stock prices, and the prediction results are generally consistent with the actual results;and LSTM has better performance in predicting stock prices(especially in expressing stock price changes), while the application of ARIMA is more convenient. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.02407&r= |