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Abstract: This paper studies the spectrum of the idiosyncratic volatility (IVOL) puzzle in the Chinese A-share market using functional data analysis (FDA). It highlights a nonlinear IVOL puzzle with a steady reduction in the bottom 20% of average returns and a large drop of 1% in the top 10%, consistent with the herding, certainty, and reflection effects in China’s A-share markets. Furthermore, empirical evidence suggests that the FDA technique has a 30% greater goodness of fit than linear regressions, suggesting that nonlinearity plays a non-negligible role in the IVOL puzzle. These results can be useful for investors and hedgers, as they show that stock returns decline accelerated as the IVOL increases. PubDate: 2022-05-11
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Abstract: This paper investigates the asymmetric behavior of oil price volatility using different types of Asymmetric Power ARCH (APARCH) model. We compare the estimation and forecasting performance of the models estimated from the maximum likelihood estimation (MLE) method and support vector machine (SVM) based regressions. Combining nonparametric SVM method with parametric APARCH model not only enables to keep interpretations of the parametric models but also leads to more precise estimation and forecasting results. Daily or weekly oil price volatility is investigated from March 8, 1991 to September 13, 2019. This whole sample period is split into four sub-periods based on the occurrence of certain economic events, and we examine whether the asymmetric behavior of the volatility exists in each sub-period. Our results indicate that SVM regression generally outperforms the other method with lower estimation and forecasting errors, and it is more robust to the choice of different APARCH models than the MLE counterparts are. Besides, the estimation results of the SVM based regressions in each sub-period show that the ARCH models with asymmetric power generally perform better than the models with symmetric power when the data sub-period includes large swings in oil price. The asymmetric behavior of oil price volatility, however, is not detected when the analysis is done using the whole sample period. This result underscores the importance of identifying the dynamics of the dataset in different periods to improve estimation and forecasting performance in modelling oil price volatility. This paper, therefore, examines volatility behavior of oil price with both methodological and economic underpinnings. PubDate: 2022-05-06
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Abstract: Our objective is to solve the time-fractional Vasicek model for European options with a new stabled relaxation method. This new approach is based on the splitting method. Some numerical tests are presented to show the stability and the reliability of our approach with the theory of options. PubDate: 2022-05-06
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Abstract: Abstract In this paper we define a new auction, called the Draw auction. It is based on the implementation of a draw when a minimum price of sale is not reached. We find that a Bayesian Nash equilibrium is reached in the Draw auction when each player bids his true personal valuation of the object. Furthermore, we show that the expected profit for the seller in the Draw auction is greater than in second-price auctions, with or without minimum price of sale. We make this affirmation for objects whose valuation can be modeled as a bimodal density function in which the first mode is much greater than the second one. Regarding the Myerson auction, we show that the expected profit for the seller in the Draw auction is nearly as good as the expected profit in the optimal auction, with the difference that our method is much more simple to implement than Myerson’s one. All these results are shown by computational tests, for whose development we have defined an algorithm to calculate Myerson auction. PubDate: 2022-05-03
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Abstract: Abstract In this paper, a new concept for some stochastic process called fractional G-Brownian motion (fGBm) is developed and applied to the financial markets. Compared to the standard Brownian motion, fractional Brownian motion and G-Brownian motion, the fGBm can consider the long-range dependence and uncertain volatility simultaneously. Thus it generalizes the concepts of the former three processes, and can be a better alternative in real applications. Driven by the fGBm, a generalized fractional Black–Scholes equation (FBSE) for some European call option and put option is derived with the help of Taylor’s series of fractional order and the theory of absence of arbitrage. Meanwhile, some explicit option pricing formulas for the derived FBSE are also obtained, which generalize the classical Black–Scholes formulas for the prices of European options given by Black and Scholes in 1973. PubDate: 2022-04-25
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Abstract: Abstract The prediction of stock market volatility is an important and challenging task in the financial market. Recently, neural network approaches have been applied to obtain better prediction of volatility, however, there have been few studies on artificial manipulation of the volatility distribution. Because the probability density of volatility is extremely biased to the left, it is a challenging problem to obtain successful predictions on the right side of the density domain, that is, abnormal events. To overcome the problem, we propose a novel approach, we call it Volume-Up (VU) strategy, that manipulates the original volatility distributions of invited explanatory variables including the Standard & Poor’s 500 (S&P 500) stock index by taking a non-linear function on them. Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) are used as our implementation models to test the performances of VU. It is found that the manipulated information improves the prediction performance of one day ahead volatility not only on the left but also on the right probability density region of S&P 500. Averaged gains of root mean square error (RMSE) and RMSE on \(P>0.8\) against the native strategy over all the three models were 27.0% and 19.9%, respectively. Additionally, the overlapping area between label and prediction is employed as an error metric to assess the distributional effects by VU, and the result shows that VU contributes to enhance prediction performances by enlarging the area. PubDate: 2022-04-24
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Abstract: Abstract We consider inference for linear regression models estimated by weighted-average least squares (WALS), a frequentist model averaging approach with a Bayesian flavor. We propose a new simulation method that yields re-centered confidence and prediction intervals by exploiting the bias-corrected posterior mean as a frequentist estimator of a normal location parameter. We investigate the performance of WALS and several alternative estimators in an extensive set of Monte Carlo experiments that allow for increasing complexity of the model space and heteroskedastic, skewed, and thick-tailed regression errors. In addition to WALS, we include unrestricted and fully restricted least squares, two post-selection estimators based on classical information criteria, a penalization estimator, and Mallows and jackknife model averaging estimators. We show that, compared to the other approaches, WALS performs well in terms of the mean squared error of point estimates, and also in terms of coverage errors and lengths of confidence and prediction intervals. PubDate: 2022-04-22
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Abstract: Abstract The purpose of the paper is to predict Bitcoin prices using various machine learning techniques. Due to its high volatility attribute, accurate price prediction is the need of the hour for sound investment decision-making. At the offset, this study categorizes Bitcoin price by daily and high-frequency price (5-min interval price). For its daily and 5-min interval price prediction, a set of high-dimensional features and fundamental trading features are employed, respectively. Thereafter, we find that statistical methods like Logistic Regression predict daily price with 64.84% accuracy while complex machine learning algorithms like XGBoost predict 5-min interval price with an accuracy level of 59.4%. This work on Bitcoin price prediction recognizes the significance of sample dimensions in machine learning algorithms. PubDate: 2022-04-21
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Abstract: Abstract The present work aims to optimize the time index of financial engineering to improve the efficiency of financial decision-making. A Back Propagation Neural Network (BPNN) model is designed and optimized by the Ant Colony Algorithm (ACA) based on the bionic algorithm and Deep Learning (DL). After introducing the basic knowledge of neural networks and bionic algorithms, the advantages and disadvantages of the algorithms are integrated for maximal effects. Besides, ACA optimizes the weights and thresholds in the neural network in complex problems to reduce the relative error, enhance the stability and accuracy, and improve the classification speed of the BPNN model. The experimental results indicate that the classification accuracy of the ACA model is 91.3%, and the area under the receiver operating characteristic curve is 0.867. Moreover, the running time of BPNN based on ACA is 2.5 s, the error is 0.2, and the required number of iteration steps is 36 times, better than the test results of similar algorithms. These results demonstrate that the improved BPNN based on ACA has higher classification efficiency, better efficiency and smaller errors than the traditional BPNN. In terms of financial engineering decision-making, the time index of decision-making has been significantly improved, which is conducive to reducing the decision-making risk of financial institutions and has a positive effect on improving the overall operational efficiency of enterprises. PubDate: 2022-04-20
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Abstract: Abstract External barrier options are financial securities that have two assets for stochastic variables, where the payoff depends on one underlying asset and the barrier depends on another state variable such that it determines whether the option is knocked in or out. In this study, considering the financial derivatives subject to the default risks of the option writer in over-the-counter markets since the global financial crisis of 2007–2008, we study vulnerable external barrier option prices by utilizing multivariate Mellin transforms and the method of images and then examine the behaviors and sensitivities of the vulnerable external barrier option prices in terms of the model parameters. Based on the results obtained, our study has two main contributions. First, by using multivariate Mellin transform approaches, we can find an explicit-form pricing formula for the option prices more effectively and easily, resolving the complexity of calculation of the option prices by using probabilistic or other methods. Second, we verify that our closed-form solution has been accurately and efficiently obtained by comparing the closed-form solution with the Monte Carlo simulation solution. PubDate: 2022-04-15
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Abstract: Abstract With a sample of monthly data from January 2000 to July 2021, this paper investigates the risk connectedness relationship between different kinds of China’s EPU and global oil prices in both time and frequency domains. To achieve that, a research framework mainly consists of wavelet transform method and spillover index approach is established. The results show that EPU of China receives the risk spillover from global oil prices in most cases. Moreover, we find fiscal policy uncertainty and trade policy uncertainty are generally the recipients of risk spillover on most time scales, except that monetary policy uncertainty primarily serves as the risk transmitter. Lastly, the risk role of exchange rate policy uncertainty in China has the most frequent change among four kinds of EPU. This paper provides valuable policy implications for policymakers, investors and risk managers in the energy market. PubDate: 2022-04-15
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Abstract: Abstract The objective of this work is to valuate a European standard call option with different types of volatilities and a European exchange option based on the price of underlying assets through numerical experiments using the Euler–Maruyama stochastic numerical method with strong approximation. Different trajectories of Brownian motion are simulated with different time steps, different risk-free interest rates, different levels of volatility, different strikes or exercise price and different expiration times, assuming constant initial values for the different subjacent assets. The results obtained in the valuation of the options considered show that the proposed method presents very low mean squared errors compared to the valuation obtained from the reference methods: The Black–Scholes formula for an asset, Margrabe for two assets and the Euler–Maruyama scheme with weak approximation are analyzed for all the scenarios proposed. The strong Euler–Maruyama method becomes an attractive method for future research in terms of options valuation where there is no explicit formula. The results show that the proposed method can also be considered to value options, over one or more assets, since it produces a low mean square error in the analyzed scenarios. PubDate: 2022-04-13
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Abstract: Abstract In the past, the bottom-up study of financial stock markets relied on first-generation multi-agent systems (MAS) , which employed zero-intelligence agents and often required the additional implementation of so-called noise traders to emulate price formation processes. Nowadays, thanks to the tools developed in cognitive science and machine learning, MAS can quantitatively gauge agent learning, a pivotal element for information and stock price estimation in finance. In our previous work, we therefore devised a new generation MAS stock market simulator , which implements two key features: firstly, each agent autonomously learns to perform price forecasting and stock trading via model-free reinforcement learning ; secondly, all agents ’ trading decisions feed a centralised double-auction limit order book, emulating price and volume microstructures. Here, we study which trading strategies (represented as reinforcement learning policies) the agents learn and the time-dependency of their heterogeneity. Our central result is that there are more ways to succeed in trading than to fail. More specifically, we find that : i- better-performing agents learn in time more diverse trading strategies than worse-performing ones, ii- they tend to employ a fundamentalist, rather than chartist, approach to asset price valuation, and iii- their transaction orders are less stringent (i.e. larger bids or lower asks). PubDate: 2022-04-10
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Abstract: Abstract Investors usually resort to financial advisors to improve their investment process until the point of complete delegation on investment decisions. Surely, financial advice is potentially a correcting factor in investment decisions but, in the past, the media and regulators blamed biased advisors for manipulating the expectations of naive investors. In order to give an analytic formulation of the problem, we present an Agent-Based Model formed by individual investors and a financial advisor. We parametrize the games by considering a compromise for the financial advisor (between a sufficient reward by bank and to keep her reputation), and a compromise for the customers (between the desired return and the proposed return by advisor), and incorporating the social psychological concepts of truthfulness and cognitive dissonance. Then we obtain the Nash equilibria and the best response functions of the resulting game. We also describe the parameter regions in which these points result acceptable equilibria. In this way, the greediness/naivety of the customers emerge naturally from the model. Finally, we focus on the efficiency of the best Nash equilibrium. PubDate: 2022-04-09
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Abstract: Abstract Contemporary debates about scientific institutions and practice feature many proposed reforms. Most of these require increased efforts from scientists. But how do scientists’ incentives for effort interact' How can scientific institutions encourage scientists to invest effort in research' We explore these questions using a game-theoretic model of publication markets. We employ a base game between authors and reviewers, before assessing some of its tendencies by means of analysis and simulations. We compare how the effort expenditures of these groups interact in our model under a variety of settings, such as double-blind and open review systems. We make a number of findings, including that open review can increase the effort of authors in a range of circumstances and that these effects can manifest in a policy-relevant period of time. However, we find that open review’s impact on authors’ efforts is sensitive to the strength of several other influences. PubDate: 2022-04-08
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Abstract: Abstract This paper derives a macroeconomic resilient control framework that provides the optimal feedback fiscal and monetary policy responses in response to a potentially large negative external incident. We simulate the model for the U.S. under the conditions that prevailed throughout the 2020 economic crisis that occurred due to the government lockdown that was caused by the coronavirus pandemic. We develop a discrete-time soft-constrained linear-quadratic dynamic game under a worst-case design with multiple disturbances. Within this context, we introduce a resilience feedback response and compare the case where the policymakers counter in response the external incident with the case when they do not counter. This framework is especially applicable to large-scale macroeconomic tracking control models and wavelet-based control models when formulating the magnitudes of the policy changes necessary for the unemployment rate and national output variables to maintain acceptable tracking errors in the periods following a major disruption. Our policy recommendations include the maintenance of “rainy day” funds at appropriate levels of government to mitigate the effects of large adverse events. PubDate: 2022-04-02
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Abstract: The study aims to analyze and forecast Internet financial risks based on the model based on deep learning and the Back Propagation Neural Network (BPNN). First, the theory of Internet financial risks is introduced and a theoretical framework for analyzing and forecasting internet financial risks is established. Second, the theory of the BPNN and the algorithms based on deep learning are introduced. Then, the model based on the BPNN and deep learning is implemented to improve the early warning of Internet financial risks, analyze the data image of China's Gross Domestic Product (GDP), currency (M2), non-performing loan records, and the Shanghai Composite Index from 2006 to 2020, and forecast the risks in 2021. Through the model based on deep learning and BPNN, it can be found that the trends of the growth rate of China's GDP take on the shapes of V and L, and the trend of M2 is opposite to that of GDP. In the whole year, there is a low at the beginning and the end of the year, and the monthly non-performing loans and the Shanghai Composite Index decrease. The forecast made by the model is that there will be many fluctuations in 2021. At present, China’s economy just enters the era of the new normal, which helps to build a more scientific and sensitive early warning system for financial risks. The model based on the BPNN and deep learning greatly improves the timeliness of forecasts and has a positive impact on the stability of China’s financial environment. PubDate: 2022-04-01
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Abstract: This study proposes a new time series forecasting method that employs possibilistic fuzzy c-means, an autoregressive moving average model (ARMA), and a grey wolf optimizer (GWO) in type-1 fuzzy functions. Type-1 fuzzy functions (T1FFs) were used to forecast functions using an autoregressive model. However, rather than relying solely on past values of the forecast variable in a regression, the inclusion of past forecast errors improves forecasting ability. In this sense, the moving average model also employed in the proposed method. The inputs therefore are a combination of the past values of the time series and the past errors. The main idea of T1FFs is to include a new variable (or variables) that provides more information about the time series. The fuzzy c-means clustering (FCM) algorithm was used to quantify the values of this new variable. The degrees of memberships were obtained for each observation in each cluster and these membership grades were used as a new variable in the input matrix. Studies in the literature, however, have shown certain restrictions for FCM, such as sensitive noise and coincidence cluster centers. Consequently, possibilistic FCM is employed in T1FFs to overcome the aforementioned limitations. Because of the non-derivative objective function of ARMA type possibilistic fuzzy forecasting functions, the GWO was adapted in order to obtain coefficients for the model. The performance of the proposed ARMA type-1 fuzzy possibilistic functions was validated using 16 practical time-series. PubDate: 2022-04-01
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Abstract: Based on a two-dimensional valence-arousal sentiment evaluation method, we used the sentiment extracted from the text of online news media and stock forums to predict future financial performance of publicly listed companies. A Chinese lexicon called the Chinese Valence-Arousal Words, provided by Yu et al. (2016), was used to obtain the valence and arousal scores of all Web text for each of the 183 large listed companies for each fiscal quarter over the Q1 2013 through Q3 2017 period. Our empirical results tended to support our two hypotheses that there is a positive association between the valence (under Hypothesis 1) or arousal-augmented valence (under Hypothesis 2) of online public opinion about the listed companies and their future financial performance. In particular, when the sentiment was positive (negative) in the current fiscal quarter, whether measured by the valence alone or by the arousal-augmented valence, the financial performance (measured by ROA, ROE, and Tobin’s Q) observed in the next quarter would tend to be better (worse). PubDate: 2022-04-01
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