Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract As one can see in many previous well-known papers, an one–factor stochastic volatility model has its limitation to fit the market dynamics. Based on empirical facts that the market volatility can be well explained by the combination of short-term and long-term volatilities, a multi–scale stochastic volatility model that is governed by two factors evolving on different time-scales: a fast mean-reverting factor and a persistent, slow mean-reverting factor is applied to capture the dynamics of two assets in this paper. The validity of the model was tested by calibration against the market return distribution of the S&P 500 and Dow Jones Industrial Average Indices. Based on this multiscale model, an analytically approximate formula, in terms of the Gaussian copula, was obtained for the joint transition density and the parameters of this density were estimated using daily data from the S&P 500 and DAX Indices. PubDate: 2022-05-24
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Technological developments play a crucial role in allowing governments and industries to meet carbon emission targets, whilst maintaining cost effectiveness. Mathematical modeling related to climate change has often included technology (including technology transfer between nations) as an effective policy instrument. However, such models often incorporate technology as an exogenous variable, highlighting the need to further interrogate the role of technology, its dynamics and limitations on reducing international pollution levels to improve sustainability, energy reliability and subsequent policy initiatives. Hence, in this study, we consider technology as an endogenous variable within a broader trans-boundary industrial pollution problem with random interference factors to obtain a closed-loop (Markov perfect) Nash equilibrium. We then articulate the Nash non-cooperative and cooperative equilibria via a stochastic linear quadratic differential game paradigm and prove the stability of a cooperative game by using Pareto optimal solution. We show that under such strategies to control carbon pollution a cooperative game is more efficient than a non-cooperative game, emphasizing the importance of technology transfer and collaboration between nations, subsequently serving as a mutual benefit for multi-lateral efforts to reduce global carbon emissions. In doing so, our study highlights the role of government subsidy incentives when collaborating with industry to encourage the integration of carbon-reducing technologies, whilst simultaneously increasing each country’s net revenue. Hence, our study provides a novel insight and framework for policymakers when encouraging industry to use carbon capturing and storage technologies. We also emphasize that efforts to coordinate emissions control should be pursued jointly to ensure mutual benefit for government and industry alike. PubDate: 2022-05-23
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract In this paper we study the interdependences between the dynamics of the stock market indexes of 30 different stock markets across 29 different countries to analyze the nonlinear dynamics of their information flows. We find that the system exhibits complex dynamic properties that go beyond what has been generally found in the previous literature, suggesting that the structure of information flows is regulated by subtle homeostatic forces that cause the roles of the single markets in the whole network to evolve in unexpected ways. We present a toolkit of ANN-based methods that can be systematically deployed to analyze different aspects of such dynamics. PubDate: 2022-05-20
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract This paper proposes an extension of the Bayesian instrumental variables regression which allows spatial and temporal correlation among observations. For that, we introduce a double separable covariance matrix, adopting a Conditional Autoregressive structure for the spatial component, and a first-order autoregressive process for the temporal component. We also introduce a Bayesian multiple imputation to handle missing data considering uncertainty. The inference procedure is described joint with a step by step Monte Carlo Markov Chain algorithm for parameters estimation. We illustrate our methodology through a simulation study and a real application that investigates how broadband affects the Gross Domestic Product of municipalities in the state of Mato Grosso do Sul from 2010 to 2017. PubDate: 2022-05-19
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: Abstract Using 1-min data, we explore the dynamic variation of the intraday lead–lag relations between stock indices and their derivatives through a comprehensive study with broader coverage of research objectives and methodologies. This paper provides explicit evidence that the futures and options exhibit price leadership over the spot market, and the options is ahead of the futures on most trading days in all three markets. This paper also reports a new finding that the relation between the derivative and its underlying index reverses when the index return has a significantly larger mean value, and the reversal phenomenon is also observed in the relations between the futures and the options, which enriches the empirical results of intraday lead–lag relations. Moreover, these conclusions still hold under the impact of extreme events, e.g., the outbreak of the Covid-19. Finally, we construct a pair trading strategy based on the intraday lead–lag relationships, which can get better performance than the corresponding spot index. Our findings can potentially help regulators understand the price discovery process between the index and its derivatives, and also be of great value for timely adjustment of investors intraday trading strategies. PubDate: 2022-05-14
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: 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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: 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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: 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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
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
Hybrid journal (It can contain Open Access articles)
[2469 journals]
