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Trending academic research:
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Using Natural Language Processing Techniques for Stock Return Predictions
Stock Return Prediction with Fully Flexible Models and Coefficients
A Gentle Introduction to Value at Risk
Naive Risk Parity Portfolio with Fractal Estimation of Volatility abstract A fractal approach to long-only portfolio optimization is proposed. The quantitative system is based on naive risk parity approach. The core of the optimization scheme is a fractal distribution of returns, applied to estimation of the volatility law. Out-of-sample performance data has been represented in ten period of observation with half year and one year horizons. Implementation of fractal estimator of volatility improves all performance metrics of portfolio in comparison to the standard estimator of volatility. The efficiency of fractal estimator plays a significant protective role for the periods of market abnormal volatility and drawdowns, which allows beating the market in the long term perspective. The provided results may be useful for a wide range of quantitative investors, including hedge funds, robo-advisors and retail investors.
Higher-Order Moments of Fundamentals: A Literature Review
Non-parametric and semi-parametric asset pricing abstract We find that the CAPM fails to explain the small firm effect even if its non-parametric form is used which allows time-varying risk and non-linearity in the pricing function. Furthermore, the linearity of the CAPM can be rejected, thus the widely used risk and performance measures, the beta and the alpha, are biased and inconsistent. We deduce semi-parametric measures which are non-constant under extreme market conditions in a single factor setting; on the other hand, they are not significantly different from the linear estimates of the Fama-French three-factor model. If we extend the single factor model with the Fama-French factors, the simple linear model is able to explain the US stock returns correctly.
Emergence of world-stock-market network abstract In the age of globalization, it is natural that the stock market of each country is not independent form the other markets. In this case, collective behavior could be emerged form their dependency together. This article studies the collective behavior of a set of forty influential markets in the world economy with the aim of exploring a global financial structure that could be called world-stock-market network. Towards this end, we analyze the cross-correlation matrix of the indices of these forty markets using Random Matrix Theory (RMT). We find the degree of collective behavior among the markets and the share of each market in their structural formation. This finding together with the results obtained from the same calculation on four stock markets reinforce the idea of a world financial market. Finally, we draw the dendrogram of the cross-correlation matrix to make communities in this abstract global market visible. The dendrogram, drawn by at least thirty percent of correlation, shows that the world financial market comprises three communities each of which includes stock markets with geographical proximity.
Elliptical Black-Litterman Portfolio Optimization
Implied Volatility Changes as Evidence of Stock Price Disequilibrium
Orthogonal Expansions for VIX Options Under Affine Jump Diffusions
Global Portfolio Diversification for Long-Horizon Investors
A Numerical Method for Pricing Discrete Double Barrier Option by Legendre Multiwavelet abstract In this Article, a fast numerical numerical algorithm for pricing discrete double barrier option is presented. According to Black-Scholes model, the price of option in each monitoring date can be evaluated by a recursive formula upon the heat equation solution. These recursive solutions are approximated by using Legendre multiwavelets as orthonormal basis functions and expressed in operational matrix form. The most important feature of this method is that its CPU time is nearly invariant when monitoring dates increase. Besides, the rate of convergence of presented algorithm was obtained. The numerical results verify the validity and efficiency of the numerical method.
Analysis of Realized Volatility for Nikkei Stock Average on the Tokyo Stock Exchange abstract We calculate realized volatility of the Nikkei Stock Average (Nikkei225) Index on the Tokyo Stock Exchange and investigate the return dynamics. To avoid the bias on the realized volatility from the non-trading hours issue we calculate realized volatility separately in the two trading sessions, i.e. morning and afternoon, of the Tokyo Stock Exchange and find that the microstructure noise decreases the realized volatility at small sampling frequency. Using realized volatility as a proxy of the integrated volatility we standardize returns in the morning and afternoon sessions and investigate the normality of the standardized returns by calculating variance, kurtosis and 6th moment. We find that variance, kurtosis and 6th moment are consistent with those of the standard normal distribution, which indicates that the return dynamics of the Nikkei Stock Average are well described by a Gaussian random process with time-varying volatility.
Microstructure under the Microscope: Tools to Survive and Thrive in The Age of (Too Much) Information abstract Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between observed variables, such as price, volume and spread, and hidden constituents, such as transaction costs and volatility, that hold sway over the efficient functioning of the system.
"My dear, here we must process as much data as we can, just to stay in business. And if you wish to make a profit you must process at least twice as much data." - Red Queen to Alice in Hedge-Fund-Land.
In this age of (Too Much) Information, it is imperative to uncover nuggets of knowledge (signal) from buckets of nonsense (noise). To aid in this effort to extract meaning from chaos and to gain a better understanding of the relationships between financial variables, we summarize the application of the theoretical results from (Kashyap 2016b) to microstructure studies. The central concept rests on a novel methodology based on the marriage between the Bhattacharyya distance, a measure of similarity across distributions, and the Johnson Lindenstrauss Lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. We provide an empirical illustration using prices, volumes and volatilities across seven countries and three different continents. The degree to which different markets or sub groups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors looking for diversification or looking for more of the same thing.
Towards a probability-free theory of continuous martingales abstract Without probability theory, we define classes of supermartingales, martingales, and semimartingales in idealized financial markets with continuous price paths. This allows us to establish probability-free versions of a number of standard results in martingale theory, including the Dubins-Schwarz theorem, the Girsanov theorem, and results concerning the It\^o integral. We also establish the existence of an equity premium and a CAPM relationship in this probability-free setting.
Asset Return & Camel Process: Beauty and the Beast
Modelling Foreign Exchange Rate Transaction Exposure of UK Insurance Companies: A Cash Flow-Based Methodology
The Impacts of Interest Rate on Stock Market: Empirical Evidence from Dhaka Stock Exchange
Mean-Variance Versus Naïve Diversification: The Role of Mispricing
The Relationships between Exchange Rates and Stock Prices: EmpiricalInvestigation from Johannesburg Stock Exchange
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