1,720,994 research outputs found
Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Techniques
No full textAfinancialasset’svolatilityexhibitskeycharacteristics, such as mean-reversion and high autocorrelation [1], [2]. Empirical evidence suggests that this volatility autocorrelation exponentially decays (or exhibits long-range memory) [3]. We employ Genetic Programming (GP) for volatility forecasting because of its ability to detect patterns such as the conditional mean and conditional variance of a time-series. Genetic Programming is typically applied to optimisation, searching, and machine learning applications like classification, prediction etc. From our experiments, we see that Genetic Programming is a good competitor to the standard forecasting techniques like GARCH(1,1), Moving Average (MA), Exponentially Weighted Moving Average (EWMA). However it is not a silver bullet: we observe that different forecasting methods would perform better in different market conditions. In addition to Genetic Programming, we consider a heuristic technique that employs a series of standard forecasting methods and dynamically opts for the most appropriate technique at a given time. Using a heuristic technique, we try to identify the best forecasting method that would perform better than the rest of the methods in the near out-of-sample horizon. Our work introduces a preliminary framework for forecasting 5-day annualised volatility in GBP/USD, USD/JPY, and EUR/USD
Alphas in disguise: A new approach to uncovering them
Get PDFFama-French (Carhart) alphas of passive indices should be zero, but recent evidence shows otherwise. Inaccuracies of factors in the performance measurement models have been put forward as the main reason for this. Some computationally intensive solutions to factor adjustment have been proposed, but are not applicable to all benchmark indices. We propose an optimisation algorithm that makes minor adjustments to the market, size, value and momentum factors to obtain zero alphas for any benchmark index. In the sample of 1281 active and 102 passive US equity mutual funds benchmarking against S&P500, our adjustment leads to augmentation of fund performance upwards in periods of index underperformance and downwards in periods of index outperformance. Overall, the adjusted alphas of both groups of funds are significantly negative, signalling poor performance. This is particularly pronounced for tracker funds, whose managers have not been successful in enhancing returns adequately to make-up for the costs involved in any of the sub-periods examined
Special issue on algorithms in computational finance
No full textAlgorithms play an important part in finance [...
International trade-network and stock-market connectedness: evidence from eleven major economies
Get PDFDepth of cross-country international trade engagement is an important source of (the strength of) stock-market connectedness, depicting how directional attributes of trade determine the magnitude of spillover of stock returns across economies. We premise and test this hypothesis for a group of eleven major economies during 2000m1-2021m6 using both system-wide and directional evidence. We exploit the input-output network of Bilgin and Yilmaz (2018) to construct a trade-network, and use Diebold and Yilmaz’s (2009, 2012, 2014) Connectedness Index to proxy for stock-market connectedness among economies. We reveal China’s instrumental role in the trade-network and its rising influence in stock markets dominated by the US. Motivated by the fact that shocks on an economy’s imports and exports may lead to different magnitude of stock market spillover to its trade partner, we further carry out a pairwise directional level investigation. Once the directional dimensions of both the trade flows and the stock market influences are considered, we find that an economy’s stock return spillover to its trade partner is generated from its position as an importer and exporter. More importantly, being an importer is found to be a stronger source of such spillover than being an exporter
Intraday industry-specific spillover effect in European equity markets
Get PDFThis paper investigates the existence of financial contagion between the US and ten European stock markets. Using intraday minute-per-minute data of a large set of 374 equities from three different industries, over the period from January to June 2011, we investigate the impact of increased volatility in the US on the inter-country industry-level spillover effect. Self-built industry indices are used, which allows the implementation of the same index methodology across different markets. We first show that the spillover of asset price volatility from the US to European markets does exist; the greatest spike in the volatility in the target markets is observed in the first minute, and is absorbed in the first five minutes after the volatility increase. Second, we can state that euro-denominated markets amplify the spillover effect of volatility from the US market. Third, we provide evidence of the industry heterogeneity of the spillover effects, and claim that an analysis of financial contagion across different industries is desirable, using industry indices instead of global market indices
On the Laplace Transforms of the First Hitting Times for Drawdowns and Drawups of Diffusion-Type Processes
Get PDFWe obtain closed-form expressions for the value of the joint Laplace transform of therunning maximum and minimum of a diffusion-type process stopped at the first time at which theassociated drawdown or drawup process hits a constant level before an independent exponentialrandom time. It is assumed that the coefficients of the diffusion-type process are regular functionsof the current values of its running maximum and minimum. The proof is based on the solution tothe equivalent inhomogeneous ordinary differential boundary-value problem and the applicationof the normal-reflection conditions for the value function at the edges of the state space of theresulting three-dimensional Markov process. The result is related to the computation of probabilitycharacteristics of the take-profit and stop-loss values of a market trader during a given time period.</jats:p
TSFDC: A Trading strategy based on forecasting directional change
Get PDFDirectional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market’s trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. We argue that TSFDC outperforms another DC-based trading strategy
High frequency trading strategies, market fragility and price spikes: an agent based model perspective
Get PDFGiven recent requirements for ensuring the robustness of algorithmic trading strategies laid out in the Markets in Financial Instruments Directive II (MiFID II), this paper proposes a novel agent-based simulation for exploring algorithmic trading strategies. Five different types of agents are present in the market. The statistical properties of the simulated market are compared with equity market depth data from the Chi-X exchange and found to be significantly similar. The model is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The results are found to be insensitive to reasonable parameter variations
Stock-ADR Arbitrage: Microstructure Risk
Get PDFThis paper is the first to highlight that the stock-ADR arbitrage pair trading found by Alsayed and McGroarty (2012) is directly influenced by the market microstructure of ADRs. In Alsayed and McGroarty (2012) they are the first to demonstrate that arbitrage opportunities exist between stocks and their ADRs, through convergence pairs trading. Given that such arbitrage opportunities exist, we pose the question as to why such pair trades occur, rather than be eliminated by the law of one price? Using high frequency data over a 3 year sample period, with over 3.7 million 1-min observations, we investigate stock-ADR arbitrage pair trading. In this paper, we find pair trading returns exhibit substantial asymmetry in returns: pair trades involving ADR shorts (compared to stock shorts) have significantly less probability of loss, substantially higher returns but higher convergence risk. The asymmetric results are consistent with the market microstructure of ADR trading, specifically the sourcing of ADRs. Whilst long and short stocks can be easily sourced from the relevant markets, long and short ADR sourcing is less viable due to the market microstructure, but also, ADR's microstructure directly impacts the stock's price. We test our microstructure hypothesis further for robustness, with respect to specific investor types (such as retail traders), as well as during different market conditions (before, during and after the commencement of the global financial crisis), and find our results are consistent with our ADR microstructure hypothesis. This is also supported by CFD (contracts for difference) and ADR pairs trading results. Our results also confirm the results of Alsayed and McGroarty (2012) by conducting trades over a substantially longer and more varied trading period. Our results have implications for ADR markets, as well as market microstructures upon financial innovations such as exchange traded funds
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