This paper addresses the little investigated topic of the relationship between the speed of exchange servers, an absolute reference for the system, and trading speed, considered relative to the former. This is a major issue, as trading speed overwhelming the capability of the server to cope with the incoming orders might jeopardise the orderly functioning of the markets. It will be shown how, by raising the speed of trading and increasing the number of the agents operating in the market, it is possible to generate a crisis, no matter how performing the exchange server is. The paper presents a theoretical framework and then verifies its occurrence by analysing audit trail data. The theoretical framework shows a scenario in which under certain, heavy but by no means uncommon, conditions, the excess speed of the trading agents with respect to servers is capable of exacerbating price volatility, leading to vicious feedback loops capable of potentially creating a financial crisis. The empirical part analyses data taken from a particularly volatile day and compares them with much less volatile days. It results that, because of excessive speed, one of the most widely used techniques for minimising risk, order churning, can cause a major crisis.
{"title":"Absolute vs. relative speed in high-frequency trading","authors":"G. Virgilio","doi":"10.3233/AF-180253","DOIUrl":"https://doi.org/10.3233/AF-180253","url":null,"abstract":"This paper addresses the little investigated topic of the relationship between the speed of exchange servers, an absolute reference for the system, and trading speed, considered relative to the former. This is a major issue, as trading speed overwhelming the capability of the server to cope with the incoming orders might jeopardise the orderly functioning of the markets. It will be shown how, by raising the speed of trading and increasing the number of the agents operating in the market, it is possible to generate a crisis, no matter how performing the exchange server is. The paper presents a theoretical framework and then verifies its occurrence by analysing audit trail data. The theoretical framework shows a scenario in which under certain, heavy but by no means uncommon, conditions, the excess speed of the trading agents with respect to servers is capable of exacerbating price volatility, leading to vicious feedback loops capable of potentially creating a financial crisis. The empirical part analyses data taken from a particularly volatile day and compares them with much less volatile days. It results that, because of excessive speed, one of the most widely used techniques for minimising risk, order churning, can cause a major crisis.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"7 1","pages":"71-86"},"PeriodicalIF":0.5,"publicationDate":"2019-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-180253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48102760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Allocation skew: Managers with conviction","authors":"Vikram K. Srimurthy, Matthew Smalbach","doi":"10.3233/AF-180225","DOIUrl":"https://doi.org/10.3233/AF-180225","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"7 1","pages":"63-69"},"PeriodicalIF":0.5,"publicationDate":"2019-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-180225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48351377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Impact of short-sales in stock market efficiency","authors":"B. Llacay, G. Peffer","doi":"10.3233/AF-190215","DOIUrl":"https://doi.org/10.3233/AF-190215","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"8 1","pages":"5-26"},"PeriodicalIF":0.5,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-190215","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69724656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. R. Sipos, A. Ceffer, G. Horváth, J. Levendovszky
{"title":"Parallel MCMC sampling of AR-HMMs for prediction based option trading","authors":"I. R. Sipos, A. Ceffer, G. Horváth, J. Levendovszky","doi":"10.3233/AF-190243","DOIUrl":"https://doi.org/10.3233/AF-190243","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"8 1","pages":"47-55"},"PeriodicalIF":0.5,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-190243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69724737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We provide a non-parametric method for stochastic volatility modelling. Our method allows the implied volatility to be governed by a general Levy-driven Ornstein–Uhlenbeck process, the density function of which is hidden to market participants. Using discrete-time observation we estimate the density function of the stochastic volatility process via developing a cumulant M-estimator for the Levy measure. In contrast to other non-parametric estimators (such as kernel estimators), our estimator is guaranteed to be of the correct type. We implement this method with the aid of a support-reduction algorithm, which is an efficient iterative unconstrained optimisation method. For the empirical analysis, we use discretely observed data from two implied volatility indices, VIX and VDAX. We also present an out-of-sample test to compare the performance of our method with other parametric models.
{"title":"A non-parametric inference for implied volatility governed by a Lévy-driven Ornstein-Uhlenbeck process","authors":"F. A. Fard, Armin Pourkhanali, M. Sy","doi":"10.3233/AF-180200","DOIUrl":"https://doi.org/10.3233/AF-180200","url":null,"abstract":"We provide a non-parametric method for stochastic volatility modelling. Our method allows the implied volatility to be governed by a general Levy-driven Ornstein–Uhlenbeck process, the density function of which is hidden to market participants. Using discrete-time observation we estimate the density function of the stochastic volatility process via developing a cumulant M-estimator for the Levy measure. In contrast to other non-parametric estimators (such as kernel estimators), our estimator is guaranteed to be of the correct type. We implement this method with the aid of a support-reduction algorithm, which is an efficient iterative unconstrained optimisation method. For the empirical analysis, we use discretely observed data from two implied volatility indices, VIX and VDAX. We also present an out-of-sample test to compare the performance of our method with other parametric models.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"7 1","pages":"15-30"},"PeriodicalIF":0.5,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-180200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49435190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An Asian-style futures is settled by an Asian-style settlement procedure, more specifically, it is settled against the arithmetic average of the underlying asset prices taken over the settlement period. In this paper, we propose a practical trading strategy based on an integer programming technique to exploit the mispricing opportunity of Asian-style index futures over the settlement period using a proxy of the underlying asset. The integer program can detect mispricing, construct an arbitrage portfolio by using the proxy and dynamically maintain the arbitrage portfolio. Hang Seng Index Futures (HSI Futures) of the Hong Kong market is used to test the trading strategy. The historical data of HSI Futures shows that there is a positive relationship between the magnitude of mispricing and the time to maturity over the settlement period. Moreover, our empirical findings show positive profitability of the trading strategy.
{"title":"An integer programming based strategy for Asian-style futures arbitrage over the settlement period","authors":"Raymond H. Chan, Kelvin K. Kan, A. Ma","doi":"10.3233/AF-180219","DOIUrl":"https://doi.org/10.3233/AF-180219","url":null,"abstract":"An Asian-style futures is settled by an Asian-style settlement procedure, more specifically, it is settled against the arithmetic average of the underlying asset prices taken over the settlement period. In this paper, we propose a practical trading strategy based on an integer programming technique to exploit the mispricing opportunity of Asian-style index futures over the settlement period using a proxy of the underlying asset. The integer program can detect mispricing, construct an arbitrage portfolio by using the proxy and dynamically maintain the arbitrage portfolio. Hang Seng Index Futures (HSI Futures) of the Hong Kong market is used to test the trading strategy. The historical data of HSI Futures shows that there is a positive relationship between the magnitude of mispricing and the time to maturity over the settlement period. Moreover, our empirical findings show positive profitability of the trading strategy.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"7 1","pages":"31-42"},"PeriodicalIF":0.5,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-180219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45730561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A new variable selection method applied to credit coring","authors":"D. Boughaci, A. Alkhawaldeh","doi":"10.3233/AF-180227","DOIUrl":"https://doi.org/10.3233/AF-180227","url":null,"abstract":"","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"7 1","pages":"43-52"},"PeriodicalIF":0.5,"publicationDate":"2018-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-180227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41999244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the network dynamics of fourteen Asian Stock Markets (ASMs) in three phases (pre, during, and post) of financial crisis of 2008. Based on network statistics, I find that ASMs network is more interconnected during the crisis period than pre-and post-crisis period. Furthermore, using the Minimum Spanning Tree (MST) diagram, I find that the stock markets of Hong Kong, Japan, Korea, and India play a significant role in these networks and any shock to these markets can lead to contagion. The trade and the interest rate differential are the major driving forces behind these linkages. This work has practical implications as it provides insight on portfolio diversification during the crisis period and can also be used in anticipating the route of crisis.
{"title":"Impact of global financial crisis on network of Asian stock markets","authors":"Jitendra Aswani","doi":"10.3233/AF-170192","DOIUrl":"https://doi.org/10.3233/AF-170192","url":null,"abstract":"This study examines the network dynamics of fourteen Asian Stock Markets (ASMs) in three phases (pre, during, and post) of financial crisis of 2008. Based on network statistics, I find that ASMs network is more interconnected during the crisis period than pre-and post-crisis period. Furthermore, using the Minimum Spanning Tree (MST) diagram, I find that the stock markets of Hong Kong, Japan, Korea, and India play a significant role in these networks and any shock to these markets can lead to contagion. The trade and the interest rate differential are the major driving forces behind these linkages. This work has practical implications as it provides insight on portfolio diversification during the crisis period and can also be used in anticipating the route of crisis.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"6 1","pages":"79-91"},"PeriodicalIF":0.5,"publicationDate":"2017-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-170192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44945890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Technical analysis is the art of identifying patterns in historical data with the belief that certain patterns foretell future price movements. An empirical evaluation of the effectiveness of technical analysis is confounded by the subjectivity involved in identifying patterns. This work presents a robust framework for pattern identification using probabilistic neural networks (PNN). The thirty components of the Dow Jones Industrial Average and a set of ten indices are considered. Fourteen patterns are analyzed. In order to test the possibility that technical patterns are more predictable in certain market environments, the period under study (1990–2015) is partitioned into bull and bear markets and the statistical significance of profits earned by identified patterns observed in each environment is analyzed. A range of holding periods from 10 to 50 trading days is considered and a simple model of transaction costs is added. The study reveals that no pattern produces statistically and economically significant profits for a cross-section of stocks and indices analyzed, though a few patterns are more successful predictors. Bullish (bearish) patterns are more reliable predictors in bullish (bearish) market environments. These observations can be explained by the Adaptive Market Hypothesis with certain patterns becoming more accurate predictors in specific market environments.
{"title":"Empirical evaluation of price-based technical patterns using probabilistic neural networks","authors":"Samit Ahlawat","doi":"10.3233/AF-160059","DOIUrl":"https://doi.org/10.3233/AF-160059","url":null,"abstract":"Technical analysis is the art of identifying patterns in historical data with the belief that certain patterns foretell future price movements. An empirical evaluation of the effectiveness of technical analysis is confounded by the subjectivity involved in identifying patterns. This work presents a robust framework for pattern identification using probabilistic neural networks (PNN). The thirty components of the Dow Jones Industrial Average and a set of ten indices are considered. Fourteen patterns are analyzed. In order to test the possibility that technical patterns are more predictable in certain market environments, the period under study (1990–2015) is partitioned into bull and bear markets and the statistical significance of profits earned by identified patterns observed in each environment is analyzed. A range of holding periods from 10 to 50 trading days is considered and a simple model of transaction costs is added. The study reveals that no pattern produces statistically and economically significant profits for a cross-section of stocks and indices analyzed, though a few patterns are more successful predictors. Bullish (bearish) patterns are more reliable predictors in bullish (bearish) market environments. These observations can be explained by the Adaptive Market Hypothesis with certain patterns becoming more accurate predictors in specific market environments.","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"5 1","pages":"49-68"},"PeriodicalIF":0.5,"publicationDate":"2017-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-160059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42761529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：