<<
>>

CONCLUSION

In this chapter, we have identified some stylized facts of FX market traders’ behaviour mainly in terms of the seasonalities of their trading activ­ity. We have built agent-based models of traders’ behaviour in which their collective behaviour resembles the collective behaviour of FX market traders.

We focus on the important elements, which are able to reproduce the stylized facts observed in real market traders’ behaviour, and discuss under which conditions their stylized facts have emerged. This has been addressed in a systematic way to permit an interpretation of the original stylized facts. Simplicity is very important in order to avoid a complexity that could prevent us from identifying the causes behind the emergence of stylized facts. We consider that exploring the high-frequency data of individual traders’ histori­cal transactions in the market is the main factor when it comes to achieving realistic modelling of FX market traders’ behaviour. Additionally, we consider heterogeneity in modelling the traders’ behaviour compulsory. We model heterogeneity in different forms, including traders’ initial wealth, profit objectives, risk appetite, the generation of market and limit orders, trading time windows, etc. The different forms of heterogeneity have different effects on the generation of the stylized facts of FX market traders’ behaviour. In future works, the model can then be modified in many ways in order to consider more realistic situations for the description of the FX market traders’ be­haviour. These more realistic situations will also be addressed in a systematic way.

ACKNOWLEDGMENT

The authors would like to thank Olsen Ltd. for providing the FX market data and wish to thank Dr. Alex Dupuis for helpful discussions on the workings of the FX market.

REFERENCES

Aguirre, M., & Said, R. (1999). Feedback trading in exchange-rate markets: Evidence from within and across economic blocks.

Journal ofEconomics and Finance, 23, 1-14. doi:10.1007/BF02752681 Alfarano, S., & Lux, T. (2003). A minimal noise trader model with realistic time series proper­ties. Economics Working Papers. Kiel, Germany: Christian-Albrechts-University of Kiel.

Alfarano, S., Lux, T., & Wagner, F. (2005). Estima­tion of agent-based models: The case of an asym­metric herding model. Computational Economics, 26(1), 19-49. doi:10.1007/s10614-005-6415-1

Alfi, V., Cristelli, M., Pietronero, L., & Zaccaria, A. (2009a). Minimal agent based model for financial markets I: Origin and self-organization of stylized facts. The European Physical Journal B, 67(3), 385-397. doi:10.1140/epjb/e2009-00028-4

Alfi, V., Cristelli, M., Pietronero, L., & Zacca- ria, A. (2009b). Minimal agent based model for financial markets II: Statistical properties of the linear and multiplicative dynamics. The European Physical Journal B, 67(3), 399-417. doi:10.1140/ epjb/e2009-00029-3

Almeida, A., Goodhart, C., & Payne, R. (1998). The effects of macroeconomic news on high frequency exchange rate behavior. Journal of Fi­nancial and Quantitative Analysis, 33, 383-408. doi:10.2307/2331101

Aloud, M., Olsen, R., & Tsang, E. (2010). Defini­tions of directional- Change events. In Proceed­ings of the 2nd Computer Science and Electronic Engineering Conference. Colchester, UK: ACM.

Andersen, T., & Bollerslev, T. (2003). Mi­cro effects of macro announcements: Real­time price discovery in foreign exchange. The American Economic Review, 93, 38-62. doi:10.1257/000282803321455151

Andersen, T. G. (2000). Some reflections on analysis of high-frequency data. Journal of Busi­ness & Economic Statistics, 18(2), 146-153. doi:10.2307/1392552

Arifovic, J. (1996). The behavior of the exchange rate in the genetic algorithm and experimental economics. The Journal of Political Economy, 104(3), 510-541. doi:10.1086/262032

Arrow, K. J. (2004). The changing face of eco­nomics. In Conversations with Cutting Edge Economists (p.

301). Ann Arbor, MI: University of Michigan Press.

Arthur, W. B., Holland, J., LeBaron, B., Palmer, R., & Tayler, P. (1997). Asset pricing under endog­enous expectations in an artificial stock market. In W. B. Arthur, S. Durlauf, & D. Lane (Eds.), The Economy as an Evolving Complex System II (pp. 15-44). Reading, MA: Addison-Wesley. doi:10.2139/ssrn.2252

Atkinson, A., & Harrison, A. (1978). Distribu­tion of total wealth in Britain. Cambridge, UK: Cambridge University Press.

Bak, P., Paczuski, M., & Shubik, M. (1997). Price variations in a stock market with many agents. Physica A, 246, 430-453. doi:10.1016/S0378- 4371(97)00401-9

Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: The common stock investment performance of individual inves­tors. The Journal of Finance, 55, 773-806. doi:10.1111/0022-1082.00226

Barberis, N., & Thaler, R. (2003). A survey of behavioral finance. NBER Working Paper No. 9222. Washington, DC: NBER.

Berger, D., Chaboud, A., Chernenko, S., Howorka, E., & Wright, J. (2006). Order flow and exchange rate dynamics in electronic brokerage system data. Board of Governors of the Federal Reserve System, International Finance Discussion Papers No. 830. Washington, DC: Federal Reserve.

Biais, B., Martimort, D., & Rochet, J. (2000). Competing mechanisms in a common value en­vironment. Econometrica. Econometric Society, 68(4), 799-838. doi:10.1111/1468-0262.00138

Bj0nnes, G. H., & Rime, D. (2005). Dealer be­havior and trading systems in foreign exchange markets. Journal of Financial Economics, 75, 571-605. doi:10.1016/j.jfineco.2004.08.001

Blume, M., & Goldstein, M. (1997). Quotes, order flow, and price discovery. Journal of Finance. American Finance Association, 52(1), 221-244.

Bondarenko, O. (2001). Competing market mak­ers, liquidity provision, and bid-ask spreads. Journal of Financial Markets, 4(3), 269-308. doi:10.1016/S1386-4181(01)00014-3

Brownlees, C., & Gallo, G.

(2006). Financial econometric analysis at ultra-high frequency: Data handling concerns. Computational Statistics & Data Analysis, 51(4), 2232-2245. doi:10.1016/j. csda.2006.09.030

Chan, N. T., LeBaron, B., Lo, A. W., & Poggio, T. (1999). Agent-based models of financial markets: A comparison with experimental markets. MIT Artificial Markets Project. Cambridge, MA: MIT.

Chang, Y., & Taylor, S. (2003). Information arriv­als and intraday exchange rate volatility. Journal of International Financial Markets, Institutions and Money, 13(2), 85-112. doi:10.1016/S1042- 4431(02)00039-2

Chen, S.-H., & Yeh, C.-H. (2001). Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics & Control, 25(3-4), 363-393. doi:10.1016/S0165- 1889(00)00030-0

Chen, S.-H., & Yeh, C.-H. (2002). On the emergent properties of artificial stock markets: The efficient market hypothesis and the rational expectations hypothesis. Journal of Economic Behavior & Organization, 49(2), 217-239. doi:10.1016/ S0167-2681(02)00068-9

Cincotti, S., Ponta, L., & Raberto, M. (2005). A multi-assets artifcial stock market with zero­intelligence traders. Paper presented at WEHIA. Essex, UK.

Cliff, D. (2009). ZIP60: Further explorations in the evolutionary design of trader agents and online auction-market mechanisms. IEEE Transactions on Evolutionary Computation, 13(1), 3-18. doi:10.1109/TEVC.2008.907594

Cont, R., & Bouchaud, J. P. (2000). Herd behavior and aggregate fluctuations in financial markets. Macroeconomic Dynamics, 4(2), 170-196. doi:10.1017/S1365100500015029

Cristelli, M., Pietronero, L., & Zaccaria, A. (2011). Critical overview of agent-based models for eco­nomics. Quantitative Finance Papers 1101.1847. Retrieved from http://www.arXiv.org

Dacorogna, M., Gencay, R., Muller, U., Olsen, R., & Pictet, O. (2001). An introduction to high frequency finance.

London, UK: Academic Press.

Daniel, G. (2006). Asynchronous simulations of a limit order book. (PhD Thesis). Manchester University. Manchester, UK.

Edmonds, B. (1999). Modelling bounded ra­tionality in agent-based simulations using the evolution of mental models. In T. Brenner (Ed.), Compuational Techniques for Modelling Learn­ing in Economics (pp. 305-332). Dordrecht, The Netherlands: Kluwer. doi:10.1007/978-1-4615- 5029-7_13

Engle, R. (2000). The econometrics of ultra- high frequency data. Econometrica: Journal of the Econometric Society, 68(1), 1-22. doi:10.1111/1468-0262.00091

Evans, M., & Lyons, R. K. (2002a). Order flow and exchange rate dynamics. The Journal of Political Economy, 110, 170-180. doi:10.1086/324391

Evans, M., & Lyons, R. K. (2002b). Information in­tegration and FX trading. Journal of International Money and Finance, 21, 807-831. doi:10.1016/ S0261-5606(02)00024-4

Evans, M., & Lyons, R. K. (2004a). Exchange rate fundamentals and order flow. NBER Work­ing Paper No. 13151. Washington, DC: NBER.

Evans, M., & Lyons, R. K. (2004b). Do cur­rency markets absorb news quickly? Journal of International Money and Finance, 24, 197-217. doi:10.1016/j.jimonfin.2004.12.004

Falkenberry, T. (2002). High frequency data fil­tering. Technical Report. Retrieved from http:// www.tickdata.com

Farmer, J. (1998). Market force, ecology, and evo­lution. Industrial and Corporate Change, 11(5), 895-953. doi:10.1093/icc/11.5.895

Farmer, J., & Joshi, S. (2002). The price dynamics of common trading strategies. Journal of Eco­nomic Behavior & Organization, 49(2), 149-171. doi:10.1016/S0167-2681(02)00065-3

Frankel, J. A., & Froot, K. A. (1990a). Chartists, fundamentalists, and trading in the foreign ex­change market. The American Economic Review, 80, 181-185.

Frankel, J. A., & Froot, K. A. (1990b). Exchange rate forecasting techniques, survey: Data, and implications for the foreign exchange market. Working Paper WP/90.

Washington, DC: Inter­national Monetary Fund.

Ghysels, E. (2000). Some econometric recipes for high-frequency data cooking. Journal of Business & Economic Statistics, 18(2), 154-163. doi:10.2307/1392553

Gilli, M., & Winker, P. (2003). A global optimiza­tion heuristic for estimating agent based models. Computational Statistics & Data Analysis, 42(3), 299-312. doi:10.1016/S0167-9473(02)00214-1

Glaser, M., & Weber, M. (2007). Overconfidence and trading volume. The Geneva Risk and Insur­ance Review, 32, 1-36. doi:10.1007/s10713-007- 0003-3

Gode, D. K., & Sunder, S. (1991). Allocative efficiency of markets with zero intelligence (Z1) traders: Market as a partial substitute for indi­vidual rationality. GSIA Working Papers 1992­16. Pittsburgh, PA: Carnegie Mellon University.

Hott, C. (2009). Herding behavior in asset mar­kets. Journal of Financial Stability, 5(1), 35-56. doi:10.1016/j.jfs.2008.01.004

Ito, T. (1990). Foreign exchange rate expectations: Micro survey data. The American Economic Re­view, 3, 434-449.

Ito, T., & Hashimoto, Y. (2006). Intraday sea­sonality in activities of the foreign exchange markets: Evidence from the electronic broking system. Journal of the Japanese and Interna­tional Economies, 20(4), 637-664. doi:10.1016/j. jjie.2006.06.005

Kim, K., Yoon, S., & Kim, Y. (2004). Herd be­haviors in the stock and foreign exchange markets. Physica A: Statistical Mechanics and its Applica­tions, 341, 526-532.

Kirman, A. (1993). Ants, rationality, and re­cruitment. The Quarterly Journal of Economics, 108(1), 137-156. doi:10.2307/2118498

Kyle, A. S. (1984). Market structure, informa­tion, futures markets, and price formation. In G. G. Storey, A. Schmitz, & A. H. Harris (Eds.), International Agricultural Trade: Advanced Readings in Price Formation, Market Structure, and Price Instability (pp. 45-64). Boulder, CO: Westview Press.

Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Economet­ric Society, 53, 1315-1335. doi:10.2307/1913210

Laopodis, N. T. (2005). Feedback trading and au­tocorrelation interactions in the foreign exchange market: Further evidence. Economic Modelling, 22, 811-827. doi:10.1016/j.econmod.2005.05.008

Lebaron, B. (2001a). A builder’s guide to agent based financial markets. Quantitative Finance, 1(2), 254-261. doi:10.1088/1469-7688/1/2/307

LeBaron, B. (2001b). Evolution and time ho­rizons in an agent-based stock market. Macro­economic Dynamics, 5, 225-254. doi:10.1017/ S1365100501019058

Lebaron, B. (2001c). Empirical regularities from interacting long- and short-memory investors in an agent-based stock market. IEEE Transactions on Evolutionary Computation, 5(5), 442-455. doi:10.1109/4235.956709

LeBaron, B. (2003). Calibrating an agent-based financial market. Working Paper. Boston, MA: Brandeis University.

Lebaron, B. (2006a). Agent-based computational finance. In K. Judd, & L. Tesfatsion (Eds.), Hand­book of Computational Economics (Vol. 2, pp. 1187-1233). London, UK: Elsevier. doi:10.1016/ S1574-0021(05)02024-1

LeBaron, B. (2006b). Agent-based financial markets: Matching stylized facts with style. In D. C. Coler (Ed.), Post Walrasian Macroeco­nomics: Beyond the Dynamic Stochastic General Equilibrium Model (pp. 221-238). Cambridge, UK: Cambridge University Press. doi:10.1017/ CBO9780511617751.013

Levy, M., Levy, H., & Solomon, S. (1994). A microscopic model of the stock market: Cycles, booms and crashes. Economics Letters, 45(1), 103-111. doi:10.1016/0165-1765(94)90065-5

Lux, T. (1995). Herd behaviour, bubbles and crashes. The Economic Journal, 705(431), 881-896. doi:10.2307/2235156

Lux, T. (1998). The socio-economic dynamics of speculative markets: Interacting agents, chaos, and the fat tails of return distributions. Journal of Economic Behavior & Organization, 33(2), 143-165. doi:10.1016/S0167-2681(97)00088-7

MacDonald, R., & Marsh, I. W. (1996). Currency forecasters are heterogeneous: Confirmation and consequences. Journal of International Money and Finance, 15, 665-685. doi:10.1016/0261- 5606(96)00030-7

Markose, S., Tsang, E., & Martinez, S. (2003). The red queen principle and the emergence of efficient financial markets: An agent based approach. In

T. Lux, S. Reitz, & E. Samanodou (Eds.), Non­linear Dynamics and Heterogeneous Interacting Agents (pp. 287-303). Berlin, Germany: Springer. doi:10.1007/3-540-27296-8_19

Martinez-Jaramillo, S., & Tsang, E. P. (2009). An heterogeneous, endogenous and co-evolutionary GP-based financial market. IEEE Transactions on Evolutionary Computation, 13(1), 33-55. doi:10.1109/TEVC.2008.2011401

Masry, S., Aloud, M., Dupuis, A., Olsen, R., & Tsang, E. K. (2010). High frequency FOREX market transaction data handling. Paper presented at the 4th CSDA International Conference on Computational and Financial Econometrics. London, UK.

Menkhoff, L., Rebitzky, R., & Schroder, M. (2009). Heterogeneity in exchange rate expectations: Evi­dence on the chartist-fundamentalist approach. Journal of Economic Behavior & Organization, 70, 241-252. doi:10.1016/j.jebo.2009.01.007

Nishide, K. (2006). Insider trading with im­perfectly competitive market makers. Technical Report. Kyoto, Japan: Kyoto University.

O’Connelle, P., & Teo, M. (2009). Institutional investors, past performance, and dynamic loss aversion. Journal of Financial and Quanti­tative Analysis, 44, 155-188. doi:10.1017/ S0022109009090048

Oberlechner, T. (2001). Evaluation of curren­cies in the foreign exchange market: Attitudes and expectations of foreign exchange traders. Zeitschriftfur Sozialpsychologie, 32(3), 180-188. doi:10.1024//0044-3514.32.3.180

Oberlechner, T., & Osler, C. (2009). Overconfi­dence in currency markets. Working Paper 02. Boston, MA: Brandeis University.

Oomen, R. C. A. (2006). Properties of realized variance under alternative sampling schemes. Journal of Business & Economic Statistics, 24, 219-237. doi:10.1198/073500106000000044

Palmer, R. G., Arthur, W. B., Holland, J. H., LeBaron, B., & Tayler, P. (1994). Artificial eco­nomic life: A simple model of a stock market. Physica D. Nonlinear Phenomena, 75, 264-274. doi:10.1016/0167-2789(94)90287-9

Pareto, V. (1897). Cours d’economie politique. Paris, France: Academic Press.

Persky, J. (1992). Retrospectives: Pareto’s law. The Journal of Economic Perspectives, 6, 181-192.

Russell, J. (1999). Econometric modeling of mul­tivariate irregularly-spaced highfrequency data. Chicago, IL: University of Chicago.

Samanidou, E., Zschischang, E., Stauffer, D., & Lux, T. (2007). Agent-based models of financial markets. Reports on Progress in Physics, 70, 409-450. doi:10.1088/0034-4885/70/3/R03

Shleifer, A. (2000). Inefficient markets: An in­troduction to behavioral finance. Oxford, UK: Oxford University Press.

Steindl, J. (1965). Random processes and the growth of firms - A study of the Pareto law. Lon­don, UK: Charles Griffin and Company.

Subrahmanyam, A. (2007). Behavioural finance: A review and synthesis. European Financial Management, 14, 12-29.

Sunder, S. (2004). Market as an artifact aggregate efficiency from zero intelligence traders. In M. E. Augier, & J. G. March (Eds.), Models of a Man: Essays in memory of Herbert A. Simon (pp. 501-519). Cambridge, MA: MIT Press.

Winker, P., & Gilli, M. (2001). Indirect estima­tion of the parameters of agent based models of financial markets. Fame Research Paper. Geneva, Switzerland: University of Geneva.

Yan, B., & Zivot, E. (2003). Analysis of high- frequency financial data with S-PLUS. Working Papers UWEC-2005-03. Seattle, WA: University of Washington.

Yang, J. (2002). The efficiency of an artificial double auction stock market with neural learning agents. In Evolutionary Computation in Econom­ics and Finance, (pp. 85-106). Retrieved from http://www.essex.ac.uk/ccfea/news%20and%20 events%20holding%20folder/seminarsetc/semi- nars/archive/past/doubleauctionsimulation.pdf Yeh, C.-H., & Chen, S.-H. (2000). Toward an integration of social learning and individual learning in agent-based computational stock mar­kets: The approach based on population genetic programming. In Computing in Economics and Finance. Retrieved from http://fmwww.bc.edu/ cef00/papers/paper338.pdf

Zimmermann, H., Neuneier, R., & Grothmann, R. (2001a). An approach of multi-agent FX- market modelling based on cognitive systems. In Proceedings of the International Conference on Artificial Neural Networks (ICANN). Vienna, Austria: ICANN.

Zimmermann, H., Neuneier, R., & Grothmann, R. (2001b). Multi-agent modeling of multiple FX-markets by neural networks. IEEE Trans­actions on Neural Networks, 12(4), 735-743. doi:10.1109/72.935087 PMID:18249909

ADDITIONAL READING

Alexander, C. (2001). Market models: A guide to financial data analysis. Chichester, UK: Wiley.

Aoki, M. (2001). On dynamic re-specifications of Kiyotaki-Wright model. In A. Kirman, & J.

B. Zimmermann (Eds.), Economics with Het­erogeneous Interacting Agents (pp. 109-120). Berlin, Germany: Springer. doi:10.1007/978-3- 642-56472-7_8

Aoki, M. (2002). Modeling aggregate behavior and fluctuations in economics. Cambridge, UK: Cambridge University Press.

Ariel, R. A. (1987). A monthly effect in stock returns. Journal of Financial Economics, 18(1), 161-174. doi:10.1016/0304-405X(87)90066-3

Ariel, R. A. (1990). High stock returns before holidays: Existence and evidence on possible causes. The Journal of Finance, 45(5), 1611-1626. Arifovic, J., & Masson, P. (2000). Heterogene­ity and evolution of expectations in a model of currency crisis. Nonlinear Dynamics Psychology and Life Sciences, 8, 231-257. PMID:15068737

Aronson, D. R. (2006). Evidence-based techni­cal analysis: Applying the scientific method and statistical inference to trading signals. Hoboken, NJ: Wiley. doi:10.1002/9781118268315

Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797-817. doi:10.2307/2118364

Beechey, M., Gruen, D., & Vickery, J. (2000). The efficient market hypothesis: A survey. Research Discussion Paper. Academic Press.

Biham, O., Huang, Z. F., Malcai, O., & Solomon, S. (2001). Long-time )uctuations in a dynamical model of stock market indices. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 64, 026101. doi:10.1103/PhysRevE.64.026101

Challet, D., Marsili, M., & Zhang, Y.-C. (2005). Minority game: Interacting agents in financial markets. Oxford, UK: Oxford University Press.

Cheung, Y.-W. (1993). Long memory in foreign- exchange rates. Journal of Business & Economic Statistics, 11(1), 93-101. doi:10.2307/1391309

Chiarella, C., Dieci, R., & Gardini, L. (2002). Speculative behaviour and complex asset price dynamics. Journal of Economic Behavior & Organization, 49, 173-197. doi:10.1016/S0167- 2681(02)00066-5

Cincotti, S., Focardi, S., Marchesi, M., & Raberto, M. (2001). Agent-based simulation of a financial market. Physica A, 299, 319-327. doi:10.1016/ S0378-4371(01)00312-0

Eisler, Z., & Kert’esz, J. (2006). Size matters: Some stylized facts of the market revisited. The European Physical Journal B, 51, 145-154. doi:10.1140/epjb/e2006-00189-6

Georges, C. (2006). Learning with misspecifica­tion in an artificial currency market. Journal of Economic Behavior & Organization, 60, 70-84. doi:10.1016/j.jebo.2004.08.005

Giardina, I., & Bouchaud, J. P. (2003). Bubbles, crashes and intermittency in agent based market models. The European Physical Journal B, 31, 421-437. doi:10.1140/epjb/e2003-00050-6

Goonatilake, S., & Treleaven, P. (Eds.). (1995). Intelligent systems for finance and business. Chichester, UK: Wiley.

Grinblatt, M., & Keloharju, M. (2001). What makes investors trade? The Journal of Finance, 56(2), 589-616. doi:10.1111/0022-1082.00338

Guillaume, D. M., Dacorogna, M. M., Dav’e, R. R., M''uuller, U. A., Olsen, R. B., & Pictet, O. V. (1997). From the bird’s eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets. Finance and Stochas­tics, 1(2), 95-129. doi:10.1007/s007800050018

Hommes, C. H. (2002). Modeling the stylized facts in finance through simple nonlinear adaptive systems. In Proceedings of PNAS, (pp. 7221­7228). PNAS.

LeBaron, B. (2006). Time scales, agents, and empirical finance. In Medium Econometrische To­epassingen (MET). Rotterdam, The Netherlands: Erasmus University.

Lux, T., & Marchesi, M. (2000). Volatility clus­tering in financial markets: A micro simulation of interacting agents. International Journal of Theoretical and Applied Finance, 3, 675-702. doi:10.1142/S0219024900000826

Moody, J., & Wu, L. (1995). Price behavior and hurst exponents of tick-by-tick interbank foreign exchange rates. In Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering, (pp. 26-30). IEEE Press.

Sewell, M. V., & Yan, W. (2008). Ultra high frequency financial data. In M. Keijzer (Ed.), Proceedings of the 2008 GECCO Conference Companion on Genetic and Evolutionary Com­putation, (pp. 1847-1850). New York, NY: ACM Press.

Takayasu, M., Mizuno, T., & Takayasu, H. (2006). Potentials force observed in market dy­namics. Physica A, 370, 91-97. doi:10.1016/j. physa.2006.04.041

Vaglica, G., Lillo, F., Moro, E., & Mantegna, N. (2008). Scaling laws of strategic behaviour and size heterogeneity in agent dynamics. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 77, 036110. doi:10.1103/PhysRevE.77.036110

Zhou, B. (1996). High-frequency data and volatility in foreign-exchange rates. Journal of Business & Economic Statistics, 14(1), 45-52. doi:10.2307/1392098

KEY TERMS AND DEFINITIONS

Leverage: Also called margin, this is the ratio of margin to the maximum transaction size. For example, with a deposit of $1,000 and a leverage of 20:1, a trader could enter a position value of $20,000.

Long Position: A position is said to be long when the base currency in the currency pair is bought (long) while the quote currency is sold (short).

Market-Maker: In the FX market, a market­maker is a dealer-broker firm which quotes both a bid and an offer price in a given currency pair to the market, in order to make a profit through the bid/ask spread. In addition, market-maker is equipped to buy from, and sell to, investors and other market-makers at those quoted bid and ask prices.

Net Asset Value (NAV): A trader portfolio is defined by the Net Asset Value (NAV). The NAV at time t is the amount of cash in the trading agent’s account, plus/minus all unrealized gains/losses associated with all the account’s open positions.

Short Position: A position is said to be short when the base currency in the currency pair is sold (short) while the quote currency is bought (long).

Unit: In the FX market, a unit is used as a quantity of currency. For example: one unit of EUR is one Euro, and one unit of USD is one United States dollar.

Unrealized Gains/Losses: These are the profits/losses that would be produced if an open position were closed at the current currency pair exchange rate.

This work was previously published in Simulation in Computational Finance and Economics, edited by Biliana Alexandrova- Kabadjova, Serafin Martinez-Jaramillo, Alma Lilia Garcia-Almanza, and Edward Tsang, pages 303-338, copyright 2013 by Business Science Reference (an imprint of IGI Global).

<< | >>
Source: Banking, Finance, and Accounting: Concepts, Methodologies, Tools, and Applications. IGI Global,2014. — 1593 p.. 2014
More financial literature on Economics.Studio

More on the topic CONCLUSION: