Speculative trading stems from disagreements among traders. Besides the approaches based on the existence of private information (and noise traders) or the dierences of opinions, Harrison and Kreps(1978) and Morris(1996) relied on the presence of diverse beliefs to explain speculative phenomena. This paper proposes a new model of speculative trading by introducing rational beliefs of Kurz(1994) and Kurz and Wu(1996). Agents hold diverse beliefs which are rational” in the sense of being compatible with observed data. In a non-stationary environment the agents may learn only about the stationary measure of observed data. Agents’ beliefs can be non-stationary and diverse even when their stationary measures become the same as that of the data with complete learning.In a Markovian framework of dividends and beliefs, we obtainanalytical results on how the speculative premium depends on the extent of heterogeneity of beliefs. In addition, we demonstrate the possible emergence of endogenous uncertainty (as dened by Kurz and Wu(1996)) and the persistent presence of diverse beliefs and positive speculative premiums.
Often described as home bias, an enduring feature of strategic asset allocations in Australia and abroad is a relatively high weight to domestic assets. This paper analyses whether a home bias to Australian equities can be justified, and concludes that, on the basis of evidence from historic outcomes, investors with very long investment horizons should have held most if not all of those equities in global portfolio decision would have disappointed over many short and even medium-term periods. On balance, on the evidence presented in this paper, it would appear prudent to lean towards inversting at least 50% and perhaps up to 60%-70% of a portfolio’s total equity exposure in international equities.
By Winsor A.G.A. Hoang
Forex Trading has been marketed to the average person as his voucher to financial independence and unlimited wealth. With the introduction of so-called veteran trading chat rooms, real time market data, instantaneous trade execution, and trading on the news, there is an enormous amount of information feeding to your home PC or laptop by the internet. The Forex trading claims to provide full time self employment opportunities with a gargantuan payday everyday. This is a dream come true for want-to-be traders.
By Abe Cofnas
Adifficult challenge facing a trader, and particularly those trading e-forex,is inding perspective. Achieving that in markets with regular hours is hard enough, but with forex, where prices are moving 24 hours a day, seven days a week, it is exceptionally laborious..
We reconsider the problem of option pricing using historical probability distributions. We first discuss how the risk-minimisation scheme proposed recently is an adequate starting point under the realistic assumption that price increments are uncorrelated (but not necessarily independent) and of arbitrary probability density. We discuss in particular how, in the Gaussian limit, the Black-Scholes results are recovered, including the fact that the average return of the underlying stock disappears from the price (and the hedging strategy). We compare this theory to real option prices and find these reflect in a surprisingly accurate way the subtle statistical features of the underlying asset fluctuations.eptionally laborious..
MrPip, the data I use is freely available from Alpari from this location – http://www.alpari-idc.com/en/dc/databank.php. Download all the M1 data (about 15 months available) for the currency pair you’re interested in and uncompress it to a known location. Go to ToolsHistory Center and locate the symbol you’re working with. Delete all data (M1 to MN) for each period by first double-clicking on the period, highlighting ALL the data in the window and hitting Delete. The reason for this is that the Period Converter Script doesn’t seem to work properly if the fields already contain data. Also work offline when doing this because if any EAs or charts are active these will be updated in the background. Next, still in the History Center, ‘Import’ all the M1….
Distills complex theories for the benefit of the average trader with little or no background in finance or mathematics by offering a wide range of valuable, practical strategies for limiting risk, avoiding catastrophic losses and managing the futures portfolio to maximize profits. Numerous topics are explored including: why most traders lose at the futures game most of the time; why most mechanical trading systems are apt to fail; the probabilistic approach to trading; how to make stop-loss orders work for, rather than against you; the pros and cons of options versus futures trading; and how to limit risk through diversification.
Market risk management under normal conditions traditionally has focussed on the distribution of portfolio value changes resulting from moves in the mid-price. Hence the market risk is really in a “pure” form: risk in an idealized market with no “friction” in obtaining the fair price. However, many markets possess an additional liquidity component that arises from a trader not realizing the mid-price when liquidating her position, but rather the mid-price minus the bid-ask spread. We argue that liquidity risk associated with the uncertainty of the spread, particularly for thinly traded or emerging market securities under adverse market conditions, is an important part of overall risk and is therefore an important component to model.
Divergence, which is a term that technicians use when two or more averages or indices fail to show confirming trends, is one of the mainstays of technical analysis. Here’s a new way to use oscillators and divergence as well as methods to locate entry levels during a trend.