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Risk Of Ruin
Risk of ruin is a concept in gambling, insurance, and finance relating to the likelihood of losing all one's investment capital or extinguishing one's bankroll below the minimum for further play. For instance, if someone bets all their money on a simple coin toss, the risk of ruin is 50%. In a multiple-bet scenario, ''risk of ruin'' accumulates with the number of bets: each play increases the risk, and persistent play ultimately yields the stochastic certainty of gambler's ruin. Finance Risk of ruin for investors Two leading strategies for minimising the risk of ruin are diversification and hedging/portfolio optimization. An investor who pursues diversification will try to own a broad range of assets – they might own a mix of shares, bonds, real estate and liquid assets like cash and gold. The portfolios of bonds and shares might themselves be split over different markets – for example a highly diverse investor might like to own shares on the LSE, the NYSE and various oth ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic
Stochastic (, ) refers to the property of being well described by a random probability distribution. Although stochasticity and randomness are distinct in that the former refers to a modeling approach and the latter refers to phenomena themselves, these two terms are often used synonymously. Furthermore, in probability theory, the formal concept of a ''stochastic process'' is also referred to as a ''random process''. Stochasticity is used in many different fields, including the natural sciences such as biology, chemistry, ecology, neuroscience, and physics, as well as technology and engineering fields such as image processing, signal processing, information theory, computer science, cryptography, and telecommunications. It is also used in finance, due to seemingly random changes in financial markets as well as in medicine, linguistics, music, media, colour theory, botany, manufacturing, and geomorphology. Etymology The word ''stochastic'' in English was originally used as a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Trader (finance)
A trader is a person, firm, or entity in finance who buys and sells financial instruments, such as forex, cryptocurrencies, stocks, bonds, commodities, derivatives, and mutual funds in the capacity of agent, hedger, arbitrageur, or speculator. Duties and types Traders buy and sell financial instruments traded in the stock markets, derivatives markets and commodity markets, comprising the stock exchanges, derivatives exchanges, and the commodities exchanges. Several categories and designations for diverse kinds of traders are found in finance, including: *Bond trader *Floor trader *Hedge fund trader *High-frequency trader *Market maker *Pattern day trader * Principal trader * Proprietary trader *Rogue trader *Scalper *Stock trader Income According to the Wall Street Journal in 2004, a managing director convertible bond trader was earning between $700,000 and $900,000 on average. See also *Commodities exchange *Commodity market *Derivatives market *List of commodity traders *Li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Value At Risk
Value at risk (VaR) is a measure of the risk of loss for investments. It estimates how much a set of investments might lose (with a given probability), given normal market conditions, in a set time period such as a day. VaR is typically used by firms and regulators in the financial industry to gauge the amount of assets needed to cover possible losses. For a given portfolio, time horizon, and probability ''p'', the ''p'' VaR can be defined informally as the maximum possible loss during that time after excluding all worse outcomes whose combined probability is at most ''p''. This assumes mark-to-market pricing, and no trading in the portfolio. For example, if a portfolio of stocks has a one-day 95% VaR of $1 million, that means that there is a 0.05 probability that the portfolio will fall in value by more than $1 million over a one-day period if there is no trading. Informally, a loss of $1 million or more on this portfolio is expected on 1 day out of 20 days (because of 5% proba ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Operational Risk Management
Operational risk management (ORM) is defined as a continual recurring process that includes risk assessment, risk decision making, and the implementation of risk controls, resulting in the acceptance, mitigation, or avoidance of risk. ORM is the oversight of operational risk, including the risk of loss resulting from inadequate or failed internal processes and systems; human factors; or external events. Unlike other type of risks (market risk, credit risk, etc.) operational risk had rarely been considered strategically significant by senior management. Four principles The U.S. Department of Defense summarizes the principles of ORM as follows: * Accept risk when benefits outweigh the cost. * Accept no unnecessary risk. * Anticipate and manage risk by planning. * Make risk decisions in the right time at the right level. Three levels ; In Depth: In depth risk management is used before a project is implemented, when there is plenty of time to plan and prepare. Examples of in de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Key Risk Indicator
A key risk indicator (KRI) is a measure used in management to indicate how risky an activity is. Key risk indicators are metrics used by organizations to provide an early signal of increasing risk exposures in various areas of the enterprise. It differs from a key performance indicator (KPI) in that the latter is meant as a measure of how well something is being done while the former is an indicator of the possibility of future adverse impact. KRI give an early warning to identify potential events that may harm continuity of the activity/project. KRIs are a mainstay of operational risk analysis. Definitions According to OECD :''A risk indicator is an indicator that estimates the potential for some form of resource degradation using mathematical formulas or models.'' Risk management Security risk management According to Risk IT framework by ISACA, key risk indicators are metrics capable of showing that the organization is subject or has a high probability of being subject ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Financial Risk Modeling
Financial risk modeling is the use of formal mathematical and econometric techniques to measure, monitor and control the market risk, credit risk, and operational risk on a firm's balance sheet, on a bank's trading book, or re a fund manager's portfolio value; see Financial risk management. Risk modeling is one of many subtasks within the broader area of financial modeling. Application Risk modeling uses a variety of techniques including market risk, value at risk (VaR), historical simulation (HS), or extreme value theory (EVT) in order to analyze a portfolio and make forecasts of the likely losses that would be incurred for a variety of risks. As above, such risks are typically grouped into credit risk, market risk, model risk, liquidity risk, and operational risk categories. Many large financial intermediary firms use risk modeling to help portfolio managers assess the amount of capital reserves to maintain, and to help guide their purchases and sales of various classes of f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fat-tailed Distribution
A fat-tailed distribution is a probability distribution that exhibits a large skewness or kurtosis, relative to that of either a normal distribution or an exponential distribution. In common usage, the terms fat-tailed and Heavy-tailed distribution, heavy-tailed are sometimes synonymous; fat-tailed is sometimes also defined as a subset of heavy-tailed. Different research communities favor one or the other largely for historical reasons, and may have differences in the precise definition of either. Fat-tailed distributions have been empirically encountered in a variety of areas: physics, earth sciences, economics and political science. The class of fat-tailed distributions includes those whose tails decay like a power law, which is a common point of reference in their use in the scientific literature. However, fat-tailed distributions also include other slowly-decaying distributions, such as the log-normal distribution, log-normal. The extreme case: a power-law distribution The m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Asset Allocation
Asset allocation is the implementation of an investment strategy that attempts to balance risk versus reward by adjusting the percentage of each asset in an investment portfolio according to the investor's risk tolerance, goals and investment time frame. The focus is on the characteristics of the overall portfolio. Such a strategy contrasts with an approach that focuses on individual assets. Description Many financial experts argue that asset allocation is an important factor in determining returns for an investment portfolio. Asset allocation is based on the principle that different assets perform differently in different market and economic conditions. A fundamental justification for asset allocation is the notion that different asset classes offer returns that are not perfectly correlated, hence diversification reduces the overall risk in terms of the variability of returns for a given level of expected return. Asset diversification has been described as "the only free lunch ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Finance
Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets. In general, there exist two separate branches of finance that require advanced quantitative techniques: derivatives pricing on the one hand, and risk and portfolio management on the other. Mathematical finance overlaps heavily with the fields of computational finance and financial engineering. The latter focuses on applications and modeling, often by help of stochastic asset models, while the former focuses, in addition to analysis, on building tools of implementation for the models. Also related is quantitative investing, which relies on statistical and numerical models (and lately machine learning) as opposed to traditional fundamental analysis when managing portfolios. French mathematician Louis Bachelier's doctoral thesis, defended in 1900, is considered the first scholarly work on mathematical fina ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Absorbing Markov Chain
In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. An absorbing state is a state that, once entered, cannot be left. Like general Markov chains, there can be continuous-time absorbing Markov chains with an infinite state space. However, this article concentrates on the discrete-time discrete-state-space case. Formal definition A Markov chain is an absorbing chain if # there is at least one absorbing state and # it is possible to go from any state to at least one absorbing state in a finite number of steps. In an absorbing Markov chain, a state that is not absorbing is called transient. Canonical form Let an absorbing Markov chain with transition matrix ''P'' have ''t'' transient states and ''r'' absorbing states. Unlike a typical transition matrix, the rows of ''P'' represent sources, while columns represent destinations. Then : P = \left( \begin Q & R\\ \mathbf & I_r \end \right), where '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Chain
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs ''now''." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC). It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, such as studying cruise control systems in motor vehicles, queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used for simulating sampling from complex probability dist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
