Financial risk is any of various types of risk associated with financing, including financial transactions that include company loans in risk of default. Often it is understood to include only downside risk, meaning the potential for financial loss and uncertainty about its extent.
A science has evolved around managing market and financial risk under the general title of modern portfolio theory initiated by Harry Markowitz in 1952 with his article, "Portfolio Selection". In modern portfolio theory, the variance (or standard deviation) of a portfolio is used as the definition of risk.
According to Bender and Panz (2021), financial risks can be sorted into five different categories. In their study, they apply an algorithm-based framework and identify 193 single financial risk types, which are sorted into the five categories market risk, liquidity risk, credit risk, business risk and investment risk.
Market risk
The four standard market risk factors are equity risk, interest rate risk, currency risk, and commodity risk:
Equity risk is the risk that stock prices in general (not related to a particular company or industry) or the implied volatility will change. When it comes to long-term investing, equities provide a return that will hopefully exceed the risk free rate of return The difference between return and the risk free rate is known as the equity risk premium. When investing in equity, it is said that higher risk provides higher returns. Hypothetically, an investor will be compensated for bearing more risk and thus will have more incentive to invest in riskier stock. A significant portion of high risk/ high return investments come from emerging markets that are perceived as volatile.
Interest rate risk is the risk that interest rates or the implied volatility will change. The change in market rates and their impact on the profitability of a bank, lead to interest rate risk. Interest rate risk can affect the financial position of a bank and may create unfavorable financial results.
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This course presents the problem of static optimization, with and without (equality and inequality) constraints, both from the theoretical (optimality conditions) and methodological (algorithms) point
The course provides a market-oriented framework for analyzing the major financial decisions made by firms. It provides an introduction to valuation techniques, investment decisions, asset valuation, f
This course is an introduction to quantitative risk management that covers standard statistical methods, multivariate risk factor models, non-linear dependence structures (copula models), as well as p
Equity risk is "the financial risk involved in holding equity in a particular investment." Equity risk is a type of market risk that applies to investing in shares. The market price of stocks fluctuates all the time, depending on supply and demand. The risk of losing money due to a reduction in the market price of shares is known as equity risk. The measure of risk used in the equity markets is typically the standard deviation of a security's price over a number of periods.
In simple terms, risk is the possibility of something bad happening. Risk involves uncertainty about the effects/implications of an activity with respect to something that humans value (such as health, well-being, wealth, property or the environment), often focusing on negative, undesirable consequences. Many different definitions have been proposed. The international standard definition of risk for common understanding in different applications is "effect of uncertainty on objectives".
In finance, the beta (β or market beta or beta coefficient) is a statistic that measures the expected increase or decrease of an individual stock price in proportion to movements of the Stock market as a whole. Beta can be used to indicate the contribution of an individual asset to the market risk of a portfolio when it is added in small quantity. It is referred to as an asset's non-diversifiable risk, systematic risk, or market risk. Beta is not a measure of idiosyncratic risk.
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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...