# Tag Info

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Here are a few risks when using historical data: Data fidelity: Is your data an accurate reflection of history? For stocks, should you use actual closing prices or adjusted prices? For futures, how should you construct a realistic, continuous contract? Simulation realism: Are you making realistic assumptions about trade execution? Are you naively assuming, ...

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I assume you mean risk neutral pricing? Think of it this way (beware, oversimplification ahead ;-) You want to price a derivative on gold, a gold certificate. The product just pays the current price of an ounce in $. Now, how would you price it? Would you think about your risk preferences? No, you won't, you would just take the current gold price and ... 16 ML/AI systems are susceptible to a number of risks not traditionally discussed in risk management: What I call 'backtest arbitrage'. In the process of automated model generation and testing, your machine learner may discover, exploit, and concentrate on irregularities in your backtesting system which do not exist in the real world. If, for example, your ... 16 This is practically a textbook case begging for the Kelly criterion. In your specific example, the optimal trade size is$f^*A$, where$f^*$maximizes the average rate of return $$\mathbb{E}[\log (X)]=0.5\log(1+0.3f)+0.5\log(1-0.23f).$$ Here$f$is the fraction of the current capital to trade. A straightforward calculation yields that $$f^*=\frac{0.3-0.23}{... 15 Suppose that you and other bettors participate in a lottery with N possible outcomes; event will occur with probability \pi_n. There are N basic contracts available for purchase. Contract n costs p_n and entitles you to one dollar if outcome n occurs, zero otherwise. Now, imagine that you have a contingent claim that pays a complex payoff based ... 14 We bet on a fair coin toss -- heads you get \100, tails you get \0. So the expected value is \50. But it is unlikely that you'll pay \50 to play this game because most people are risk averse. If you were risk neutral, then you WOULD pay \50 for an expected value of \50 for an expected net payoff of \0. A risk neutral player will accept ... 13 Accuracy The trader must make sure the data is not only right, but that the timestamps are useable. That's why a good data warehouse will be bitemporal or point-in-time. Thus, we know not only when the item was announced, but when we received it and could act on it. Gaps An aggressive safety check on incoming data might inadvertently exclude correct data. ... 12 Knowledge leads to profit BUT NOT the reverse YOU are the biggest risk to the process. All the hopes, wishes and bias you come with get in the way of making good decisions. The more you want something to be true, the more you have to kick the tires. So many people try out a bunch of random stuff, find a pattern that has a notional profit and just get ... 10 The risks involved in trading is everywhere and always a multifaceted thing: it includes the volatility of the selected asset, the leverage and concentration of the porfolio, whether there is a stop loss, a hedge, etc. Also, risk management is frequently not tied to the "alpha model" directly (e.g. VaR, shortfall, and scenario testing). For instance, one ... 10 I'm not sure about the "CAPM formula" that you are referring to. I assume you are referring to the estimated coefficient of a regression of a security on a market portfolio. That is to say $$\beta_{security,market} = \frac{\sigma_{security,market}}{\sigma^2_{market}}$$ The idiosyncratic risk is the portion of risk unexplained ... 9 I think the biggest risk is trusting your model too much. I would summarize modelling like that: Model for the best but risk-manage for the worst! As an example for modelling a portfolio approach with derivatives that could e.g. mean: use black scholes for option pricing (model) but manage your risk by assuming a power law distribution and vary your alpha ... 8 Jennifer Bender of MSCI Barra has a paper from 2007 entitled: To Beta or Not to Beta: A Comparison of Historical Versus Fundamental Betas for Hedging Market Risk She deals specifically and exclusively with which method is superior for hedging long-only portfolios. Not surprisingly, she finds that Barra's approach is better. She tests long-only and long-... 8 Risk Parity is not about "having the same volatility", it is about having each asset contributing in the same way to the portfolio overall volatility. The volatility of the portfolio is defined as:$$\sigma(w)=\sqrt{w' \Sigma w}$$The risk contribution of asset i is computed as follows:$$\sigma_i(w)= w_i \times \partial_{w_i} \sigma(w)$$You can then ... 8 Indeed, algorithmic trading is a very hidden subject. All I can help you with are some industry-specific terms which might speed up your search for relevant papers and information: Risk of ruin tables (Peak-to-valley) drawdown (maximum drawdown, duration of drawdown etc.) Number of consecutive losses Confidence intervals Empirical distributions (for risk ... 7 The risk-netural measure has a massively important property which is worth making very clear: The price of any trade is equal to the expectation of the trade’s winnings and losses under the risk-neutral measure. This property gives us a scheme for pricing derivatives: take a collection of prices of trades that exist in the market (eg swap rates, bond ... 7 Each shop will differ - there is no widely used, unified framework shared across firms. Competitive advantages vary across shops, which ultimately reflect the biases/characteristics of the particular shop. Some will be far more mathematically sophisticated/inclined than others. Some maintain strong aversion to quantiative techniques such as risk models. ... 7 I would use the identity and three step process that:$$\textrm{Total Variance} = \textrm{Systematic Variance} + \textrm{Unsystematic Variance}$$You can calculate systematic variance via:$$\textrm{Systematic Risk} = \beta \cdot \sigma_\textrm{market} \Rightarrow \; \textrm{Systematic Variance} = (\textrm{Systematic Risk})^2$$then you can rearrange ... 7 If you measure risk by the standard deviation of the portfolio return$$ \sigma = \sqrt{w^T \Sigma w}, $$then it is usual to define risk contributions for each asset by$$ \sigma_i = w_i (\Sigma w)_i/\sigma,$$then diversified could mean that these$\sigma_i\$ are evenly spread over the assets in the portfolio. You find this approach and more in this paper ...

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A market is said to be complete if any contingent claim can be replicated by an admissible (i.e. with value process bounded from below) self-financing (i.e. all gains and losses exactly offset each other) trading strategy, a so-called replicating strategy. This strategy being constructed from primary securities - the market prices of which are unique - it ...

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To give an example of a source of risk that isn't one of the ones you mentioned but still broadly on-topic for a Quant Finance site: operational risk - for which there are many references for contigency plans. This is the domain of the back office. Trades are created (priced and analysed) by quants, executed by traders and approved by preferably at least one ...

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The shops I've worked for have had access to multiple brokers, but not for redundancy as your question implies. It's often because no one broker can handle every task. For example, I might need a floor broker, a dark-pool broker, an algo broker, and a separate prime broker. Each agency handles a different requirement. Even if one broker could handle all of ...

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The Kelly criterion is a very popular bet-sizing method. Edward Thorp has written a great deal on this topic. You can try googling for more, or start with his review of the concept, or a recent paper, Medium Term Simulations of The Full Kelly and Fractional Kelly Investment Strategies. This is not specific to futures, but I'm not sure why you would need ...

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Hull - Options, Futures and other Derivatives.

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Also, RiskMetrics' 'granular approach' may be of interest (I have no affiliation): See: I. Developing an Equity Factor Model for Risk II. The RiskMetrics 2006 Methodology, RM2006

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The algorithm is certainly useful in that it is non-parametric, fast, and versatile. Meucci summarizes the advantages nicely: Unlike traditional copula techniques, CMA a) is not restricted to few parametric copulas such as elliptical or Archimedean; b) never requires the explicit computation of marginal cdf’s or quantile functions; c) does not ...

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What you refer to as the 99.5th percentile is known as the "Value-at-Risk." You are correct that you will need to make a distributional assumption, and there is a popular and well-researched approach to this problem, though I'm not certain it could be called "standard." I would recommend you use the "truncated Levy flight" distribution. James Xiong at ...

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I am a risk taker and I can say with confidence that you will never convince those individuals, you cited in your question, that they incur too much risk, because there will always be certain traders who prefer lottery tickets over longevity with the same firm (running high risk books unfortunately in the current environment runs equal to a free option; blow ...

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Yes, it is correct. Underestimation: you under-estimate the risk, so you have more VaR violations than what your model predicts. Ex: With 100 observations, and a 99% VaR, you expect 1 violation but you observe 5 violations. Overestimation: you over-estimate the risk, i.e the risk is less important that you expect. You observe less VaR violations that you ...

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There are all sorts of financial and non-financial risks. I define financial risk as all risks defined from events in the financial markets that affect all participants. Non-financial risks are all other forms of risk (including risks that a particular firm may face). Financial: Market value risk (interest rate risk, exchange prices, equity prices, ...

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