# Tag Info

13

Yes it is a better way. Just take a look to figure 3, from Buss and Vilkov (2012, RFS):

12

From Yahoo! Finance Help The Beta used is Beta of Equity. Beta is the monthly price change of a particular company relative to the monthly price change of the S&P500. The time period for Beta is 3 years (36 months) when available. Source: https://help.yahoo.com/kb/finance/SLN2347.html?impressions=true (+Stock Price History)

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Proof: Recall that $$\beta_{i} = \frac{\mathrm{Cov}(r_{i},r_{m})}{\mathrm{Var}(r_{m})}.$$ Now, the returns on unlevered and levered equity are given by $$r_{U} = \frac{\mathrm{EBIT}(1-\tau) - \mathrm{CAPEX} + \mathrm{Depreciation}}{E_{U}}$$ $$r_{L} = \frac{\mathrm{EBIT}(1-\tau) - \mathrm{CAPEX} + \mathrm{Depreciation} + \mathrm{Net\ Debt} - \mathrm{... 8 There are more ways to approach this but the method I propose should work reasonably well in practice, especially if you increase the number of assets you hold. Calculate the beta of the stocks you're holding with respect to an index Buy N_f (sell when N_f is negative) future contracts on that index N_f can be calculated as$$N_f = \frac{\beta_T - \...

7

Infinity is rather non-sensical. A better question perhaps is whether you can put some theoretical bounds on an asset's market beta. An asset's volatility bounds its market beta Let $R_i$ be the return of security $i$ and $R_m$ be the return of the market. Market beta would be given by: $$\beta_i = \frac{\operatorname{Cov}(R_i, R_m)}{\operatorname{Var}(... 6 beta_A = correlation_A_Index * (stdd_A / stdd_Index ) The difference you see is due to correlation. The correlation between A and the index is lower than B and the index, and that's why you're seeing a lower beta. The moral of the story is that risk is subjective, and in fact you need to understand how your portfolio is correlated with these stocks in ... 6 What you're describing sounds like the reverse of a Fama-Macbeth regression. The original Fama-Macbeth approach estimated rolling time series regressions to get CAPM betas and then doing a cross-sectional regression to estimate the overall sensitivity of returns to beta. If I were to write down what the model looks like, I think you're talking about ... 6 concerning your first question: the derivative does not disappear: \sigma(R_p) contains the square root. To be more precise, set$$ \sigma(R_p) = \sqrt{w_1^2\cdot\sigma(R_1)^2 + w_2^2\cdot\sigma(R_2)^2 + 2w_1w_2\text{Cov}(R_1, R_2)}. Then we get using the chain rule: \begin{align} \frac{\partial\sigma(R_p)}{\partial w_1} &= \frac 12 \cdot \biggl(\... 6 I slightly disagree with Alex’s comment. The CAPM does not read as \begin{align*} r_{i,t} = r_{f,t}+ \beta_{i,t} (r_{m,t}-r_f) + \varepsilon_{i,t}. \end{align*} There is an important difference between the single index model (aka market model) (SIM) which reads as \begin{align*} r_{i,t} = \alpha_{i,t} + \beta_{i,t}(r_{m,t}-r_{f,t}) + \varepsilon_{i,t} \end{... 5 Yahoo Finance calculates beta from monthly prices over a time of three years. The S&P500 is used as the benchmark You need 37 monthly prices (so you can get 36 returns) on the first trading day of each month. The final price should be on the first trading day of the previous month. The first price should be on the first trading day of the month 36 ... 5 This is a very good question. It can be argued that risk parity is one example of a smart beta strategy. Yet it is important to understand that both are coming from two different directions: risk parity is basically a form of risk management (in the sense of risk-adjustment) because its basic approach lies in diversification - like the alternative methods ... 5 Imagine a scenario where a beta neutral portfolio comprised being long one very high beta stock and short many low beta stocks. Such a portfolio clearly has extreme concentration of risk. Additionally imposing a 'dollar neutral' constraint, would help to spread the weights more evenly over all the stocks. A further observation is that measuring true 'beta'... 5 An excess return is the payoff of a zero cost portfolio. For example: R_i - R_f is an excess return. c \left( R_i - R_f \right)  is an excess return for any c \in \mathbb{R},. More generally, R_i - R_j is an excess return for any returns R_i and R_j. Excess returns are nice to work with because you cans simply scale them up or scale them down ... 5 In a word, yes. That's a correct and valid view to take but, as you'll always find in finance, it really depends on context and the question that you're trying to answer. This is the case in markets but more broadly in business and something that academically minded scientists/engineers struggle often understand and appreciate fully. This boils down to the ... 5 To give you an idea of industry standards for funds (although not hedge-fund specific), Morningstar and Trustnet both use monthly returns and annualize their data. See, for an example plucked at random, https://www.trustnet.com/factsheets/o/gnol/aberdeen-asia-pacific--japan-equity-i-acc. Monthly returns remain the standard because some funds only publish ... 5 You are right to be sceptical of the beta of an international portfolio when it is calculated using daily returns. Beta estimates are often low for international portfolios because stock market returns are asynchronous. For example, Tokyo and the New York Stock Exchange have very different trading hours. Portfolios constructed with a tilt towards either ... 4 I did not look at the data, but recall that beta is a parameter in the following equation: r_A = \alpha + \beta r_B + \epsilon $$relating two returns (random variables, samples) r_A and r_B. To calculate beta you peform$$ \beta = \frac{cov(r_A,r_B)}{var(r_B)}. $$Thus if assets A and B exchange roles, then only the denominator changes. In your ... 4 I've started thinking about this, too. My gedanken conclusion turned out to be too simple once I found what I was after: http://www.investment-and-finance.net/derivatives/o/option-beta.html, which I've confirmed in Black & Scholes (1973) p10 (eq 15). In short:$$ \beta_{\text{option}} = \frac{S\cdot\Delta}{O}{\beta_S} $$where S is the underlying ... 4 You need returns for 36 months, in particular data from 37 months. Yahoo also uses unadjusted closing prices for the reference index as far as i know. The data from 8/1/2015 got to be an error, I checked multiply data sources and found no similarities. After interpolating that point i got a beta of 0.48. 3 Intuitively put you can say that volatility is the within variation and beta is the between variation. Within means the variation that A has within its own time-series, whereas between means between A and the index. 3 Beta calculation varies quite a bit as you've already noted. Using monthly or weekly closing prices is fairly common though; I don't know of anyone who uses daily prices. Yahoo gets its data from CapitalIQ so you may want to look over there. Good luck! 3 Interesting idea. I'm guessing this isn't used for two reasons: First, the only algorithm I could find is O(n^3), which is horrible if you're using a moderately-sized high-frequency dataset. Least squares is O(nk^2) (n is the number of rows, and k is the number of predictors; typically k<<n). More relevantly, L1 regression is almost as ... 3 Suppose you have$$X\equiv\left(x_{1},\: x_{2}\right) $$where x_{1} are the daily log returns of the security and x_{2} are the daily log returns of the market. Assume further that X is iid multivariate normal$$X\sim N\left(\mu,\Sigma\right) $$People frequently calculate beta as$$\beta_{1,2}\equiv\frac{\Sigma_{1,2}}{\Sigma_{2,2}} $$If you ... 3 Not sure what the question is. As John points out: the method is linear regression. For the data you could look at Kenneth French's wegpage for US stocks. In the wikipedia article you find the links to factors for other countries (UK, Germny, Switzerland) - though I have not checked these links. Note however that the Fama-French model works better for ... 3 This is in essence the idea behind Andrea Frazzini's paper 'Betting Against Beta'. There are various ETFs that aim to exploit the premium. In R, you can do just do a linear regression using the lm(Y~X) which includes an intercept or using lm(Y~X+0) which regresses without an intercept. Assuming you've saved the model in variable lm.r, then to get the ... 3 The standard deviation (and variance) of the returns of an asset has two sources: the market beta times the market's standard deviation, and the asset's own idiosyncratic (market independent) standard deviation. Hence, an asset with high idiosyncratic standard deviation can have a high standard deviation despite a low beta. Definition of A:s beta to the ... 3 Focusing on intuition rather than theory, \beta can also be thought of as the "risk premium" of that specific asset relative to the market. In general, market risk premium links two very important aspects of the world: Consumption & Return. So if we look at the world in two states, an "Up State" & "Down State", here ... 3 The answer is NO. It's mathematically incorrect. Simply look the correlation and covariance formulas. But here is a gedankenexperiment (thought experiment) that demonstrates that it's incorrect. Suppose, R1 = M. Then the claim Corr(M,R1) = Corr(M,R2) implies 1 = Corr(M,R2) for any R2, which is obviously wrong. 3 Market beta just tells your portfolio has low covariance, scaled by variance, with the market. Remember that$$ \beta= \frac{Cov(x,y)}{Var(x)} = \rho\frac{\sigma_x \sigma_y}{\sigma_x^2}=\rho\frac{\sigma_y}{\sigma_x}  You can see that it well may be that $\sigma_x<\sigma_y$ but $\rho$ is small enough to have a beta of 0.5. By the way, you can directly ...

3

This simply suggests the linear model is a poor fit in high frequency. But is this that surprising, even before you crunch the numbers? I argue not, for the following reasons: Even at low frequencies (i.e. monthly or annually), it is known that the classical CAPM (which is what you're running, albeit at a much higher frequency) does not fit well. It'd be a ...

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