Hansen–Jagannathan Bounds Do Not Bound Factor Betas

# Existence of an upper bound for risk-factor betas/coefficients


# Existence of an upper bound for risk-factor betas/coefficients












### Theory:

Based on Hansen/Jagannathan, the set of means and variances of returns is limited. With $R^f$ as the risk-free rate, $R_i^e$ as the return of stock $i$ in excess of $R^f$ and a stochastic discount factor $m$, we know:

$$\frac{\sigma(m)}{\mathrm{E(m)}} \geq \frac{\lvert \mathrm{E}(R_i^e)-R^f \rvert}{\sigma(R_i^e)} $$

We further know, that Beta pricing models are equivalent to linear models for the discount factor $m$:

$$\mathrm{E}(R_i^e) = \gamma + \lambda'\beta_i \leftrightarrow m = a + b'f,$$

with $\lambda$ being the factor risk-premium of factor $f$, where $\lambda = \mathrm{E}(f)$ holds (consider $f$ to be a tradable factor, e.g. the market excess return in case of the CAPM).

In empirical research we estimate $\beta_i$ by time-series regressions,

$$R_i^e = a_0 + \lambda' \beta_i + \epsilon_i$$

which implies

$$\beta_i = \frac{cov(R_i ^e, \lambda')}{var(\lambda')}$$

Does there exist an upper bound for $\beta_i$, dependent on $m$ or information about $\lambda'$?

My question is related to this one, where Matthew Gunn states that

> Infinity is rather non-sensical.

However, the question asks for the market beta and not for an upper limit of coefficients ("beta") of proposed risk-factors (i.e. non-market factors).

### Example:

Consider the Fama/French (1992, 1993) three factor model with a size factor SMB, the value factor HML and the market factor MKTRF and lets replicate Table 6 of the paper. We use 25 value-weighted portfolio returns, sorted on size and book-to-market ratio, and regress each of them onto our three risk factors:

$$R_i^e = a_0 + b_1 MKTRF+ b_2 SMB + b_3 HML + \epsilon_i,$$

and we are interested on the estimated coefficients $\hat{b}_2$ for the size-factor SMB.

R-Code:

```
library(FFdownload)

tempf <- tempfile(fileext = ".RData")
inputlist <- c("F-F_Research_Data_Factors", "25_Portfolios_5x5")

# download factors and 5x5 portfolios sorted on size and book-to-market
FFdownload(output_file = tempf, inputlist=inputlist, exclude_daily = TRUE, download = TRUE, download_only=FALSE)
load(tempf)
factors    <- FFdata$`x_F-F_Research_Data_Factors`$monthly$Temp2[,c("Mkt.RF","SMB","HML")]
portfolios <- FFdata$x_25_Portfolios_5x5$monthly$average_value_weighted_returns

# mean and volatility of factor returns
apply(factors, 2, mean)

> Mkt.RF    SMB       HML 
> 0.6704671 0.1931055 0.3590398 

apply(factors, 2, sd)

> Mkt.RF    SMB       HML 
> 5.352773 3.170227 3.565333 

SIZE_BETAS <- vector(mode = "numeric", length = 25)

# regress portfolio returns on the three factors
for(i in 1:25){
  ret <- portfolios[,i]
  reg <- lm(ret ~ factors)
  SIZE_BETAS[i] <- reg$coefficients[3]  # extract estimated coef. for size factor
}

# results as in Fama/French (1993), Table 6, coefficient "s"
# deviations from the original Table are because data on French Data Lib. is updated
t(matrix(SIZE_BETAS, nrow = 5, ncol = 5))

           [,1]       [,2]       [,3]       [,4]       [,5]
[1,]  1.4617511  1.5366500  1.2439367  1.2224793  1.3077786
[2,]  1.1348994  0.9895360  0.8200150  0.8118625  0.9160859
[3,]  0.8070132  0.5416144  0.4409870  0.4683932  0.5762129
[4,]  0.3306691  0.2294190  0.2040363  0.2017700  0.3108749
[5,] -0.1537455 -0.1927042 -0.2378840 -0.1890345 -0.1753790
```

The replication of size-factor coefficients using the 25 test portfolios gives estimates within the interval $[-0.24;1.54]$, so i would take the maximum value 1.54 as an upper bound in that case. However, using another set of test portfolios would yield other estimates. What can we say about an upper limit of $\hat{\beta}_i$ having information about risk-factors (here MKTRF, SMB and HML) spanning the discount factor $m$?

## Answer by Kevin (score 3)

https://quant.stackexchange.com/a/74160

I do not think such an upper bound exists for beta in terms of moments of the SDF alone. The upper bound of beta depends on the volatility of the SDF and the volatility of the asset returns. The former is given from its factor mimicking portfolio, the latter can be arbitrarily large.

### Sharpe ratios

To start with, you write

> Based on Hansen/Jagannathan, the set of means and variances of returns is limited.

This is not true because the Hansen and Jagannathan (1991) bound only limits assets' Sharpe ratios by moments of the SDF. There is no bound on the individual means and variances of asset returns.

The Hansen and Jagannathan (1991) bound essentially says: high Sharpe ratios only exist if the SDF is very volatile [if there's much aggregated risk]. Empirical asset pricing often applies the theorem to test whether a candidate SDF is volatile enough to match the observable Sharpe ratios in the data.

### Trivial bound

A regression coefficient is bounded by assuming perfect correlation, \begin{align} \beta_i=\frac{Cov(R_i,M)}{Var[M]}\leq \frac{\sigma_i}{\sigma_M}. \end{align} Beta is bounded if $\sigma_i$ is bounded and if $\sigma_M$ isn't to close to zero.

Knowing the factors mimicking the SDF means we know the mean and variance of the SDF. The Hansen and Jagannathan (1991) bound tells that the true SDF needs to be sufficiently volatile. Otherwise, we can't justify the high Sharpe ratios in the data. So, the second concern about a low $\sigma_M$ is less of a concern.

However, note that some candidate SDFs are known for their low volatility. The CCAPM uses aggregate consumption growth as SDF, $M_{t,t+1}=\delta\left(\frac{C_{t+1}}{C_t}\right)^{-\gamma}$. However, in the data, consumption growth isn't very volatile. It's this low volatility which causes the sound rejection of the model. More modern macro-finance models include habits, long run risks or disaster risks to make their SDFs more volatile and better able to match the data.

### Leverage

Returning to $\beta_i$, if we can rule out $\sigma_M$ running to zero, we can be assured that $\beta_i$ is bounded if $\sigma_i$ is. However, this is a problem. In your example, you use the famous 25 size/book-market portfolios as test assets. Other popular examples include 125 portfolios sorted on size/book-market/momentum or industry portfolios. However, we can choose our test assets fairly flexibly. Why not take any arbitrary portfolio and lever it up? This is still a tradable portfolio which the model should be able to price. However, by levering up a portfolio, we can arbitrarily increase its volatility (and beta). Crucially, note that levering doesn't impact the Hansen and Jagannathan (1991) bound which is concerned with Sharpe ratios, means divided by volatilities, and is insensitive to leverage.

Take your `R` code, replace the 25 portfolios by levered versions and see how your betas change. To bound the betas, you'd need to assume that investors can't arbitrarily lever up their portfolios. You'd need to restrict the set of valid test assets. There are surely models incorporating such frictions, but they're absent from the standard introduction of the SDF.

### Easy illustration

To see the points more easily, let $R_i$ be an excess return and $\lambda>0$. Then, $R_\lambda=\lambda R_i$ is also a valid return and \begin{align} \mu_{\lambda} &= \lambda \mu_i, \\ \sigma_\lambda &= \lambda \sigma_i,\\ \beta_\lambda &= \lambda \beta_i, \\ \frac{\mu_\lambda}{\sigma_\lambda} &= \frac{\mu_i}{\sigma_i}. \end{align}