Volatility Forecasting: HAR Model
Among the different members of the family of volatility forecasting models by weighted moving average^{1} like the simple and the exponentially weighted moving average models or the GARCH(1,1) model, the Heterogeneous AutoRegressive (HAR) model introduced by Corsi^{2} has become the workhorse of the volatility forecasting literature^{3} on account of its simplicity and generally good forecasting performance^{3}.
In this blog post, strongly inspired by the paper A Practical Guide to harnessing the HAR volatility model from Clements and Preve^{4}, I will describe the HAR volatility forecasting model together with some important implementation details and I will illustrate its practical performances in the context of monthly volatility forecasting for various ETFs.
Mathematical preliminaries (reminders)
This section contains reminders from a previous blog post.
Volatility modelling and volatility proxies
Let $r_t$ be the (logarithmic) return of an asset over a time period $t$ (a day, a week, a month..), over which its (conditional) mean return is supposed to be null.
Then:

The asset (conditional) variance is defined as $ \sigma_t^2 = \mathbb{E} \left[ r_t^2 \right] $
From this definition, the squared return $r_t^2$ of an asset is a (noisy^{4}) variance estimator  or variance proxy^{4}  for that asset variance over the considered time period.
Another example of an asset variance proxy is the Parkinson range of an asset.
Yet another example of an asset variance proxy, this time over a specific time period $t$ of one day, is the daily realized variance $RV_t$ of an asset, which is defined as the sum of the asset squared intraday returns sampled at a high frequency (1 minutes, 5 minutes, 15 minutes…).
The generic notation for an asset variance proxy in this blog post is $\tilde{\sigma}_t^2$.

The asset (conditional) volatility is defined as $ \sigma_t = \sqrt { \sigma_t^2 } $
The generic notation for an asset volatility proxy in this blog post is $\tilde{\sigma}_t$.
Weighted moving average volatility forecasting model
Boudoukh et al.^{1} show that many seemingly different methods of volatility forecasting actually share the same underlying representation of the estimate of an asset next period’s variance $\hat{\sigma}_{T+1}^2$ as a weighted moving average of that asset past periods’ variance proxies $\tilde{\sigma}^2_t$, $t=1..T$, with
\[\hat{\sigma}_{T+1}^2 = w_0 + \sum_{i=1}^{k} w_i \tilde{\sigma}^2_{T+1i}\], where:
 $1 \leq k \leq T$ is the size of the moving average, possibly timedependent
 $w_i, i=0..k$ are the weights of the moving average, possibly timedependent as well
The HAR volatility forecasting model
The original HAR model
Due to the limitations of the GARCH model in reproducing the main empirical features of financial returns (long memory, fat tails, and selfsimilarity)^{2}, Corsi^{2} proposes to use an additive cascade model of different volatility components each of which is generated by the actions of different types of market participants^{2}.
As detailled by Corsi^{2}:
The main idea is that agents with different time horizons perceive, react to, and cause different types of volatility components. Simplifying a bit, we can identify three primary volatility components: the shortterm traders with daily or higher trading frequency, the mediumterm investors who typically rebalance their positions weekly, and the longterm agents with a characteristic time of one or more months.
Under this volatility forecasting model called the Heterogeneous AutoRegressive model (HAR)^{5}, an asset next day’s daily realized variance $RV_{T+1}$ is modeled as an AR(22) process subject to economically meaningful restrictions^{2} on its parameters, which results in the formula^{4}
\[\hat{RV}_{T+1} = \beta + \beta_d RV_{T} + \beta_w RV_{T}^w + \beta_m RV_{T}^m\], where:
 $\hat{RV}_{T+1}$ is the forecast at time $T$ of the asset next day’s daily realized variance $RV_{T+1}$
 $RV_T$ is the asset daily realized variance at time $T$
 $RV_T^w = \frac{1}{5} \sum_{i=1}^5 RV_{Ti+1}$ is the asset weekly realized variance at time $T$
 $RV_T^m = \frac{1}{22} \sum_{i=1}^{22} RV_{Ti+1}$ is the asset monthly realized variance at time $T$
 $\beta$, $\beta_d$, $\beta_w$ and $\beta_m$ are the HAR model parameters, to be determined
In terms of practical performances, and in spite of its simplicity […], the [HAR] model is able to reproduce the same volatility persistence observed in the empirical data as well as many of the other main stylized facts of financial data^{2}, which makes it a very accurate volatility forecasting model.
Realized variance v.s. generic variance proxy
The original HAR model described in the previous subsection relies on a very specific asset variance proxy  the realized variance of an asset  over a very specific time period  a day  for its definition.
Some papers (Lyocsa et al.^{6}, Lyocsa et al.^{7}, Clements et al.^{3}, …) propose to replace the (highfrequency) daily realized variance by a (lowfrequency) daily rangebased variance estimator^{8} like:
 The square of the Parkinson volatility estimator
 The square of the GarmanKlass volatility estimator
 The square of the RogersSatchell volatility estimator
Going one step further, it is possible to replace the daily realized variance by any generic daily variance estimator.
This leads to the generic HAR volatility forecasting model, under which an asset next days’s conditional variance $\sigma_{T+1}^2$ is modeled as a linear function of [its previous day’s] daily, weekly and monthly [conditional variance] components^{4}, with the following daily variance forecasting formula
\[\hat{\sigma}_{T+1}^2 = \beta + \beta_d \tilde{\sigma}^2_{T} + \beta_w \tilde{\sigma}^{2,w}_{T} + \beta_m \tilde{\sigma}^{2,m}_{T}\], where:
 $\hat{\sigma}_{T+1}^2$ is the forecast at time $T$ of the asset next day’s conditional variance $\sigma_{T+1}^2$
 $\tilde{\sigma}^2_{T}, \tilde{\sigma}^2_{T1},…,\tilde{\sigma}^2_{T21}$ are the asset daily variance estimators over each of the previous 22 days at times $T$, $T1$, …, $T21$
 $\tilde{\sigma}^{2,w}_{T} = \frac{1}{5} \sum_{i=1}^5 \tilde{\sigma}^2_{T+1i}$ is the asset weekly variance estimator at time $T$
 $\tilde{\sigma}^{2,m}_{T} = \frac{1}{22} \sum_{i=1}^{22} \tilde{\sigma}^2_{T+1i}$ is the asset monthly variance estimator at time $T$
 $\beta$, $\beta_d$, $\beta_w$ and $\beta_m$ are the HAR model parameters, to be determined
Going another step further, it is also possible to replace the baseline daily time period by any desired time period (weekly, biweekly, monthly, quarterly…), but given the theoretical foundations of the HAR model, this also requires to replace the weekly and the monthly variance estimators $\tilde{\sigma}^{2,w}_{T}$ and $\tilde{\sigma}^{2,m}_{T}$ by appropriate variance estimators.
Relationship with the generic weighted moving average model
From its definition, it is easy to see that the HAR volatility forecasting model is a specific kind of weighted moving average volatility forecasting model, with:
 $w_0 = \beta$
 $w_1 = \beta_d + \frac{1}{5} \beta_w + \frac{1}{22} \beta_m$
 $w_i = \frac{1}{5} \beta_w + \frac{1}{22} \beta_m, i = 2..5$
 $w_i = \frac{1}{22} \beta_m, i = 6..22$
 $w_i = 0$, $i \geq 23$, discarding all the past variance proxies beyond the $22$th from the model
Volatility forecasting formulas
Under an HAR volatility forecasting model, the generic weighted moving average volatility forecasting formula becomes:

To estimate an asset next day’s volatility:
\[\hat{\sigma}_{T+1} = \sqrt{ \beta + \beta_d \tilde{\sigma}^2_{T} + \frac{\beta_w}{5} \sum_{i=1}^5 \tilde{\sigma}^2_{T+1i} + \frac{\beta_m}{22} \sum_{i=1}^{22} \tilde{\sigma}^2_{T+1i} }\] 
To estimate an asset next $h$day’s ahead volatility^{9}, $h \geq 2$:
\[\hat{\sigma}_{T+h} = \sqrt{ \beta + \beta_d \hat{\sigma}_{T+h1}^2 + \frac{\beta_w}{5} \left( \sum_{i=1}^{5h+1} \tilde{\sigma}^2_{T+1i} + \sum_{i=1}^{h1} \hat{\sigma}^2_{T+hi} \right) + \frac{\beta_m}{22} \left( \sum_{i=1}^{22h+1} \tilde{\sigma}^2_{T+1i} + \sum_{i=1}^{h1} \hat{\sigma}^2_{T+hi} \right) }\] 
To estimate an asset aggregated volatility^{9} over the next $h$ days:
\[\hat{\sigma}_{T+1:T+h} = \sqrt{ \sum_{i=1}^{h} \hat{\sigma}^2_{T+i} }\]
Note:
Clements and Preve^{4} extensively discuss whether to use an indirect or a direct multistep ahead forecast scheme for estimating an asset next $h$day’s ahead volatility under an HAR volatility forecasting model:
 In an indirect scheme^{10}, which is the scheme described above, the asset next $h$day’s ahead volatility is estimated indirectly, by a recursive application of the HAR model formula for the asset next days’s conditional variance.
 In a direct scheme, the asset next $h$day’s ahead volatility is estimated directly, by replacing the asset next days’s conditional variance on the lefthand side of the HAR model formula with the asset next $h$day’s ahead conditional variance.
Clements and Preve^{4} argue in particular that “direct forecasts are easy to compute and more robust to model misspecification compared to indirect forecasts”^{4}, but at the same time, they show that this robustness does not always translate into more accurate forecasts.
Harnessing the HAR volatility forecasting model
HAR model parameters estimation
Ordinary least squares estimators
Corsi^{2} writes that it is possible to easily estimate [the HAR model] parameters by applying simple linear regression^{2}, in which case the ordinary least squares (OLS) estimator of the parameters $\beta$, $\beta_d$, $\beta_w$ and $\beta_m$ at time $T \geq 23$ is the solution of the minimization problem^{4}
\[\argmin_{ \left( \beta, \beta_d, \beta_w, \beta_m \right) \in \mathbb{R}^{4}} \sum_{t=23}^T \left( \tilde{\sigma}_{t}^2  \beta  \beta_d \tilde{\sigma}^2_{t1}  \beta_w \tilde{\sigma}^{2,w}_{t1}  \beta_m \tilde{\sigma}^{2,m}_{t1} \right)^2\]Other least squares estimators
Nevertheless, Clements and Preve^{4} warn that given the stylized facts of [volatility estimators] (such as spikes/outliers, conditional heteroskedasticity, and nonGaussianity) and wellknown properties of OLS, this […] should be far from ideal^{4}.
Indeed, as detailled in Patton and Sheppard^{11}:
Because the dependent variable in all of our regressions is a volatility measure, estimation by OLS has the unfortunate feature that the resulting estimates focus primarily on fitting periods of high variance and place little weight on more tranquil periods. This is an important drawback in our applications, as the level of variance changes substantially across our sample period and the level of the variance and the volatility in the error are known to have a positive relationship.
Instead of OLS, Clements and Preve^{4} and Clements et al.^{3} suggest to use other least squares estimators:
 Weighted least squares estimators (WLS), with for example the weighting schemes described in Clements and Preve^{4}
 Robust least squares estimators (RLS), with for example Tukey’s biweight loss function
 Regularized least squares estimators (RRLS) (ridge regression, LASSO regression, elastic net regression…), with the crossvalidation procedure for the associated hyperparameters described in Clements et al.^{3}.
Expanding v.s. rolling window estimation procedure
Clements and Preve^{4} empirically demonstrate that the HAR model parameters $\beta$, $\beta_d$, $\beta_w$ and $\beta_m$ are timevarying, as illustrated in Figure 1.
To deal with this nonstationarity, or more generally to deal with parameter drift that is difficult to model explicitly^{4}, the standard^{12} approach in the litterature is to use a rolling window procedure^{13} for the least squares estimation of the HAR model parameters.
Insanity filter
The HAR volatility forecasting model may on rare occasions generate implausibly large or small forecasts^{4}, because no restrictions on the parameters [$\beta$, $\beta_d$, $\beta_w$ and $\beta_m$] are imposed^{11}.
In particular, it has been noted^{14} that forecasts are occasionally negative^{11}.
In order to correct this behaviour, Clements and Preve^{4} propose to implement an insanity filter^{15} ensuring that any forecast greater than the maximum, or less than the minimum, of [an asset next days’s conditional variance] observed in the estimation period is replaced by the sample average over that period^{4}.
Two important remarks on such a filter:
 It seems that the particular choice of insanity filter is not important; what matters is to eliminate unrealistic forecasts^{16}.
 The presence of an insanity filter is all the more important when an indirect multistep ahead forecast scheme is used for estimating an asset next $h$day’s ahead volatility, because an unreasonable [forecast] is most likely to generate another unreasonable forecast^{16}.
Variance proxies transformations
Clements and Preve^{4} study three different BoxCox transformations of an asset daily realized variance that can be used in the HAR volatility forecasting model instead of that asset “raw” daily realized variance:
 The logarithmic transformation (log)
 The quartic root transformation (qr)
 The square root transformation (sqr)
It is visible on Figure 2 that these transformations appear useful for reducing skewness, and hence the possible effect of outliers and potential heteroskedasticity in the realized variance series^{4}.
Of particular interest is the logarithmic transformation^{17}, which in addition to good practical performances and closed form biascorrected expressions for variance forecasts^{18}, also guarantees that the generated forecasts are always positive^{19}.
Nonoverlapping daily, weekly and monthly variance proxies
Corsi et al.^{20} propose a slightly different parametrization of the HAR model compared to Corsi^{2}, where:
 $\tilde{\sigma}^2_{T}, \tilde{\sigma}^2_{T1},…,\tilde{\sigma}^2_{T21}$ are the asset daily variance estimators over each of the previous 22 days at times $T$, $T1$, …, $T21$
 $\tilde{\sigma}^{2,w}_{T} = \frac{1}{4} \sum_{i=2}^5 \tilde{\sigma}^2_{T+1i}$ is the asset weekly variance estimator at time $T$, nonoverlapping with the asset daily variance estimator $\tilde{\sigma}^2_{T}$
 $\tilde{\sigma}^{2,m}_{T} = \frac{1}{17} \sum_{i=6}^{22} \tilde{\sigma}^2_{T+1i}$ is the asset monthly variance estimator at time $T$, nonoverlapping with either the asset daily variance estimator $\tilde{\sigma}^2_{T}$ nor with the asset weekly variance estimator $\tilde{\sigma}^{2,w}_{T}$
Such a reparametrization does not imply any loss of information compared to the original [HAR model], since it relies only on a different rearrangement of the terms^{20}, but allows an easiest interpretation of the HAR model parameters, c.f. Patton and Sheppard^{11}.
Incidentally, this nonoverlapping formulation of the HAR volatility forecasting model has been found to generate better forecasts by practitioners^{21}.
Other lag indexes for the variance proxies
The standard lag indexes for the variance proxies in the HAR volatility forecasting model are 1, 5 and 22, each corresponding to a different volatility component in Corsi’s underlying additive cascade model.
As mentioned in Corsi^{2}, more components could easily be added to the additive cascade of partial volatilities^{2}, which is done for example in Lyocsa et al.^{7}.
In that case, denoting $l$ and $h$, respectively, the lowest and highest frequency in the cascade^{2}, an asset next days’s conditional variance is modeled as an AR($\frac{l}{h}$) process reparameterized in a parsimonious way by imposing economically meaningful restrictions^{2}.
Implementation in Portfolio Optimizer
Portfolio Optimizer implements the HAR volatility forecasting model  augmented with the insanity filter described in Clements and Preve^{4}  through the endpoint /assets/volatility/forecast/har
.
This endpoint supports the 4 variance proxies below:
 Squared closetoclose returns
 Demeaned squared closetoclose returns
 The Parkinson range
 The jumpadjusted Parkinson range
This endpoint also supports:

Transforming the input variance proxies into log variance proxies before estimating the HAR model parameters; the associated variance forecasts are then unbiased thanks to the formulas established in Buccheri and Corsi^{18}.
 Estimating the HAR model parameters through 3 different least squares procedures:
 Ordinary least squares
 Weighted least squares, using the inverse of the variance proxies as weights
 Robust least squares, using Tukey’s biweight loss function
 Using up to 5 lag indexes for the variance proxies
Example of usage  Volatility forecasting at monthly level for various ETFs
As an example of usage, I propose to enrich the results of the previous blog post, in which monthly forecasts produced by different volatility models are compared  using MincerZarnowitz^{22} regressions  to the next month’s closetoclose observed volatility for 10 ETFs representative^{23} of misc. asset classes:
 U.S. stocks (SPY ETF)
 European stocks (EZU ETF)
 Japanese stocks (EWJ ETF)
 Emerging markets stocks (EEM ETF)
 U.S. REITs (VNQ ETF)
 International REITs (RWX ETF)
 U.S. 710 year Treasuries (IEF ETF)
 U.S. 20+ year Treasuries (TLT ETF)
 Commodities (DBC ETF)
 Gold (GLD ETF)
Vanilla HAR volatility forecasting model
Averaged results for all ETFs/regression models over each ETF price history^{24} are the following^{25}, when using the vanilla HAR volatility forecasting model:
Volatility model  Variance proxy  $\bar{\alpha}$  $\bar{\beta}$  $\bar{R^2}$ 

Random walk  Squared closetoclose returns  5.8%  0.66  44% 
SMA, optimal $k \in \left[ 1, 5, 10, 15, 20 \right]$ days  Squared closetoclose returns  5.8%  0.68  46% 
EWMA, optimal $\lambda$  Squared closetoclose returns  4.7%  0.73  45% 
GARCH(1,1)  Squared closetoclose returns  1.3%  0.98  43% 
HAR  Squared closetoclose returns  0.7%  0.95  46% 
Random walk  Parkinson range  5.6%  0.94  44% 
SMA, optimal $k \in \left[ 1, 5, 10, 15, 20 \right]$ days  Parkinson range  5.1%  1.00  47% 
EWMA, optimal $\lambda$  Parkinson range  4.3%  1.06  48% 
GARCH(1,1)  Parkinson range  2.7%  1.18  47% 
HAR  Parkinson range  0.1%  1.25  44% 
Random walk  Jumpadjusted Parkinson range  4.9%  0.70  45% 
SMA, optimal $k \in \left[ 1, 5, 10, 15, 20 \right]$ days  Jumpadjusted Parkinson range  5.1%  0.71  47% 
EWMA, optimal $\lambda$  Jumpadjusted Parkinson range  4.0%  0.76  45% 
GARCH(1,1)  Jumpadjusted Parkinson range  1.0%  1.00  45% 
HAR  Jumpadjusted Parkinson range  1.4%  0.99  47% 
Alternative HAR volatility forecasting models
Averaged results for all ETFs/regression models over each ETF price history^{24} are the following^{25}, when using different variations of the vanilla HAR volatility forecasting model:
Volatility model  Variance proxy  $\bar{\alpha}$  $\bar{\beta}$  $\bar{R^2}$ 

HAR  Squared closetoclose returns  0.7%  0.95  46% 
HAR (weighted least squares)  Squared closetoclose returns  3.1%  0.71  34% 
HAR (robust least squares)  Squared closetoclose returns  12.3%  2.50  26% 
HAR (log)  Squared closetoclose returns  0.5%  0.62  40% 
HAR (log, weighted least squares)  Squared closetoclose returns  10%  0.26  13% 
HAR (log, robust least squares)  Squared closetoclose returns  0.5%  0.53  40% 
HAR  Parkinson range  0.1%  1.25  44% 
HAR (weighted least squares)  Parkinson range  0.1%  1.19  44% 
HAR (robust least squares)  Parkinson range  4.2%  2.20  40% 
HAR (log)  Parkinson range  1.9%  1.22  50% 
HAR (log, weighted least squares)  Parkinson range  0.6%  1.47  47% 
HAR (log, robust least squares)  Parkinson range  2.2%  1.22  50% 
HAR  Jumpadjusted Parkinson range  1.4%  0.99  47% 
HAR (weighted least squares)  Jumpadjusted Parkinson range  4.2%  0.92  46% 
HAR (robust least squares)  Jumpadjusted Parkinson range  6.6%  1.76  41% 
HAR (log)  Jumpadjusted Parkinson range  0.9%  0.92  51% 
HAR (log, weighted least squares)  Jumpadjusted Parkinson range  0.8%  1.06  48% 
HAR (log, robust least squares)  Jumpadjusted Parkinson range  1.2%  0.92  51% 
Comments
From the results of the two previous subsections, it is possible to make the following comments:
 When using squared returns as a variance proxy, the vanilla HAR model is the best volatility forecasting model among all the alternative HAR models as well as among all the models already studied in this series.
 When using the Parkinson range or the jumpadjusted Parkinson range as variance proxies, the log HAR model exhibits the highest rsquared among all the models already studied in this series.
 When using the jumpadjusted Parkinson range as a variance proxy, the log HAR model is the best volatility forecasting model in this series (relatively low bias, highest rsquared).
 When using alternative least square estimators, the forecasts quality generally degrades
Conclusion
The previous section empirically demonstrated that the HAR volatility forecasting model, despite its relationship with realized variance, is still accurate when used with rangebased variance estimators at a long forecasting horizon, in line with Lyocsa et al.^{6}
In that context, the (log) HAR model is also the most accurate volatility forecasting model studied so far in this series on volatility forecasting by weighted moving average models!
As usual, feel free to connect with me on LinkedIn or to follow me on Twitter.
–

See Boudoukh, J., Richardson, M., & Whitelaw, R.F. (1997). Investigation of a class of volatility estimators, Journal of Derivatives, 4 Spring, 6371. ↩ ↩^{2}

See Fulvio Corsi, A Simple Approximate LongMemory Model of Realized Volatility, Journal of Financial Econometrics, Volume 7, Issue 2, Spring 2009, Pages 174–196. ↩ ↩^{2} ↩^{3} ↩^{4} ↩^{5} ↩^{6} ↩^{7} ↩^{8} ↩^{9} ↩^{10} ↩^{11} ↩^{12} ↩^{13} ↩^{14}

See Clements, Adam and Preve, Daniel P. A. and Tee, Clarence, Harvesting the HARX Volatility Model. ↩ ↩^{2} ↩^{3} ↩^{4} ↩^{5}

See Adam Clements, Daniel P.A. Preve, A Practical Guide to harnessing the HAR volatility model, Journal of Banking & Finance, Volume 133, 2021. ↩ ↩^{2} ↩^{3} ↩^{4} ↩^{5} ↩^{6} ↩^{7} ↩^{8} ↩^{9} ↩^{10} ↩^{11} ↩^{12} ↩^{13} ↩^{14} ↩^{15} ↩^{16} ↩^{17} ↩^{18} ↩^{19} ↩^{20} ↩^{21}

This additive cascade model is autoregressive in (daily) realized variance and combines realized variance over different horizons (daily, weekly, monthly), hence its name. ↩

See Stefan Lyocsa, Peter Molnar, Tomas Vyrost, Stock market volatility forecasting: Do we need highfrequency data?, International Journal of Forecasting, Volume 37, Issue 3, 2021, Pages 10921110. ↩ ↩^{2}

See Stefan Lyocsa, Tomas Plihal, Tomas Vyrost, FX market volatility modelling: Can we use lowfrequency data?, Finance Research Letters, Volume 40, 2021, 101776. ↩ ↩^{2}

These three volatility estimators are described in details in a previous blog post. ↩

See Brooks, Chris and Persand, Gitanjali (2003) Volatility forecasting for risk management. Journal of Forecasting, 22(1). pp. 122. ↩ ↩^{2}

Also called iterated scheme. ↩

See Andrew J. Patton, Kevin Sheppard; Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility. The Review of Economics and Statistics 2015; 97 (3): 683–697. ↩ ↩^{2} ↩^{3} ↩^{4}

Some papers like Buccheri and Corsi^{18} describe how to explicitely model the HAR model parameters as timevarying, but in that case, those parameters are not anymore obtained through a simple least squares regression… ↩

The length of the rolling window is typically 1000 days, corresponding approximately to 1000 days of trading; more details on the rolling window procedure can be found in Clements et al.^{3} and a numerical comparison in terms of outofsample volatility forecasts between an expanding and a rolling window procedure can be found in Clements and Preve^{4}. ↩

And I confirm from practical experience. ↩

Such a filter has apparently a long history in the forecasting litterature, dating back at least to Swanson and White^{26} in the context of neuralnetwork models for interest rates forecasting. ↩

See Cunha, Ronan, Kock, Anders Bredahl and Pereira, Pedro L. Valls, Forecasting large covariance matrices: comparing autometrics and LASSOVAR. ↩ ↩^{2}

Already hinted at in Corsi^{2}. ↩

See Giuseppe Buccheri, Fulvio Corsi, HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies, Journal of Financial Econometrics, Volume 19, Issue 4, Fall 2021, Pages 614–649. ↩ ↩^{2} ↩^{3}

Which helps limiting the need for an insanity filter. ↩

See Fulvio Corsi, Nicola Fusari, Davide La Vecchia, Realizing smiles: Options pricing with realized volatility, Journal of Financial Economics, Volume 107, Issue 2, 2013, Pages 284304. ↩ ↩^{2}

See Salt Financial, The Layman’s Guide to Volatility Forecasting: Predicting the Future, One Day at a Time, Research Note. ↩

See Mincer, J. and V. Zarnowitz (1969). The evaluation of economic forecasts. In J. Mincer (Ed.), Economic Forecasts and Expectations. ↩

These ETFs are used in the Adaptative Asset Allocation strategy from ReSolve Asset Management, described in the paper Adaptive Asset Allocation: A Primer^{27}. ↩

The common ending price history of all the ETFs is 31 August 2023, but there is no common starting price history, as all ETFs started trading on different dates. ↩ ↩^{2}

For all models, I used an expanding window for the volatility forecast computation. ↩ ↩^{2}

See Swanson, N. R. and H. White (1995). A modelselection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business & Economic Statistics 13 (3), 265–275. ↩

See Butler, Adam and Philbrick, Mike and Gordillo, Rodrigo and Varadi, David, Adaptive Asset Allocation: A Primer. ↩