Questions tagged [kalman]
The [Kalman filter][1], also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone. [1]: http://en.wikipedia.org/wiki/Kalman_filter
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Kalman Filter Implementation using PyKalman [closed]
I am trying to apply a simple Kalman filter to pair trading. My underlying stock pair is cointegrated with no constant term. Can someone kindly advise if i am going on the right track with the Kalman ...
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State Space model with heteroskedastic disturbance - approximation of error term
We have a state-space model with a heteroskedastic disturbance term modelled according to some time-varying process (e.g. ARCH, GARCH etc.). The disturbance term is modelled according to e.g. $h_{t+1}=...
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Why long a position when the acutal price is higher than the predicted price? (Kalman filter for pairs trading)
In this paper Elliott, R., van der Hoek, J. and Malcolm, W. (2005) Pairs Trading., the spread (state process) is assumed to follow a mean reverting process $x_{k+1}-x_k=(a-bx_k)\tau+\sigma\sqrt{\tau}\...
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Odd Result from Computing Correlation Matrix from Kalman Filter Posteriori Covariance Estimate
I am using a Kalman Filter to estimate the return dynamics of a forwards curve on a particular commodity. My state space is the initial forwards values, and an initial guess of the drift functions for ...
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Kalman Filtering theory and application in Finance models under asymmetric or incomplete information
Why do we need Kalman Filtering theory in dynamic models in finance when we consider an environment of asymmetric or incomplete information? I understand that this has to do with the update of the ...
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Style analysis and Kalman Filter
I am trying to implement a code that uses Kalman filter to improve the performance of traditional style analysis. I have come across a paper called "Return based style analysis with time varying ...
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Estimating Heston with Unscented Kalman Filter
I am trying to estimate aHeston model using an Unscented Kalman filter. In particular, I am using the following Euler-Murayama discretisation:
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DLM package in R to estimate a state space model with drifts
I am trying to use the DLM package in R to estimate a state space model where the measurement and transition equations are as follows.
The measurement equations are:
$$
\begin{align}
\left(
\begin{...
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How to apply Kalman Filter to GDP data?
Once reverted the Merton/Vasicek formula I could compute the $PD^{PIT}$ for IFRS9 as
$PD^{PIT}_i(z) = \Phi \left( \phi^{-1}(PD^{TTC}_i) \sqrt{1-\rho_i} + \sqrt{\rho_i}z\right)$
The main issue is to ...
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How to measure the sensitivity of a fund to a set of indices?
I'm trying to understand how recently created funds work and ultimately derive a sort of a probability distribution for their future returns, by approximating them with indices and then using the ...
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Oil price model calibration with Kalman Filter and MLE in python
I am trying to calibrate a one-factor mean-reverting process in python 3. The process is defined as:
\begin{equation}
dX = k(\alpha - X)dt + \sigma dW
,
\end{equation}
where $\alpha = \mu - \frac{\...
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How is it possible that the measurement uncertainty in Kalman Filter is less than 0?
In Euan Sinclair's Option Trading, Pricing and Volatility Strategies and Techniques, it mentions that the true value of the price can be estimated via Kalman Filter:
$$S_\mathrm{new} = S + k (S_b − S)...
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Calibration of G2++ two factor interest rate model using Kalman filtering
I'm trying to use the Kalman filter to calibrate a G2++ interest rate model in R. I'm reading "Implementing interest rate models: A practical guide" by Park F.C. (2004) where he provides details on ...
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Likelihood increases on increasing variance of measurement error in kalman filter
I tried to fit a local trend model to daily data of a currency. I used the "dlm" package and tried to estimate the parameters V (measurement noise) and W (the process noise) via maximum likelihood.
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Kalman Filter for Multiple Regression?
I'm using Kalman Filter to calculate a rolling spread between two asset price series as commonly described by Chan and many others. I would like to extend this regression to the price of three assets, ...
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Kalman filter state/measurement noise
In a linear Kalman filter, we assume that the state and measurement noise are white noise N(0,Q) and N(0,R) respectively.
Is it common practice to test these hypothesis?
And what are the most common ...
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How can I estimate the time-varying θ term in the Hull-White one factor model?
I am trying to simulate the prices of bond indexes (e.g. Barclays Aggregate, IBOXX sovereign, IBOXX corporates) using Monte Carlo assuming that they follow the SDE given by the Hull-White model (one-...
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Is my coding for my kalman filter off when testing this specific set of pairs?
My kalman filter seems to be off for this specific set of pairs I'm looking at. As you can see, in the kalman filtered linear regression, there seems to be an outlying blue line nowhere near the data ...
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Does Chan use the wrong state transition model in his Kalman filter code?
In his book, Algorithmic Trading: Winning Strategies and Their Rationale, Ernie Chan shows how to use a Kalman filter to improve the returns of a cointegrated portfolio. Recall that the state equation ...
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Have I used correct state space formulation of Bivariate Trending OU process for Kalman Filter estimation?
Introduction
I'm trying to estimate the parameters of an Ornstein Uhlenbeck process for a risky asset using the Kalman Filter but have doubts about the state space formulation that I am using. Also, ...
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kalman filter for a multifactor model in R
I am trying to set up a time varying factor model for the purpose of return decomposition via kalman filter.
Following this example and slightly modifying it so as to accommodate for more than one ...
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Mean and standard deviation of price series with Kalman
I like to calculate the mean and standard deviation of a price series, using the Kalman filter. I am somehow stuck with the deviation, or have some problem in understanding, which my research could ...
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Example Scalar Model Extended Kalman Filter
I have a simple question. I think not a question is, is a request. This month I have been studying how to understand and implement the Kalman filter algorithm for simple models such as the local level....
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kalman filter update equation
Assume that futures price $F(t,T)$ follows the Ito process as described by the following stochastic process
$$ln F(t,T)=lnF(0,T)+(Z_1(t)e^{-k(T-t)}+Z_2(t))-(1/4k)[(1-e^{-2kT})(h_1^2+h_2^2))+4h_1h_0(1-...
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Does Kalman filter always improve over linear regression?
If I have a simple linear regression that has statistical signification but I would like to improve the overall prediction results. Will a Kalman filter be always an improvement or as least achieve ...
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How to find optimal noise covariance matrices Q & R
I am trying to use the discrete Kalman filter for forecasting and I wonder what is commonly considered as the optimal way of determining the measurement noise covariance constants (Q and R) for a ...
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Correct form for State Space Equation for Kalman Filter for DNS
In this paper:
http://www.ssc.upenn.edu/~fdiebold/papers/paper55/DRAfinal.pdf
in eqns 3,5 the state eqn has the mean removed.
$(z_t-\mu)=A(z_{t-1}-\mu) + \epsilon_t$
$y_t=C z_t + \delta_t$
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Identifiability of a state space model (Dynamic Linear Model)
Take a general linear Gaussian state space model (SSM)(aka Dynamic Linear Model DLM):
$X_{t+1}=FX_t + V_t$
$Y=HX_t+W_t$
$V_t \sim N(0,Q)$
$W_t \sim N(0,R)$
I am interested in the ...
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Moving window forecasting in Python
I am looking to create some code that will out-of-sample forecast the HAR-RV model.
The model itself is formulated as the following, and the betas are estimated through HAC-OLS or Newey-West.
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Kalman filtering
Is it possible to the extract the latent factor f from the following equations using kalman smoothing?
f is the unobserved state value while z is observed series.
From the literature i could read ...
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Time-Varying Copulas (GAUSS)
Could anyone suggest me how to begin with Time-varying Copulas or Stochastic Copulas? I'm looking for the GAUSS code, however it seems there are only MATLAB code available over the internet.
I'm ...
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robust open source Kalman filter library in C++
I would like to know if anyone has experience with a good open source kalman filter implementation in C++ that I could use. I require an implementation that supports computation of likelihood similar ...
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Variable Selection with Kalman Filter
I'm trying to estimate factor loadings on portfolios over time for portfolios that are traded pretty frequently. I have a sense that several portfolios are loading on the Fama-French HML factor ...
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Simulating state space model with AR(1) dynamics
I asked a question similar to this previously:
https://dsp.stackexchange.com/questions/16341/simulating-a-state-space-model
However I think I have a better handle on it now and want to re-ask it:
I ...
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mean reversion with Kalman Filter - Spread calculation
Ernest Chan in its book "Algorithmic Trading" shows how to use the Kalman Filter for mean reversion pair trading.
I have seen that he uses the measurement prediction error for calculating the spread ...
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Maximum Likelihood using a Kalman filter for two factor model
I'm trying to implement a Kalman Filter for the parameter estimation of a linear gaussian two factor model in Matlab. (Schwartz Smith model for commodity prices) In other words: I try to compute the ...
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Good criteria to sort state-space $\beta_{t}$ according to Kalman filter output
Let's assume the usual state-space linear model without constant term for simplicity:
$y_{t}=\beta_{t} X_{t}+\epsilon_{t}$
If we apply Gaussian Kalman filter to estimate $\beta_{t}$ we get $P_{t}$, ...
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Time-varying correlation via state-space representation and Kalman filter
Let a linear time-varying mode like this one:
$y_{t}=\alpha_{t}+\beta_{t}x_{t}+\epsilon_{t}$.
You can also suppress the constant term to simplify this example:
$y_{t}=\beta_{t}x_{t}+\epsilon_{t}$.
...
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How to tune Kalman filter's parameter?
I plan to use Kalman filter to estimate saving account amount.
However, I'm a bit lost at how to tune the filter's parameters.
Taking as the example from the Wikipedia page, basically there are ...
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Counterintuitive time varying Beta with Kalman filter
If you're used to play with R, you'll enjoy the following reproducible code:
...
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Parameter estimation of Ornstein–Uhlenbeck and CIR processes
I would like to estimate Ornstein–Uhlenbeck process' parameters via Kalman filter.
My process is the following one:
$\text{d}x_{t}=\alpha(\theta-x_{t})\text{d}t+\sigma\text{d}W_{t}$
I'm interested ...
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Kalman Filter Equity Example
I am looking out for some material where I can study about Kalman Filter applied to Equity using Excel or R?
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