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This is a special case of the question of why $$ \int_0^T f(t) dW_t $$ is normally distributed for a continuous function $f(t).$ This Ito integral can be approximated by a sum $$ \sum_{i=0}^{N-1} f(i T/N) (W_{(i+1)T/N} - W_{i T/N}) .$$ The Brownian increments $(W_{(i+1)T/N} - W_{i T/N})$ are independent normally distributed random variables. The key point ...


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The problem of when to exercise an option with Bermudan features is an optimal stopping problem. I have a done a lot of work on how to do these things when the state space is high dimensional. There are various more complicated problems where the contract is more difficult eg swing options.


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For simplicity, We assume that $\alpha$ is a positive constant. You need to show that, for any $t>0$, \begin{align*} \int_0^t e^{\alpha u} dW_u \end{align*} is normally distributed. Consider the process $\{X_t, t \geq 0\}$, where \begin{align*} X_t = \frac{1}{\sqrt{\frac{1}{t}\int_0^t e^{2\alpha u} du}}\int_0^t e^{\alpha u} dW_u, \end{align*} for ...


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Of course, optimal control is at the core of math finance. Take few applications: Option Pricing: you have an exposure to a time dependent combination of market factors; you have some knowledge of their dynamics. They are partly deterministic, partly stochastic (i.e. random). At each "time step" you can adjust your portfolio at a given cost. Your goal is ...


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Actually, a lot of finance and economics are centered around optimal control problems. Traditionally, most economies are modeled as dynamic systems. In finance, portfolio optimizations, advanced option pricing etc are all optimal control problems. You could look at the book Non Linear Option Pricing, it has a lot of optimal control problems.


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You said that the PX_BID and PX_ASK values are dependent upon when you pulled the data. But if I pull historical data (e.g. for the last month) only the value of today would be changing but not the past ones... So there should be a point of time when the final PX_BID and PX_ASK values for a day are calculated. Or am I wrong?


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The portfolio is self-financing. You simply forgot a term in $b$ and a $-t$ term in $V$: \begin{eqnarray} V_t &=& a_t S_t + b_t \beta_t = (2B_t ) (10+ B_t) + (- t - B_t^2 - 20B_t)1 \\ &=& 20B_t + 2B_t^2 - t - B_t^2 - 20B_t \\ &=& B_t^2 - t \end{eqnarray} Applying Ito's lemma \begin{eqnarray} dV_t &=& (2B_t dB_t + ...


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Your choice of $a_t$ and $b_t$ is feasible. For a self-financing portfolio, the units invested should be static within an infinitesimal time interval, that is, no extra investing or withdrawing during this period. In other words, the portfolio value changes only through its underlyings. For a further discussion, See the article ...


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Here is a related previous StackExchange question: Modelling with negative interest rates Also, it seems that Black-Scholes option pricing breaks down.



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