Research articles for the 2020-08-01
A Data-Driven Framework for Consistent Financial Valuation and Risk Measurement
SSRN
In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical non-parametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the non-parametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.
SSRN
In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical non-parametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the non-parametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.
Dealers' Search Intensity in U.S. Corporate Bond Markets
SSRN
Are dealers' search efforts endogenous in decentralized markets? How do dealers' search efforts affect market efficiency? We propose a model with dealers choosing idiosyncratic search intensities, and estimate the model using transaction data on U.S. corporate bonds. We find that: [1] with dealers ranked by their private valuations for a bond, the middle-type dealer chooses the highest search intensity, and she reallocates bond positions from lower-type dealers to higher-type dealers; [2] the estimated model predicts that the search costs and bond mis-allocation in current OTC markets generate 13.7% welfare loss relative to a counterfactual friction-less market.
SSRN
Are dealers' search efforts endogenous in decentralized markets? How do dealers' search efforts affect market efficiency? We propose a model with dealers choosing idiosyncratic search intensities, and estimate the model using transaction data on U.S. corporate bonds. We find that: [1] with dealers ranked by their private valuations for a bond, the middle-type dealer chooses the highest search intensity, and she reallocates bond positions from lower-type dealers to higher-type dealers; [2] the estimated model predicts that the search costs and bond mis-allocation in current OTC markets generate 13.7% welfare loss relative to a counterfactual friction-less market.
Option Pricing: A Heuristic Based on Exponential Decay
SSRN
From the option prices with strike K, with K > S for calls and K < S for puts, a parameter can be estimated to calculate the prices of the entire options chain using a heuristic based on exponential decay, which has very well known applications in several natural and social phenomena. With the support of arbitrage restrictions such as the put-call parity, reasoning is validated and we can consider alternatives to evaluate forward conditions such as volatility and option prices in financial markets.
SSRN
From the option prices with strike K, with K > S for calls and K < S for puts, a parameter can be estimated to calculate the prices of the entire options chain using a heuristic based on exponential decay, which has very well known applications in several natural and social phenomena. With the support of arbitrage restrictions such as the put-call parity, reasoning is validated and we can consider alternatives to evaluate forward conditions such as volatility and option prices in financial markets.
