scholarly journals A UNIFIED MARKET MODEL FOR SWAPTIONS AND CONSTANT MATURITY SWAPS

2021 ◽  
pp. 2150026
Author(s):  
CHYNG WEN TEE ◽  
JEROEN KERKHOF
Keyword(s):  
Interest Rate ◽  
Market Model ◽  
Approximation Formula ◽  
Internal Rate Of Return ◽  
Main Interest ◽  
Interest Rate Volatility ◽  
Self Consistent ◽  
Industry Practice ◽  
The Difference ◽  
Constant Maturity Swaps

Internal-rate-of-return (IRR) settled swaptions are the main interest rate volatility instruments in the European interest rate markets. Industry practice is to use an approximation formula to price IRR swaptions based on Black model, which is not arbitrage-free. We formulate a unified market model to incorporate both swaptions and constant maturity swaps (CMS) pricing under a single, self-consistent framework. We demonstrate that the model is able to calibrate to market quotes well, and is also able to efficiently price both IRR-settled and swap-settled swaptions, along with CMS products. We use the model to illustrate the difference in implied volatilities for IRR-settled payer and receiver swaptions, the pricing of zero-wide collars and in-the-money (ITM) swaptions, the implication on put-call parity, and the issue of negative vega. These findings offer important insights to the ongoing reform in the European swaption market.

scholarly journals HOLDER-EXTENDIBLE EUROPEAN OPTION: CORRECTIONS AND EXTENSIONS

2015 ◽  
Vol 56 (4) ◽  
pp. 359-372 ◽  
Author(s):  
PAVEL V. SHEVCHENKO
Keyword(s):  
Brownian Motion ◽  
Interest Rate ◽  
Real Life ◽  
Option Price ◽  
Geometric Brownian Motion ◽  
Fixed Amount ◽  
Underlying Asset ◽  
Closed Form Solutions ◽  
Pricing Options ◽  
Interest Rate Volatility

Financial contracts with options that allow the holder to extend the contract maturity by paying an additional fixed amount have found many applications in finance. Closed-form solutions for the price of these options have appeared in the literature for the case when the contract for the underlying asset follows a geometric Brownian motion with constant interest rate, volatility and nonnegative dividend yield. In this paper, option price is derived for the case of the underlying asset that follows a geometric Brownian motion with time-dependent drift and volatility, which is more important for real life applications. The option price formulae are derived for the case of a drift that includes nonnegative or negative dividend. The latter yields a solution type that is new to the literature. A negative dividend corresponds to a negative foreign interest rate for foreign exchange options, or storage costs for commodity options. It may also appear in pricing options with transaction costs or real options, where the drift is larger than the interest rate.

Determinants of Treasury-LIBOR Swap Spreads

2005 ◽  
Vol 08 (04) ◽  
pp. 687-705 ◽  
Author(s):  
D. K. Malhotra ◽  
Vivek Bhargava ◽  
Mukesh Chaudhry
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Default Risk ◽  
Yield Curve ◽  
Short Term ◽  
Long Run ◽  
Interest Rate Volatility ◽  
Short Run ◽  
Using Data ◽  
Swap Spreads

Using data from the Treasury versus London Interbank Offer Swap Rates (LIBOR) for October 1987 to June 1998, this paper examines the determinants of swap spreads in the Treasury-LIBOR interest rate swap market. This study hypothesizes Treasury-LIBOR swap spreads as a function of the Treasury rate of comparable maturity, the slope of the yield curve, the volatility of short-term interest rates, a proxy for default risk, and liquidity in the swap market. The study finds that, in the long-run, swap spreads are negatively related to the yield curve slope and liquidity in the swap market. We also find that swap spreads are positively related to the short-term interest rate volatility. In the short-run, swap market's response to higher default risk seems to be higher spread between the bid and offer rates.

To Raise the Golden Anchor? Financial Crises and Uncertainty During the Great Depression

1999 ◽  
Vol 59 (3) ◽  
pp. 624-658 ◽  
Author(s):  
J. Peter Ferderer ◽  
David A. Zalewski
Keyword(s):  
Economic Growth ◽  
Interest Rate ◽  
Great Depression ◽  
Interwar Period ◽  
Gold Standard ◽  
Financial Crises ◽  
Capital Flight ◽  
The Great Depression ◽  
Interest Rate Volatility

This study examines the interplay between financial crises, uncertainty, and economic growth during the interwar period. Comparing the experiences of ten countries, we provide evidence that reductions in the credibility of a country's commitment to the gold standard generated capital flight and higher interest rate volatility. This volatility, in turn, was inversely correlated with economic growth. These results suggest that financial crises helped propagate the Great Depression, in part, by increasing uncertainty.

Predicting Interest Rate Volatility

1992 ◽  
Vol 1 (4) ◽  
pp. 21-27 ◽  
Author(s):  
Robert C. Kuberek
Keyword(s):  
Interest Rate ◽  
Interest Rate Volatility

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

2021 ◽  
Author(s):  
Yashas Samaga B L ◽  
Shampa Raghunathan ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Protein Stability ◽  
Data Augmentation ◽  
Stability Prediction ◽  
Structural Environment ◽  
Self Consistent ◽  
The Difference ◽  
Protein Sequence Space ◽  
New Machine

<div>Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that estimates a residue's contributions towards protein stability dG in its local structural environment. The difference between independently predicted contributions of the reference and mutant residues in a missense mutation is reported as dG. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, and is robust to overfitting.</div><div><br></div>

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