AN EMPIRICAL INVESTIGATION OF THE FORWARD INTEREST RATE TERM STRUCTURE

2000 ◽  
Vol 03 (04) ◽  
pp. 703-729 ◽  
Author(s):  
ANDREW MATACZ ◽  
JEAN-PHILIPPE BOUCHAUD
Keyword(s):  
Term Structure ◽  
Value At Risk ◽  
Previous Investigation ◽  
Empirical Investigation ◽  
Forward Rate ◽  
The Past ◽  
A Value ◽  
Interest Rate Term Structure ◽  
Spot Volatility ◽  
The U.S

In this paper we study empirically the Forward Rate Curve (FRC) of 5 different currencies. We confirm and extend the findings of a previous investigation of the U.S. FRC. In particular, the average FRC follows a square-root law, with a prefactor related to the spot volatility, suggesting a Value-at-Risk-like pricing. We find a striking correlation between the instantaneous FRC and the past spot trend over a certain time horizon, in agreement with the idea of an extrapolated trend effect. We present a model which can be adequately calibrated to account for these effects.

scholarly journals EXPLAINING THE FORWARD INTEREST RATE TERM STRUCTURE

2000 ◽  
Vol 03 (03) ◽  
pp. 381-389 ◽  
Author(s):  
ANDREW MATACZ ◽  
JEAN-PHILIPPE BOUCHAUD
Keyword(s):  
Term Structure ◽  
Value At Risk ◽  
Forward Rate ◽  
The Past ◽  
A Value ◽  
Interest Rate Term Structure ◽  
New Interpretation ◽  
The One ◽  
Volatility Function ◽  
Spot Volatility

We present compelling empirical evidence for a new interpretation of the Forward Rate Curve (FRC) term structure. We find that the average FRC follows a square-root law, with a prefactor related to the spot volatility, suggesting a Value-at-Risk like pricing. We find a striking correlation between the instantaneous FRC and the past spot trend over a certain time horizon. This confirms the idea of an anticipated trend mechanism proposed earlier and provides a natural explanation for the observed shape of the FRC volatility. We find that the one-factor Gaussian Heath–Jarrow–Morton model calibrated to the empirical volatility function fails to adequately describe these features.

AN ALTERNATIVE INTEREST RATE TERM STRUCTURE MODEL

2005 ◽  
Vol 08 (06) ◽  
pp. 717-735 ◽  
Author(s):  
ECKHARD PLATEN
Keyword(s):  
Interest Rate ◽  
Term Structure ◽  
Market Price ◽  
Forward Rate ◽  
Term Structure Model ◽  
Forward Rates ◽  
Consistent Price System ◽  
Interest Rate Term Structure ◽  
The Interest Rate ◽  
Total Market

This paper proposes an alternative approach to the modeling of the interest rate term structure. It suggests that the total market price for risk is an important factor that has to be modeled carefully. The growth optimal portfolio, which is characterized by this factor, is used as reference unit or benchmark for obtaining a consistent price system. Benchmarked derivative prices are taken as conditional expectations of future benchmarked prices under the real world probability measure. The inverse of the squared total market price for risk is modeled as a square root process and shown to influence the medium and long term forward rates. With constant parameters and constant short rate the model already generates a hump shaped mean for the forward rate curve and other empirical features typically observed.

Testing for Strategic Manipulation of Economic and Financial Data

Benford's Law ◽  
2015 ◽  
Author(s):  
Charles C. Moul ◽  
John V. C. Nye
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Strategic Behavior ◽  
Financial Data ◽  
Benford’S Law ◽  
Data Series ◽  
The Past ◽  
Strategic Manipulation ◽  
Benford's Law ◽  
The U.S

This chapter surveys applications of Benford's law within economics, such as its use in investigating the validity of macroeconomic data series. It argues that, given the strong interest in strategic behavior in economics, it makes sense to use Benford's law to investigate possible anomalies that suggest manipulation or other interference, especially when incentives increase for such tampering. The chapter then considers how a first-digit analysis informs Value at Risk (VAR) data from the U.S. financial sector over the past ten years. It finds that Benford's law fits precrisis data very well but is rejected for postcrisis data. Opportunities and incentives for such misreporting are then discussed.

scholarly journals METODE SIMULASI HISTORIS UNTUK PERHITUNGAN NILAI VALUE AT RISK PADA PORTOFOLIO DENGAN MODEL MARKOWITZ

2021 ◽  
Vol 9 (2) ◽  
pp. 94-102
Author(s):  
I Wayan Eka Sultra ◽  
Muhammad Rifai Katili ◽  
Muhammad Rezky Friesta Payu
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Calculation Model ◽  
Expected Returns ◽  
Historical Simulation ◽  
Markowitz Model ◽  
Time Value ◽  
The Past ◽  
A Value ◽  
Multiple Assets

A portfolio concerns the formation of the composition of multiple assets to obtain optimum results. At the same time, Value at Risk is a technique in risk management to measure and assess parametrically (variant and co-variant), Monte-Carlo, and historical simulation. This research employed historic simulation because normal distribution is not required from returns and is a Value at Risk calculation model that is determined by the past value on produced return asset, in which this research aimed to determine the Markowitz model positive shares and Value at Risk in the portfolio by using historical simulation. The Markowitz model found eight shares with positive expected returns, which are as follows: BBCA, BBRI, BRPT, EXCL, ICBP, INDF, MNCN, and TPIA. The BBCA has the most significant exposure of all the shares with the amount of Rp 2.287.200.440.000, while the TPIA has the smallest exposure of all the shares with the amount of Rp 58.899.375.000. Further, the EXCL has the largest VaR with the amount of Rp 236.189.538.497, while the TPIA and ICBP had no VaR losses because the VaR of TPIA and ICBP is Rp 0 and Rp -1.407.719.893, respectively, along with the INDF as the share with the smallest VaR of Rp 18.513.213.620. The most significant exposure average is Rp 719.246.318.375, while the largest VaR average is Rp 76.827.608.341,3. As long as the VaR did not exceed the exposure value, the investors will be safe and have no loss.

Stress Testing in a Value at Risk Framework

CFA Digest ◽  
1999 ◽  
Vol 29 (2) ◽  
pp. 76-78
Author(s):  
Thomas J. Latta
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stress Testing ◽  
A Value ◽  
Risk Framework

Novel Drugs and Biologics of 2016: A Boom for Neurodrugs in the Pipelin

2017 ◽  
Vol 10 (5) ◽  
pp. 3809-3814
Author(s):  
D Samba Reddy
Keyword(s):  
Muscular Atrophy ◽  
Clinical Care ◽  
New Drugs ◽  
Essential Medicines ◽  
High Demand ◽  
The Past ◽  
Novel Drugs ◽  
Innovative Products ◽  
Cns Drugs ◽  
The U.S

This article provides a brief overview of novel drugs approved by the U.S. FDA in 2016.  It also focuses on the emerging boom in the development of neurodrugs for central nervous system (CNS) disorders. These new drugs are innovative products that often help advance clinical care worldwide, and in 2016, twenty-two such drugs were approved by the FDA. The list includes the first new drug for disorders such as spinal muscular atrophy, Duchenne muscular dystrophy or hallucinations and delusions of Parkinson’s disease, among several others. Notably, nine of twenty-two (40%) were novel CNS drugs, indicating the industry shifting to neurodrugs. Neurodrugs are the top selling pharmaceuticals worldwide, especially in America and Europe. Therapeutic neurodrugs have proven their significance many times in the past few decades, and the CNS drug portfolio represents some of the most valuable agents in the current pipeline. Many neuroproducts are vital or essential medicines in the current therapeutic armamentarium, including dozens of “blockbuster drugs” (drugs with $1 billion sales potential).  These drugs include antidepressants, antimigraine medications, and anti-epilepsy medications. The rise in neurodrugs’ sales is predominantly due to increased diagnoses of CNS conditions. The boom for neuromedicines is evident from the recent rise in investment, production, and introduction of new CNS drugs.  There are many promising neurodrugs still in the pipeline, which are developed based on the validated “mechanism-based” strategy. Overall, disease-modifying neurodrugs that can prevent or cure serious diseases, such as multiple sclerosis, epilepsy, and Alzheimer’s disease, are in high demand. 

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